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asset-allocation\ndescription: Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.\ncategory: asset-class\n---\n\n# Asset Allocation and Portfolio Optimization\n\n## Overview\n\nFrom asset allocation theory to practical implementation, this skill covers classical frameworks (MPT, BL, risk budgeting, all-weather) and the usage of the four optimizers built into this system. The output can be written directly into `config.json`.\n\n## Asset Allocation Theory\n\n### 1. Modern Portfolio Theory (MPT, Markowitz)\n\n**Core idea**: maximize expected return for a given level of risk (the efficient frontier).\n\n```\nOptimization problem:\nmin w'Σw (portfolio variance)\ns.t. w'μ = target_return\n Σw = 1\n w ≥ 0 (no shorting)\n```\n\n| Advantages | Disadvantages |\n|------|------|\n| Mathematically rigorous | Extremely sensitive to inputs (garbage in, garbage out) |\n| Efficient frontier is visualizable | Concentrated-allocation problem (often produces extreme weights) |\n| Foundational framework | Assumes normality and ignores fat tails |\n\n**Practical advice**: do not use raw MPT directly. Add constraints (upper/lower bounds, sector limits) or use a regularized version.\n\n### 2. Black-Litterman Model\n\n**Core idea**: start from market equilibrium and incorporate investor views.\n\n```\nSteps:\n1. Reverse-imply market equilibrium returns: π = δΣw_mkt\n2. Build the view matrices: P (selection matrix), Q (view returns), Ω (view uncertainty)\n3. Blend the posterior: μ_BL = [(τΣ)^-1 + P'Ω^-1 P]^-1 [(τΣ)^-1 π + P'Ω^-1 Q]\n4. Run Markowitz optimization using posterior μ_BL\n```\n\n**Example views**:\n- Absolute view: \"China A-shares will return 10% over the next year\" → `P=[1,0,0], Q=[0.10]`\n- Relative view: \"China A-shares will outperform US equities by 5%\" → `P=[1,-1,0], Q=[0.05]`\n\n**Parameter guidance**:\n- `τ` (uncertainty scaling): `0.025-0.05`\n- `Ω`: set according to view confidence, where higher confidence = smaller variance\n\n### 3. Risk Budgeting\n\n**Core idea**: allocate by risk contribution rather than by capital share.\n\n```\nRisk contribution: RC_i = w_i × (Σw)_i / σ_p\nTarget: RC_i / σ_p = budget_i (for all i)\n```\n\n| Strategy | Risk Budget | Best Use Case |\n|------|---------|---------|\n| Equal risk contribution | Each asset 1/N | When you do not know which asset is best |\n| Equity-tilted risk budget | Stocks 60%, bonds 30%, commodities 10% | When you want equities to contribute more risk |\n| Dynamic risk budget | Adjust dynamically by signal strength | When you have market-timing ability |\n\n### 4. All-Weather Strategy\n\n**Bridgewater framework**: allocate risk equally across economic environments.\n\n```\nEconomic environment Asset allocation\n───────── ─────────\nGrowth rising Equities + commodities + corporate bonds\nGrowth falling Government bonds + inflation-protected bonds\nInflation rising Commodities + inflation-protected bonds + EM debt\nInflation falling Equities + government bonds\n\nSimplified allocation example for China-focused portfolios:\n- 30% CSI 300 / CSI 500\n- 40% government bonds / credit bonds\n- 15% gold\n- 15% commodities / REITs\n```\n\n## Guide to the 5 Optimizers\n\n### Overview of the Built-In Optimizers\n\nConfigure them in `config.json` through `optimizer` and `optimizer_params`:\n\n| optimizer | Display Name | Core Idea | Best Use Case |\n|-----------|--------|---------|---------|\n| `equal_volatility` | Equal Volatility | Allocate weights by inverse volatility | Simple and effective baseline |\n| `risk_parity` | Risk Parity | Equalize risk contribution while accounting for correlation | Long-term robust allocation |\n| `mean_variance` | Mean-Variance | Maximize Sharpe ratio or minimize variance | When return forecasts are available |\n| `max_diversification` | Maximum Diversification | Maximize the diversification ratio | When pursuing a low-correlation portfolio |\n| `turnover_aware` | Turnover-Aware | Mean-variance utility with an L1 penalty on weight changes vs the previous rebalance | When trading costs matter; tune `turnover_penalty` to your data frequency |\n\n### 1. `equal_volatility`\n\n```json\n{\n \"optimizer\": \"equal_volatility\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: `w_i = (1/σ_i) / Σ(1/σ_j)`\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Volatility calculation window (trading days) |\n\n**Advantages**: simple and fast, no return forecast required, no correlation matrix required. \n**Disadvantages**: ignores cross-asset correlation.\n\n### 2. `risk_parity`\n\n```json\n{\n \"optimizer\": \"risk_parity\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: solve for weights such that each asset contributes the same amount of risk.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Covariance-matrix estimation window |\n\n**Advantages**: accounts for correlation, spreads risk more evenly, and is robust over long horizons. \n**Disadvantages**: requires iterative solving and is sensitive to covariance estimates.\n\n### 3. `mean_variance`\n\n```json\n{\n \"optimizer\": \"mean_variance\",\n \"optimizer_params\": {\n \"lookback\": 60,\n \"risk_free\": 0.0\n }\n}\n```\n\n**Principle**: Markowitz optimization that maximizes the Sharpe ratio.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Window for estimating means and covariances |\n| risk_free | 0.0 | Risk-free rate (annualized) |\n\n**Advantages**: theoretically optimal (if inputs are accurate). \n**Disadvantages**: extremely sensitive to inputs, prone to extreme weights, and often performs poorly out of sample. \n**Recommendation**: do not make `lookback` too short (`<30` easily overfits), and add upper/lower weight constraints.\n\n### 4. `max_diversification`\n\n```json\n{\n \"optimizer\": \"max_diversification\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: maximize `DR = (w'σ) / σ_p` (the diversification ratio).\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Calculation window |\n\n**Advantages**: does not require return forecasts and seeks true diversification. \n**Disadvantages**: effectiveness is limited in highly correlated environments.\n\n### 5. `turnover_aware`\n\n```json\n{\n \"optimizer\": \"turnover_aware\",\n \"optimizer_params\": {\n \"lookback\": 60,\n \"risk_aversion\": 1.0,\n \"turnover_penalty\": 0.5\n }\n}\n```\n\n**Principle**: minimize `-w'μ + λ·w'Σw + γ·||w - w_prev||₁` subject to long-only, fully-invested weights — mean-variance utility with an L1 penalty on weight changes versus the previous rebalance, so the optimizer only trades when the expected improvement outweighs the (implicit) cost.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Calculation window |\n| risk_aversion | 1.0 | Weight on the variance term (λ) |\n| turnover_penalty | 0.0 | Weight on the L1 turnover term (γ); 0 reduces to the mean-variance baseline |\n\n**Advantages**: dampens rebalancing churn, which usually dominates realized costs; the first rebalance is unpenalized so the cold start is undistorted. \n**Disadvantages**: `turnover_penalty` is scale-sensitive to the return frequency of the input window — for daily returns even γ ≈ 0.5 strongly prefers holding still, so tune it per data frequency.\n\n### Optimizer Selection Decision Tree\n\n```\nDo you have return forecasts?\n├── Yes → Do trading costs / churn matter?\n│ ├── Yes → turnover_aware (tune turnover_penalty to data frequency)\n│ └── No → mean_variance (remember to add constraints)\n└── No → Do you need to account for correlation?\n ├── Yes → risk_parity (recommended default)\n └── No → Are volatility differences across assets large?\n ├── Yes → equal_volatility\n └── No → max_diversification\n```\n\n## Rebalancing Strategy\n\n### Three Rebalancing Triggers\n\n| Method | Trigger Condition | Advantages | Disadvantages |\n|------|---------|------|------|\n| Periodic rebalancing | Fixed monthly / quarterly date | Simple, predictable trading cost | May miss or delay adjustments |\n| Threshold trigger | Deviation from target weight > X% | Trades only when needed | Frequent trading in high-volatility markets |\n| Volatility trigger | VIX / volatility breaks a threshold | Adapts to market regime | Parameter selection is difficult |\n\n### Suggested Rebalancing Frequency\n\n| Asset Class | Suggested Frequency | Threshold |\n|---------|---------|------|\n| Equity portfolio | Monthly | ±5% |\n| Stock-bond mix | Quarterly | ±10% |\n| Global macro | Quarterly / semiannual | ±10% |\n| Cryptocurrency | Weekly / biweekly | ±15% (high volatility) |\n\n### Rebalancing in Backtests\n\nImplement rebalancing logic in `signal_engine.py`:\n\n```python\n# Periodic rebalancing example (every 20 trading days)\nif bar_count % rebalance_freq == 0:\n # Recompute weights\n new_weights = calculate_target_weights(data_map)\n for code, weight in new_weights.items():\n signals[code].iloc[i] = weight\n```\n\n## Cross-Asset Correlation Analysis\n\n### Typical Correlation Matrix (China-Focused Portfolio Example)\n\n| | CSI 300 | CSI 500 | Government Bonds | Gold | BTC |\n|--|--------|--------|------|------|-----|\n| CSI 300 | 1.00 | 0.85 | -0.15 | 0.05 | 0.10 |\n| CSI 500 | 0.85 | 1.00 | -0.10 | 0.03 | 0.12 |\n| Government Bonds | -0.15 | -0.10 | 1.00 | 0.20 | -0.05 |\n| Gold | 0.05 | 0.03 | 0.20 | 1.00 | 0.15 |\n| BTC | 0.10 | 0.12 | -0.05 | 0.15 | 1.00 |\n\n**Key patterns**:\n- Negative stock-bond correlation is the foundation of allocation (but it does not always hold; in 2022 both stocks and bonds sold off)\n- Gold has low correlation with equities and serves as a hedge\n- BTC's correlation with traditional assets is unstable and tends to become positive in crises\n- Large-cap versus small-cap China A-shares have high correlation (`0.85`), so diversification benefits are limited\n\n## Output Format\n\n```markdown\n## Asset Allocation Recommendation\n\n### Allocation Plan\n| Asset | Weight | Risk Contribution | Expected Return (Annualized) |\n|------|------|---------|--------------|\n| CSI 300 | 30% | 45% | 8% |\n| Government Bond ETF | 40% | 15% | 3% |\n| Gold | 15% | 20% | 5% |\n| BTC | 15% | 20% | 15% |\n\n### Optimizer Configuration\n```json\n{\n \"optimizer\": \"risk_parity\",\n \"optimizer_params\": {\"lookback\": 60}\n}\n```\n\n### Expected Risk / Return\n| Metric | Value |\n|------|-----|\n| Expected annualized return | 7.2% |\n| Expected annualized volatility | 8.5% |\n| Expected Sharpe | 0.85 |\n| Expected maximum drawdown | -12% |\n\n### Rebalancing Rules\n- Frequency: quarterly (first trading day of March / June / September / December)\n- Threshold: trigger when any asset deviates from target by ±10%\n- Cost: estimated annual trading cost 0.15%\n```\n\n## Notes\n\n1. **The optimizer needs enough instruments**: at least 3 instruments are needed for meaningful optimization; with 2 instruments, `equal_volatility` is usually enough\n2. **`lookback` window**: too short (`<20`) is noisy, too long (`>120`) reacts slowly, and 60 is a reasonable default\n3. **`mean_variance` trap**: it is the easiest to overfit, and out-of-sample Sharpe is often cut by half or more\n4. **Rebalancing cost**: frequent rebalancing eats into returns; for China A-share portfolios, stamp duty of 0.05% plus commissions is material\n5. **Cross-market allocation**: use `\"source\": \"auto\"` in `config.json`, and let `codes` mix instruments from different markets\n6. **Leverage constraint**: the sum of weights must be ≤ 1.0, and leverage is not allowed unless explicitly specified\n7. **Survivorship bias**: historical correlations may be distorted by delistings and new listings\n","agent/src/skills/akshare/SKILL.md":"---\nname: akshare\ncategory: data-source\ndescription: AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance.\n---\n\n## Overview\n\nAKShare is a completely free, open-source Python financial data library. No registration or API key required. It aggregates data from public sources (Sina, East Money, etc.) covering Chinese and global markets.\n\n- GitHub: https://github.com/akfamily/akshare (18k+ stars)\n- Install: `pip install akshare`\n\n## Quick Start\n\n```python\nimport akshare as ak\n\n# A-share daily OHLCV (前复权)\ndf = ak.stock_zh_a_hist(symbol=\"000001\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n\n# US stock daily\ndf = ak.stock_us_hist(symbol=\"105.AAPL\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n\n# HK stock daily\ndf = ak.stock_hk_hist(symbol=\"00700\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n```\n\n## Top 10 High-Frequency Interfaces\n\n### A-shares\n\n| Function | Description | Key Params |\n|----------|-------------|------------|\n| `stock_zh_a_hist()` | A-share OHLCV | symbol, period, start_date, end_date, adjust |\n| `stock_zh_a_spot_em()` | Real-time A-share quotes | (none) |\n| `stock_individual_info_em()` | Stock basic info | symbol |\n| `stock_zh_a_hist_min_em()` | Intraday bars | symbol, period(1/5/15/30/60) |\n\n### US / HK\n\n| Function | Description | Key Params |\n|----------|-------------|------------|\n| `stock_us_hist()` | US stock OHLCV | symbol (e.g. \"105.AAPL\"), period, start_date, end_date |\n| `stock_hk_hist()` | HK stock OHLCV | symbol (e.g. \"00700\"), period, start_date, end_date |\n\n### Macro / Forex / Futures\n\n| Function | Description |\n|----------|-------------|\n| `macro_china_gdp()` | China GDP data |\n| `macro_china_cpi()` | China CPI data |\n| `futures_main_sina()` | Futures main contract quotes |\n| `currency_boc_sina()` | BOC forex rates |\n\n## Column Names\n\nAKShare returns Chinese column names by default:\n\n| Chinese | English | Description |\n|---------|---------|-------------|\n| 日期 | date | Trade date |\n| 开盘 | open | Open price |\n| 最高 | high | High price |\n| 最低 | low | Low price |\n| 收盘 | close | Close price |\n| 成交量 | volume | Volume |\n| 成交额 | amount | Turnover |\n| 涨跌幅 | pct_change | % change |\n| 换手率 | turnover_rate | Turnover rate |\n\n## Date Format\n\n- Input: `YYYYMMDD` string (e.g. `\"20240101\"`)\n- Output: `日期` column as string, convert with `pd.to_datetime()`\n\n## Symbol Format\n\n- A-shares: pure digits `\"000001\"` (no .SZ suffix)\n- US stocks: `\"105.AAPL\"` (NASDAQ prefix 105), `\"106.BABA\"` (NYSE prefix 106)\n- HK stocks: `\"00700\"` (5-digit zero-padded)\n\n## Built-in Loader\n\nThe project has a built-in AKShare DataLoader at `backtest/loaders/akshare_loader.py`. When backtesting, the runner automatically falls back to AKShare when tushare/yfinance are unavailable.\n\n## Reference Docs\n\nFor less common interfaces, see the `references/` subdirectory or the official docs at https://akshare.akfamily.xyz/\n","agent/src/skills/alpha-zoo/SKILL.md":"---\nname: alpha-zoo\ndescription: Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks \"which alphas exist\", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.\ncategory: research\n---\n# Alpha Zoo\n\n## Purpose\n\nWhen the user asks about prebuilt cross-sectional alphas — Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart — or wants to bench a whole zoo on an investable universe (CSI 300, S&P 500, BTC-USDT, ...), this skill orients you. The zoo is the curated library; the bench is the evaluator.\n\n## Tools Available\n\n| Tool | When to use |\n|------|------|\n| `alpha_zoo` | Browse the library. `action=list_alphas` to enumerate (filterable by zoo / theme / universe), `action=get_alpha` for one alpha's metadata, `action=health` for registry load status. |\n| `alpha_bench` | Run IC / IR on one alpha or a whole zoo over a universe + period. Emits an HTML report. |\n| `factor_analysis` | Ad-hoc factor evaluation from a user-supplied factor CSV + return CSV. Use this when the user has their own factor (not in the zoo). |\n\n## Decision Tree\n\n- \"list all momentum alphas\" → `alpha_zoo` with `action=list_alphas, theme=momentum`.\n- \"show me gtja191_alpha_001\" → `alpha_zoo` with `action=get_alpha, alpha_id=gtja191_alpha_001`.\n- \"bench all of GTJA 191 on CSI 300 from 2020 to 2024\" → `alpha_bench` with `zoo=gtja191, universe=csi300, period=2020-2024`.\n- \"is the registry healthy\" → `alpha_zoo` with `action=health` — surfaces `loaded`, `failed`, and per-error reasons.\n- User uploads `my_factor.csv` → `factor_analysis` (zoo tools are for prebuilt alphas only).\n\n## Zoo Inventory\n\n| Zoo | Description | Approx. count |\n|------|------|------|\n| `kakushadze101` | Formulaic alphas from Kakushadze's 2015 paper. Mix of momentum, reversal, volume, and microstructure. | ~101 |\n| `gtja191` | Guotai Junan 191 alphas — A-share focused cross-sectional factors. | ~191 |\n| `qlib158` | Microsoft Qlib's 158 alpha factors — features tuned for ML pipelines. | ~158 |\n| `classical` | Fama-French 3/5-factor + Carhart momentum. | <10 |\n\nCounts are nominal; check `alpha_zoo action=health` for the live count currently loaded.\n\n## Constraints\n\n- **No per-stock per-date factor values are surfaced to the agent.** IC results are aggregate stats (mean / std / IR / positive-ratio); the HTML report shows top-N by IR plus formulas, never the underlying panel.\n- **Lookahead is banned in the operator set.** `delta(df, d)` requires `d >= 1`; the negative-shift `Ref(df, -n)` form does not exist. See `docs/alpha-zoo/spec.md` for the full operator catalogue.\n- **Universe loaders may not be wired for every market yet.** When `alpha_bench` returns `universe loader for X not yet implemented`, that's the W2 scaffold — the universe is recognised but the data pull lands in W4.\n- **Do not expose absolute filesystem paths in agent output.** The bench tool writes to `~/.vibe-trading/reports/` by default; refer to it by that shorthand, not by the resolved absolute path.\n- **`alpha_zoo` is read-only.** `alpha_bench` writes a single HTML file per run — no scratch state elsewhere.\n\n## Common Pitfalls\n\n- Filter mismatch on `list_alphas`: theme / universe must match the alpha's declared metadata exactly (e.g. `equity_cn`, not `cn` or `china`).\n- Calling `alpha_bench` with both `alpha_id` and `zoo` set — they are mutually exclusive; pick one.\n- Empty registry (`loaded=0`) means no zoo modules are populated yet; treat it as \"zoos pending W3 porting\" rather than a bug.\n\n## Reference\n\n- Operator catalogue: `docs/alpha-zoo/spec.md`\n- Registry contract: `src/factors/registry.py` (frozen; do not modify)\n- IC / layered NAV math: `src/factors/factor_analysis_core.py`\n","agent/src/skills/backtest-diagnose/SKILL.md":"---\nname: backtest-diagnose\ndescription: Diagnose failed or underperforming backtests, locate the root cause, and fix the issue\ncategory: tool\n---\n\n# Backtest Diagnosis\n\n## Overview\n\nUse this skill when a user reports that a backtest failed, raised an error, or produced poor results.\n\n## Diagnostic Workflow\n\n1. **Read existing artifacts**: use `read_file` to inspect `artifacts/metrics.csv`, `equity.csv`, and `trades.csv`\n2. **Read the code**: use `read_file` to inspect `code/signal_engine.py` and `config.json`\n3. **Classify the issue**: determine the root cause using the error taxonomy below\n4. **Apply the fix**: use `edit_file` to modify the code, then rerun the backtest\n5. **Verify the fix**: use `read_file` to inspect the new `metrics.csv`\n\n## Error Taxonomy\n\n### Runtime Errors (`exit_code != 0`)\n\n| Error Type | Common Cause | Fix |\n|---------|---------|---------|\n| ImportError | Missing dependency | `bash(\"pip install xxx\")` |\n| KeyError | DataFrame column-name mismatch | Check the actual column names in `data_map` |\n| IndexError | Empty data or insufficient length | Add length checks |\n| TypeError | Incorrect signal type | Ensure the return value is `pd.Series` |\n\n### Logic Bugs (Backtest Succeeds but Results Are Abnormal)\n\n1. **Zero trades** (`trade_count=0`): signal-logic bug. Conditions are too strict, so the signal stays at 0. Check whether entry and exit logic is reasonable, and inspect the signal series to confirm it is not all zeros.\n2. **Late trades** (first trade occurs more than 2 years after the backtest start): data-filtering bug. The lookback window may be too long, or the initial data segment may have been dropped. Shorten the window or check whether `dropna` is too aggressive.\n3. **Capital utilization < 50%** (mostly in cash): position-sizing bug. Signal triggers may be too sparse, or the position-sizing logic may be wrong.\n4. **Open position at the end** (a position still exists when the backtest ends): exit-timing bug. Forced liquidation may be missing, or exit logic does not cover the final segment.\n\n### Data Errors\n\n| Symptom | Root Cause | Fix |\n|------|------|---------|\n| No data fetched | Invalid API token or code issue | Check `config.json` |\n| Too little data | Date range too narrow | Expand the date range |\n\n### Data-Source Error Ignore List\n\nIf you encounter the following keywords, **do not modify the code**. The problem is on the data-provider side:\n- a provider-side \"no data available\" response\n- `rate limit`\n- `API limit`\n- `daily limit`\n- `Information` (common in Tushare API responses)\n\nThese issues require the user to check the API token, switch data sources, or wait for the quota to reset.\n\n## Hard-Gate Checklist\n\n1. `artifacts/metrics.csv` exists and is non-empty\n2. `artifacts/equity.csv` exists and is non-empty\n3. `trade_count > 0` (`0` trades means a signal bug)\n4. The equity series contains no `NaN`\n5. `exit_code == 0`\n\n## Evidence hookup\n\nThis Hard-Gate Checklist is also the evidence ingestion gate for Strategy Discovery: a run failing any gate produces **no evidence rows** — never partial rows — and is skipped with a stable machine-readable token (`hard-gate:exit-nonzero`, `hard-gate:metrics-missing`, `hard-gate:zero-trades`, `hard-gate:equity-empty`, `hard-gate:equity-nan`). Diagnose and fix the failing gate as usual, rerun the backtest, then repopulate the evidence cache with `refresh_strategy_evidence` (agent tool / MCP tool, or `vibe-trading strategy-evidence refresh --manifest `) so the fixed run becomes queryable evidence. See the `strategy-discovery` skill for the manifest format and the full gate list.\n\n## Fixing Principles\n\n- Use **edit_file** to make precise code fixes instead of rewriting the entire file with `write_file`, unless the structure is fundamentally broken\n- Fix the bug only, do not change strategy logic unless the user explicitly asks\n- Fix one issue at a time, and rerun the backtest immediately after each fix\n- Limit yourself to at most 3 repair iterations\n\n## Post-Fix Validation Rules\n\nAfter modifying `signal_engine.py`, you must confirm:\n1. **AST syntax passes**: `bash(\"python -c \\\"import ast; ast.parse(open('code/signal_engine.py').read()); print('OK')\\\"\")`\n2. **Contains `class SignalEngine`**: the file must define `class SignalEngine`\n3. **Contains `def generate`**: the class must contain a `def generate` method\n4. **Rerun the backtest**: after the fix, rerun the backtest and verify the results\n\n## `action_items` Writing Rules\n\nAfter diagnosis, output actionable improvement suggestions:\n- Format: `\"Change X from A to B\"` or `\"Add X logic in signal_engine.py\"`\n- Be specific about parameter values, filenames, and function names\n- Provide at least 2 items\n- Examples:\n - `\"Change RSI threshold from 30 to 25 in signal_engine.py line 42\"`\n - `\"Add signals = signals.fillna(0) after signal calculation to prevent NaN propagation\"`\n - `\"Add a volume filter: skip buy signals when volume is below the 20-day average\"`\n","agent/src/skills/adr-hshare/SKILL.md":"---\nname: adr-hshare\ndescription: ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.\ncategory: flow\n---\n# ADR / H-Share / A-Share Cross-Listing Analysis\n\n## Overview\n\nMany Chinese companies are listed on multiple exchanges — A-shares in Shanghai/Shenzhen, H-shares in Hong Kong, and ADRs in the US. Pricing gaps between these listings create arbitrage opportunities and reveal market-specific sentiment differences. This skill provides frameworks for analyzing cross-listing premiums, identifying arbitrage signals, and assessing delisting risk for US-listed Chinese ADRs.\n\n## Core Concepts\n\n### 1. Cross-Listing Structures\n\n| Structure | Description | Examples |\n|-----------|-------------|---------|\n| A + H dual-listed | Same company listed on both A-share and HK exchange | PetroChina (601857.SH / 0857.HK), ICBC (601398.SH / 1398.HK) |\n| H + ADR dual-listed | HK-listed with US ADR | Alibaba (9988.HK / BABA), JD.com (9618.HK / JD) |\n| A + H + ADR triple-listed | All three markets | China Life (601628.SH / 2628.HK / LFC) |\n| HK primary + US secondary | Primary listing in HK, secondary ADR | Tencent (0700.HK / TCEHY OTC) |\n| US primary → HK secondary | Originally US, added HK listing | Alibaba (BABA → 9988.HK), Baidu (BIDU → 9888.HK) |\n\n### 2. AH Premium Analysis\n\n**AH Premium = (A-share price / H-share price in CNY terms - 1) × 100%**\n\n```python\ndef calculate_ah_premium(a_price_cny, h_price_hkd, usdcny, usdhkd):\n \"\"\"Calculate AH premium for a dual-listed stock.\"\"\"\n h_price_cny = h_price_hkd * (usdcny / usdhkd) # Convert HKD to CNY\n ah_premium = (a_price_cny / h_price_cny - 1) * 100\n return ah_premium\n\n# Example: PetroChina\n# A-share: 8.50 CNY, H-share: 6.20 HKD\n# USDCNY: 7.25, USDHKD: 7.82\n# H in CNY: 6.20 * (7.25/7.82) = 5.75 CNY\n# AH Premium: (8.50/5.75 - 1) * 100 = 47.8%\n```\n\n**AH Premium signal interpretation:**\n\n| Premium Level | Interpretation | Action |\n|--------------|----------------|--------|\n| >50% | Extreme A-share premium; A-share speculative bubble or H-share extreme undervaluation | Strong: buy H, sell/avoid A |\n| 30-50% | Elevated premium; normal for high-retail-participation names | Moderate: favor H if fundamentals same |\n| 10-30% | Normal range for most AH pairs | Neutral; no strong arbitrage signal |\n| 0-10% | Compressed premium; A-shares relatively cheap | Unusual; investigate catalyst |\n| <0% | H-share premium over A-share | Very rare; usually near-term event-driven |\n\n**Structural drivers of AH premium:**\n1. **Liquidity premium**: A-shares have much higher retail participation and turnover → liquidity premium\n2. **Access premium**: A-shares were historically hard for foreigners to access → scarcity premium\n3. **Currency expectations**: CNY depreciation expectations widen the premium\n4. **Regulatory arbitrage**: different trading rules (T+1 in A-shares vs T+0 in HK)\n5. **Investor composition**: A-share retail speculative premium vs HK institutional valuation discipline\n\n### 3. ADR Premium/Discount Analysis\n\n**ADR premium = (ADR price in USD / HK equivalent in USD - 1) × 100%**\n\n```python\ndef calculate_adr_premium(adr_price_usd, hk_price_hkd, adr_ratio, usdhkd):\n \"\"\"\n Calculate ADR premium over HK listing.\n adr_ratio: number of HK shares per 1 ADR (e.g., BABA: 1 ADR = 8 HK shares)\n \"\"\"\n hk_equivalent_usd = (hk_price_hkd * adr_ratio) / usdhkd\n premium = (adr_price_usd / hk_equivalent_usd - 1) * 100\n return premium\n\n# Example: Alibaba\n# BABA ADR: $85.00, 9988.HK: HKD 82.50\n# ADR ratio: 1 ADR = 8 HK shares\n# HK equivalent: (82.50 * 8) / 7.82 = $84.40\n# ADR premium: (85.00/84.40 - 1) * 100 = 0.71%\n```\n\n**Key ADR conversion ratios:**\n\n| Company | ADR Ticker | HK Ticker | ADR Ratio (HK:ADR) | ADR Exchange |\n|---------|-----------|-----------|---------------------|--------------|\n| Alibaba | BABA | 9988.HK | 8:1 | NYSE |\n| JD.com | JD | 9618.HK | 2:1 | NASDAQ |\n| Baidu | BIDU | 9888.HK | 8:1 | NASDAQ |\n| Bilibili | BILI | 9626.HK | 1:1 | NASDAQ |\n| NIO | NIO | 9866.HK | 1:1 | NYSE |\n| XPeng | XPEV | 9868.HK | 2:1 | NYSE |\n| Li Auto | LI | 2015.HK | 2:1 | NASDAQ |\n| NetEase | NTES | 9999.HK | 5:1 | NASDAQ |\n| Trip.com | TCOM | 9961.HK | 1:1 | NASDAQ |\n| Pinduoduo | PDD | N/A (US-only) | N/A | NASDAQ |\n\n**ADR premium drivers:**\n- US trading hours sentiment (earnings releases, macro data during US hours)\n- US-specific regulatory events (SEC, PCAOB audits)\n- Liquidity premium (ADR often more liquid for global funds)\n- Time zone gap: ADR closes at HK's open → overnight gap creates premium/discount\n\n### 4. Delisting Risk Assessment\n\n**HFCAA (Holding Foreign Companies Accountable Act) framework:**\n\nSince 2022, PCAOB gained access to audit workpapers of Chinese companies. Key risks:\n\n| Risk Level | Criteria | Impact |\n|------------|----------|--------|\n| Low | PCAOB inspection completed, no issues | ADR status stable |\n| Medium | PCAOB inspection completed, deficiencies noted | Monitor for resolution |\n| High | PCAOB access revoked or restricted | 3-year delisting countdown activated |\n| Critical | On SEC \"identified issuer\" list for 3 consecutive years | Forced delisting |\n\n**Delisting risk indicators:**\n```python\ndelisting_risk_factors = {\n \"pcaob_status\": \"inspected\", # inspected / pending / blocked\n \"sec_identified_years\": 0, # 0, 1, 2, or 3 (3 = delist)\n \"has_hk_listing\": True, # Backup listing reduces impact\n \"hk_listing_type\": \"primary\", # primary (can be in Connect) vs secondary\n \"vie_structure\": True, # Variable Interest Entity adds legal risk\n \"state_owned\": False, # SOE status adds geopolitical risk\n}\n\n# Companies with HK primary listing (BABA, JD, BIDU, NTES, etc.) have\n# a safety net if US delisting occurs → fungible conversion ADR → HK shares\n# Companies with US-only listing (PDD until HK listing) face higher risk\n```\n\n### 5. Cross-Listing Arbitrage Strategies\n\n**Strategy 1: AH Premium Mean-Reversion**\n```python\n# When AH premium for a specific stock diverges significantly from its historical average\nah_premium_current = 45 # current premium\nah_premium_mean_12m = 35 # 12-month average\nah_premium_std = 8 # standard deviation\n\nz_score = (ah_premium_current - ah_premium_mean_12m) / ah_premium_std\n\nif z_score > 2.0:\n signal = \"fade_premium\" # A-share overvalued vs H; buy H, avoid A\nelif z_score < -2.0:\n signal = \"buy_premium\" # A-share undervalued vs H; buy A, avoid H\nelse:\n signal = \"neutral\"\n```\n\n**Strategy 2: ADR-HK Intraday Arbitrage**\n- During overlapping trading hours (HK morning = US pre-market via ADR), price gaps can appear\n- Professional arbitrageurs use ADR↔HK fungible conversion to capture these gaps\n- For research purposes: tracking ADR premium trend indicates which market is leading sentiment\n\n**Strategy 3: Event-Driven Cross-Listing**\n- New HK listing announcement (US→HK): ADR typically dips 2-5% on dilution fear, then recovers\n- MSCI / FTSE index inclusion of HK listing: triggers passive fund buying in HK\n- Stock Connect inclusion (HK primary listing eligible): triggers mainland institutional buying\n\n## Data Access\n\n```python\nimport yfinance as yf\n\n# Fetch ADR and HK prices simultaneously\nbaba_adr = yf.download(\"BABA\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\nbaba_hk = yf.download(\"9988.HK\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\n\n# For A+H pairs\npetrochina_a = yf.download(\"601857.SS\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\npetrochina_h = yf.download(\"0857.HK\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\n\n# FX rates for premium calculation\ncny = yf.download(\"CNY=X\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False) # USD/CNY\nhkd = yf.download(\"HKD=X\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False) # USD/HKD\n\n# AH Premium Index (HSAHP)\n# Not directly on yfinance; use Hang Seng website or Tushare\n```\n\n## Output Format\n\n```\n## Cross-Listing Analysis — [Company Name]\n\n### Listing Structure\n- **A-share**: [code] @ [price CNY]\n- **H-share**: [code] @ [price HKD]\n- **ADR**: [ticker] @ [price USD] (ratio: X HK shares = 1 ADR)\n\n### Premium/Discount\n- **AH Premium**: [X%] (12m avg: X%, z-score: X.X)\n- **ADR-HK Premium**: [X%] (5d avg: X%)\n- **Direction**: [AH premium widening / narrowing / stable]\n\n### Valuation Comparison\n| Metric | A-share | H-share | ADR |\n|--------|---------|---------|-----|\n| PE (TTM) | XX.X | XX.X | XX.X |\n| PB | X.X | X.X | X.X |\n| Dividend yield | X.X% | X.X% | X.X% |\n\n### Delisting Risk (ADR)\n- **PCAOB status**: [inspected / pending]\n- **SEC identified years**: [0/1/2/3]\n- **HK backup**: [yes-primary / yes-secondary / no]\n- **Risk level**: [low / medium / high / critical]\n\n### Arbitrage Signal\n- **AH premium z-score**: [X.X] → [fade premium / neutral / buy premium]\n- **Best market to buy**: [A / H / ADR] — rationale\n- **Catalyst**: [index inclusion, Connect eligibility, earnings]\n\n### Investment Implication\n- **Preferred listing**: [H-share / ADR / A-share] for new position\n- **Risk**: [delisting, FX, regulatory, liquidity]\n```\n\n## Notes\n\n- AH premium arbitrage is not freely executable: A-shares and H-shares are NOT fungible (no direct conversion), so true arbitrage requires separate capital pools\n- ADR↔HK conversion IS possible for most dual-listed names (via depositary bank), but takes 2-3 business days and involves fees\n- Currency risk (CNY, HKD, USD) is a major driver of cross-listing premiums; always hedge or account for FX when comparing\n- VIE (Variable Interest Entity) structure adds a layer of legal risk for many Chinese ADRs; this is a structural risk, not a trading signal\n- Stock Connect eligibility requirements mean not all HK-listed Chinese companies are accessible to mainland investors\n- This framework is for research purposes only and does not constitute investment advice\n","agent/SKILL.md":"---\nname: vibe-trading\nversion: 0.1.18\ndescription: Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).\ndependencies:\n python: \">=3.11\"\n pip:\n - vibe-trading-ai\nenv:\n - name: TUSHARE_TOKEN\n description: \"Tushare API token for China A-share data (optional — HK/US/Canada/crypto work without any key)\"\n required: false\n - name: OPENAI_API_KEY\n description: \"OpenAI-compatible API key — only needed for run_swarm (multi-agent teams). All other tools work without it.\"\n required: false\n - name: LANGCHAIN_MODEL_NAME\n description: \"LLM model name for run_swarm (e.g. deepseek/deepseek-v4-pro). Only needed if using run_swarm.\"\n required: false\nmcp:\n command: vibe-trading-mcp\n args: []\n---\n\n# Vibe-Trading\n\nProfessional finance research toolkit with AI-powered backtesting (11 engines), multi-agent teams, 90 specialized skills, the **Alpha Zoo** (478 pre-built quantitative alphas across qlib158 / alpha101 / gtja191 / academic / fundamental with one-line CLI benchmarking), and the Shadow Account loop — extract your implicit trading rules from a journal, backtest them across A股/港股/美股/crypto, then see where they would have served you better.\n\n## Setup\n\n```bash\npip install vibe-trading-ai\n```\n\n> **Package name vs commands:** The PyPI package is `vibe-trading-ai`. Once installed, you get:\n>\n> | Command | Purpose |\n> |---------|---------|\n> | `vibe-trading` | Interactive CLI / TUI |\n> | `vibe-trading serve` | Launch FastAPI web server |\n> | `vibe-trading-mcp` | Start MCP server (for Claude Desktop, OpenClaw, Cursor, etc.) |\n\nAdd to your agent's MCP config:\n\n```json\n{\n \"mcpServers\": {\n \"vibe-trading\": {\n \"command\": \"vibe-trading-mcp\"\n }\n }\n}\n```\n\n### API Key Requirements\n\nCore research MCP tools work with zero API keys for HK/US/Canada/crypto. After `pip install`, backtesting, market data, factor analysis, options pricing, chart patterns, web search, document reading, trade journal analysis, shadow-account extraction/backtest/report, the Alpha Zoo (478 pre-built alphas), and all 90 skills are ready to use. IBKR tools require a local TWS / IB Gateway session; `run_swarm` requires an LLM key.\n\n| Feature | Key needed | When |\n|---------|-----------|------|\n| HK/US/Canada equities & crypto | None | Always free (yfinance / stooq / yahoo + OKX) |\n| China A-share data | None | Free via akshare / baostock / tencent / sina / eastmoney fallback (`TUSHARE_TOKEN` optional for premium quality) |\n| Premium US fundamentals/quotes | `FINNHUB_API_KEY` / `ALPHAVANTAGE_API_KEY` / `TIINGO_API_KEY` / `FMP_API_KEY` | Only for optional-key providers (graceful fallback to free sources) |\n| Multi-agent swarm (`run_swarm`) | `OPENAI_API_KEY` + `LANGCHAIN_MODEL_NAME` | Swarm spawns internal LLM workers |\n\n## What You Can Do\n\n### Shadow Account — flagship loop\n\nFeed a CSV broker export (同花顺 / 东财 / 富途 / generic), and the agent will:\n1. `analyze_trade_journal` — profile your behavior (holding period, win rate, disposition effect, chasing, overtrading, anchoring).\n2. `extract_shadow_strategy` — distill 3-5 if-then rules that describe your profitable roundtrips.\n3. `run_shadow_backtest` — backtest those rules across A/HK/US/crypto and compute delta-PnL vs your realized trades.\n4. `render_shadow_report` — produce an HTML/PDF report (8 sections + charts) with today's matching signals.\n5. `scan_shadow_signals` — list today's symbols that match your shadow's entry cadence (research only).\n\n### Backtesting\nCreate and run quantitative strategies across 11 engines (ChinaA, GlobalEquity, IndiaEquity, KoreaEquity, VietnamEquity, BrazilEquity, Crypto, ChinaFutures, GlobalFutures, Forex, cross-market Composite) plus an options portfolio engine, with 29 market-data sources (auto-detect + ordered fallback; the hosted forex `tickerall` source is explicit-only):\n- **HK/US equities** via yfinance / stooq / yahoo (free, no API key); optionally via **Longbridge** historical OHLCV (`longbridge`, requires the optional SDK and `LONGBRIDGE_APP_KEY` / `LONGBRIDGE_APP_SECRET` / `LONGBRIDGE_ACCESS_TOKEN`). To force it for a run, set `\"source\": \"longbridge\"` in `config.json`.\n- **Canada equities (TSX/TSXV)** via yahoo / yfinance using Yahoo's canonical `.TO` (TSX, e.g. `TD.TO`) or `.V` (TSXV, e.g. `PNG.V`) suffixes — free, no API key. The GlobalEquity engine uses CAD identity, whole-share orders, configurable Canadian commission/slippage, and the TSX/TSXV price-increment grid.\n- **India equities (NSE/BSE)** via yahoo / yfinance using `.NS` (NSE, e.g. `RELIANCE.NS`) or `.BO` (BSE, e.g. `500325.BO`) — free, no API key. The `IndiaEquityEngine` models T+1 delivery, no overnight shorts (set `allow_short` for intraday), configurable circuit bands, 1-share lots, and the STT/stamp-duty/exchange/GST cost stack. Optionally back-fill from your live broker via the `india_broker` source (Shoonya/Dhan; requires broker login).\n- **Korea equities (KRX: KOSPI/KOSDAQ)** via pykrx using `.KS` (KOSPI, e.g. `005930.KS`) or `.KQ` (KOSDAQ, e.g. `247540.KQ`) — free, no API key (`pip install \"vibe-trading-ai[krx]\"`; yahoo/yfinance fallback needs no extra). pykrx serves **daily bars only** (an intraday request falls through to another source) and its adjusted series is Naver-backed rather than a verbatim KRX print. The `KoreaEquityEngine` models same-day round trips (no T+1), the ±30% daily price limit measured from the previous close and quantized to the KRX tick grid, tick-rounded fills, the 0.20% sell-side transaction tax (2026 rate), and 1-share lots. It is **long-only**: `allow_short` is refused, because KRX covered-short and uptick rules cannot be enforced on daily bars.\n- **Vietnam equities (HOSE)** via yahoo / yfinance using `.VN` (e.g. `VIC.VN`) — no API key. Yahoo officially lists HOSE but not HNX or UPCOM; those venues are unsupported and need the `local` source. yfinance is an unofficial Yahoo client, so availability is best-effort and subject to Yahoo's personal-use terms. The `VietnamEquityEngine` approximates the formal T+2 settlement cycle — shares bought on day T normally become sellable during the afternoon session on T+2, informally called T+1.5 — as a two-bar hold on daily data (`vn_settlement_bars` covers scenario testing and future rule changes). It applies HOSE's normal ±7% band around the reference price, rounding the ceiling down and the floor up to the 10/50/100-VND tick grid, and uses 100-share round lots; odd-lot trading is not modelled. Costs are configurable brokerage plus, for individual investors, 0.1% sell-side personal income tax on gross proceeds. It is **long-only**, because operational cash-equity short selling is not generally available in Vietnam.\n- **Cryptocurrency** via OKX or CCXT/100+ exchanges (free, no API key)\n- **China A-shares** via AKShare / baostock / tencent / sina / eastmoney (free, no API key) — `TUSHARE_TOKEN` optional for premium quality\n- **Futures, forex, macro** via AKShare (free, no API key)\n- **Forex / metals with no local terminal** via the hosted **TickerAll** MetaTrader 5 feed (`source=\"tickerall\"`, `TICKERALL_API_KEY` + `TICKERALL_ACCOUNT_ID`, read-only) — the same broker feed as the `mt5` loader but over a hosted API on any OS. **Explicit-only** (never an automatic fallback).\n- **HK & A-share equities** via Futu (broker login required, optional)\n- **Local CSV/parquet bars** via the `local` loader (offline, no network)\n- **Premium cross-market data** via QVeris (optional API key)\n- **Premium US data** via optional-key finnhub / alphavantage / tiingo / fmp (graceful fallback to free sources)\n\nFactors: the Alpha101 and QLib158 zoos are tagged for the `equity_in` and `equity_kr` universes, so they compute on NSE/BSE and KRX bars (the GTJA191 zoo stays China-only). Live/paper India trading uses the Shoonya / Dhan connectors (paper + read-only live; live order placement is structurally disabled because those brokers expose no paper/live switch). Live/paper Korea equity trading uses the **KIS** connector (한국투자증권; genuine 모의투자 paper sandbox on a separate host/port from 실전투자 live, plus read-only live — no mandate-gated live-trade profile yet). Live/paper Korea crypto trading uses the **Upbit** connector (KRW markets; paper + read-only live — no runtime paper/live discriminator, so live order placement is structurally disabled, same as Shoonya/Dhan). Read-only Korea/US equity access is also available via the **Toss Securities** connector (토스증권) — no verified sandbox, so order placement always refuses, same as Trading 212.\n\nExample workflow:\n1. Use `list_skills()` to discover strategy patterns\n2. Use `load_skill(\"strategy-generate\")` for the strategy creation guide\n3. Use `write_file()` to create `config.json` and `code/signal_engine.py`\n4. Use `backtest()` to run and get metrics (Sharpe, return, drawdown, etc.)\n\n### Multi-Agent Swarm Teams\n30 pre-built agent teams for complex research:\n- **Investment Committee**: bull/bear debate → risk review → PM decision\n- **Global Equities Desk**: A-share + HK/US + crypto → global strategist\n- **Crypto Trading Desk**: funding/basis + liquidation + flow → risk manager\n- **Earnings Research Desk**: fundamentals + revisions + options → earnings strategist\n- **Macro/Rates/FX Desk**: rates + FX + commodities → macro PM\n- **Quant Strategy Desk**: screening → factor research → backtest → risk audit\n- **Risk Committee**: drawdown, tail risk, regime analysis\n- And 23 more specialized teams\n\nUse `list_swarm_presets()` to see all teams, then `run_swarm()` to execute.\n\n### Alpha Zoo (478 pre-built alphas)\nOne-line cross-sectional IC / IR / alive-reversed-dead categorisation across five bundled zoos:\n- **qlib158** (154 alphas) — Microsoft Qlib's `Alpha158` feature handler, Apache-2.0 with pinned commit SHA.\n- **alpha101** (101 alphas) — Kakushadze (2015) \"101 Formulaic Alphas\" (arXiv:1601.00991), written from the paper appendix.\n- **gtja191** (191 alphas) — Guotai Junan 2014 \"191 Short-period Trading Alpha Factors\" research report.\n- **academic** (12 factors) — Fama-French 5 + Carhart momentum + Jegadeesh reversal + George-Hwang 52-week-high + Amihud illiquidity + Harvey-Siddique skew + Frazzini-Pedersen betting-against-beta (price-based proxies) + a correlation-rewiring stability score (from the in-repo correlation-regime skill).\n- **fundamental** (20 factors) — PIT-safe earnings yield, ROE, gross profitability and asset growth, plus ten SEC XBRL additions: book-to-market, FCF yield, accruals, ROA, CFO yield, operating margin, cash-to-assets, leverage, revenue growth and earnings growth, from daily fundamental panels; three CN A-share daily-valuation factors (PB, PS_TTM, dividend yield) fed by the ``val:*`` panel namespace from tushare daily_basic; and three industry-neutralized variants of those CN value factors, z-scored within Shenwan L1 peers against a PIT SW2021 industry tag from tushare index_member_all (membership is point-in-time; the SW2021 taxonomy itself is retroactive).\n\nEach alpha ships with `__alpha_meta__` (formula LaTeX + theme + universe + warmup + columns required), guarded by an AST purity gate + 300-row lookahead sentinel test. Use the `vibe-trading alpha {list,show,bench,compare,export-manifest}` CLI, the `/alpha/*` REST routes (browser at `/alpha-zoo`), or compose multi-factor signals via `ZooSignalEngine.from_zoo(...)`.\n\n### Finance Skills (90)\nComprehensive knowledge base covering:\n- Technical analysis (candlestick, Elliott wave, Ichimoku, SMC, harmonic, chanlun)\n- Quantitative methods (factor research, ML strategy, pair trading, multi-factor)\n- Risk management (VaR/CVaR, stress testing, hedging)\n- Options (Black-Scholes, Greeks, multi-leg strategies, payoff diagrams)\n- HK/US equities (SEC filings, earnings revisions, ETF flows, ADR/H-share arbitrage)\n- Crypto trading desk (funding rates, liquidation heatmaps, stablecoin flows, token unlocks, DeFi yields)\n- Behavioral finance, trade journal diagnostics, shadow account\n- Macro analysis, credit research, sector rotation, and more\n\nUse `load_skill(name)` to access full methodology docs with code templates.\n\n## Available MCP Tools (82)\n\n| Tool | Description | API Key |\n|------|-------------|---------|\n| `list_skills` | List all 90 finance skills | None |\n| `load_skill` | Load full skill documentation | None |\n| `start_research_goal` | Create an auditable research goal | None |\n| `get_research_goal` | Read the current research goal | None |\n| `add_goal_evidence` | Attach evidence to a research goal | None |\n| `update_research_goal_status` | Update goal lifecycle status | None |\n| `backtest` | Run vectorized backtest engine | None* |\n| `paper_trade_step` | Deterministic paper-trading matching engine; never places real orders | None* |\n| `factor_analysis` | IC/IR analysis + layered backtest | None* |\n| `alpha_zoo` | Browse bundled alpha metadata and registry health | None |\n| `alpha_bench` | Benchmark one alpha or a complete zoo | None* |\n| `analyze_options` | Black-Scholes price + Greeks | None |\n| `analyze_options_payoff` | Multi-leg expiry payoff + spot/IV scenarios | None |\n| `pattern_recognition` | Detect chart patterns (H&S, double top, etc.) | None |\n| `get_market_data` | Fetch OHLCV data (auto-detect + ordered fallback across 29 sources) | None* |\n| `get_fund_flow` | Capital fund-flow (main/retail net inflow) | None* |\n| `get_dragon_tiger` | Dragon-tiger list (龙虎榜) top buyer/seller seats | None* |\n| `get_northbound_flow` | Northbound (Stock Connect) net flow | None* |\n| `get_southbound_flow` | Southbound (Stock Connect) net buy into HK (Eastmoney/HKEX) | None |\n| `get_margin_trading` | Margin trading & short-selling balances | None* |\n| `get_block_trades` | Block-trade (大宗交易) records | None* |\n| `get_shareholder_count` | Shareholder-count history per symbol | None* |\n| `get_lockup_expiry` | Restricted-share lockup release schedule | None* |\n| `get_sector_info` | Sector / industry constituents & performance | None* |\n| `get_research_reports` | Sell-side analyst research reports | None* |\n| `get_stock_news` | Market & company news headlines | None* |\n| `get_sec_filings` | SEC EDGAR filings (10-K/10-Q/8-K, etc.) | None |\n| `get_financial_statements` | Income / balance / cash-flow statements | None* |\n| `get_options_chain` | Options chain (strikes, IV, OI, Greeks) | None* |\n| `get_stock_profile` | Valuation, analyst estimates & institutional holdings (US/HK) | None |\n| `screen_market` | Market screener with fundamental/technical filters | None* |\n| `search_symbol` | Symbol / ticker search across markets | None |\n| `get_macro_series` | FRED macroeconomic series | FRED_API_KEY |\n| `get_fund_nav` | Chinese off-exchange fund NAV history | GILDATA_TOKEN |\n| `get_cn_macro_series` | China macro/industry EDB series (NL query) | GILDATA_TOKEN |\n| `get_cn_announcements` | A-share/HK/fund announcement search (NL query) | GILDATA_TOKEN |\n| `search_broker_reports` | Broker research search over the Juyuan library (NL query) | GILDATA_TOKEN |\n| `iwencai_search` | A-share natural-language research search | IWENCAI_KEY |\n| `qveris_search` | Search QVeris premium data/tool marketplace (free discovery) | QVERIS_API_KEY + paid mode |\n| `qveris_inspect` | Inspect QVeris tool schemas before executing (free) | QVERIS_API_KEY + paid mode |\n| `qveris_execute` | Execute a QVeris capability; budget-bounded, may be billable | QVERIS_API_KEY + paid mode |\n| `web_search` | Search the web via DuckDuckGo | None |\n| `read_url` | Fetch web page as Markdown | None |\n| `read_document` | Extract text from PDF/DOCX/XLSX/PPTX/images | None |\n| `write_file` | Write files (config, strategy code) | None |\n| `read_file` | Read file contents | None |\n| `read_run_artifact` | Structured run-artifact reads (rows / downsample / meta) | None |\n| `list_strategies` | Browse discoverable strategies (Alpha Zoo + SDM store) | None |\n| `query_strategies` | Evidence-gated query: regime / Sharpe / quality / cost filters | None |\n| `get_strategy_evidence` | Per-regime evidence rows for one strategy | None |\n| `adapt_strategy` | Prepare an evidence-gated child strategy for generation and its own backtest |\n| `refresh_strategy_evidence` | Rebuild the disposable strategy-evidence cache from run artifacts | None |\n| `analyze_trade_journal` | Parse broker CSV → profile + behavior diagnostics | None |\n| `extract_shadow_strategy` | Distill 3-5 if-then rules from profitable roundtrips | None |\n| `run_shadow_backtest` | Multi-market backtest + delta-PnL attribution | None* |\n| `render_shadow_report` | HTML/PDF shadow report (8 sections + charts) | None |\n| `scan_shadow_signals` | Today's symbols matching the shadow's cadence | None |\n| `list_swarm_presets` | List multi-agent team presets | None |\n| `run_swarm` | Execute a multi-agent research team | LLM key |\n| `get_swarm_status` | Poll swarm run status without blocking | None |\n| `get_run_result` | Get final report and task summaries | None |\n| `list_runs` | List recent swarm runs with metadata | None |\n| `reap_stale_runs` | Finalize stale swarm runs | None |\n| `retry_run` | Re-run a failed/stale swarm run | LLM key |\n| `trading_connections` | List selectable connector profiles | None |\n| `trading_select_connection` | Select the default connector profile | None |\n| `trading_check` | Check connector readiness | Connector app/OAuth |\n| `trading_account` | Read account summary from selected connector | Connector app/OAuth |\n| `trading_positions` | Read positions from selected connector | Connector app/OAuth |\n| `trading_orders` | Read open orders from selected connector | Connector app/OAuth |\n| `trading_quote` | Read a quote snapshot from selected connector | Connector app/OAuth |\n| `trading_history` | Read historical bars from selected connector | Connector app/OAuth |\n| `get_institutional_holdings` | SEC 13F-HR holdings by manager/ticker + quarter-over-quarter position diffs | None |\n| `etf_holdings` | ETF look-through — SEC N-PORT (US) and full-book A-share fund reports | None |\n| `prediction_market` | Event-contract search/market/history as labelled implied probability | None |\n| `research_papers` | arXiv + OpenAlex search/read with source-anchored claim extraction | None |\n| `quantlib_call` | Pure-compute finance math — 308 functions across 24 quantlib modules | None |\n| `cashflow_performance` | XIRR / MOIC / DPI / TVPI / TWR / Modified Dietz over dated cash flows | None |\n| `orderbook_depth` | Crypto L2 ladder — spread bps, depth imbalance, impact cost | None |\n| `sentiment` | Local lexicon text scoring + crypto Fear & Greed Index | None |\n| `technical_indicators` | RSI / MACD / Bollinger / SMA / EMA through the existing loaders | None* |\n| `get_fundamentals` | PIT-safe SEC fundamentals panels (filed-date anchored) | None |\n\n*A-share symbols require `TUSHARE_TOKEN`. HK/US/Canada/crypto are free. Trading connector rows use the selected connector profile, e.g. IBKR local TWS/Gateway or Robinhood MCP OAuth.\n\n## Quick Start\n\n```bash\npip install vibe-trading-ai\n```\n\nThat's it — no API keys needed for HK/US/Canada/crypto markets. Start using `backtest`, `get_market_data`, `analyze_options`, `analyze_trade_journal`, `extract_shadow_strategy`, `web_search`, the **Alpha Zoo** (`vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025`), and all 90 skills immediately.\n\n## Loading Tools from External MCP Servers\n\nThe built-in agent can load tools from your own external MCP servers in addition to its local toolset.\n\n> **Note:** This is the *MCP client* path — the opposite of the MCP plugin listed above. The plugin above makes Vibe-Trading's tools available to your agents. This section lets Vibe-Trading's own agent call tools from *your* servers.\n\n### Setup\n\nCreate `~/.vibe-trading/agent.json`:\n\n```json\n{\n \"mcpServers\": {\n \"my-server\": {\n \"command\": \"uvx\",\n \"args\": [\"my-mcp-server\"],\n \"toolTimeout\": 30,\n \"enabledTools\": [\"*\"]\n }\n }\n}\n```\n\nOrdinary external MCP tools appear automatically in every `vibe-trading run` / `vibe-trading chat` call. They are injected after local tools under stable names: `mcp__`. Live-broker MCP servers are consumed through the connector-scoped `trading_*` tools instead of exposing raw `mcp__*` tools to the agent.\n\n### Official IBKR MCP read-only probe\n\nAdd Interactive Brokers' official MCP endpoint as a read-only external server:\n\n```json\n{\n \"mcpServers\": {\n \"ibkr\": {\n \"type\": \"streamableHttp\",\n \"url\": \"https://api.ibkr.com/v1/api/mcp-public\",\n \"auth\": {\n \"type\": \"oauth\",\n \"scopes\": [\"mcp.read\"],\n \"clientName\": \"Vibe-Trading\",\n \"cacheDir\": \"~/.vibe-trading/live/ibkr/oauth\"\n },\n \"enabledTools\": [\"*\"]\n }\n }\n}\n```\n\nAuthorize it with `vibe-trading connector authorize ibkr-live-official-mcp-readonly`. The wildcard is accepted\nonly for this `mcp.read` probe. Generic `trading_account` and `trading_positions`\ncalls stay disabled until IBKR publishes stable read tool names that Vibe-Trading\ncan map safely; `mcp.write` requires an explicit tool allowlist and live\norder-guard handling. If IBKR issues a pre-registered OAuth client, add\n`clientId` and `clientSecret` inside `auth`.\n\n### Official eToro Public API MCP (discovery + dev)\n\neToro ships a hosted MCP at `https://mcp.public-api.etoro.com` with live OpenAPI\nroute discovery (`get-all-routes`, `get-route-spec`) and optional execution\n(`execute-read`, `execute-write`). Use it for **API exploration and codegen** —\nproduction agent trading in Vibe-Trading goes through the built-in `etoro-*`\nconnector profiles and `trading_*` / `etoro_*` tools (mandate gate on live writes).\n\nAdd to `~/.vibe-trading/agent.json` (credentials on the connection, not in chat):\n\n```json\n{\n \"mcpServers\": {\n \"etoro-public-api\": {\n \"type\": \"streamableHttp\",\n \"url\": \"https://mcp.public-api.etoro.com\",\n \"headers\": {\n \"x-api-key\": \"YOUR_PUBLIC_API_KEY\",\n \"x-user-key\": \"YOUR_USER_KEY\"\n },\n \"enabledTools\": [\"get-all-routes\", \"get-route-spec\", \"execute-read\"]\n }\n }\n}\n```\n\nOmit `execute-write` unless you want the MCP to place trades directly (bypasses\nVibe-Trading's live mandate gate). Install skill:\n`https://mcp.public-api.etoro.com/skill`\n\n### Trading connector profiles\n\nThe public trading surface is connector-first. Choose a connector profile, then\npaper/live is just an attribute under that connector.\n\n```bash\npip install \"vibe-trading-ai[ibkr]\"\nvibe-trading connector list\nvibe-trading connector use ibkr-paper-local\nvibe-trading connector configure ibkr-paper-local --yes\nvibe-trading connector check\nvibe-trading connector account\nvibe-trading connector positions\nvibe-trading connector orders\nvibe-trading connector quote AAPL\nvibe-trading connector history AAPL --duration \"30 D\" --bar-size \"1 day\"\n```\n\nLive-runner model spending is independent of mandate trade limits. Operators\nconfigure it through Runtime / the chat runner panel, or\n`vibe-trading connector runner-settings --profile robinhood-live-mcp`\n(`--file settings.json` saves settings while stopped). Defaults are one model\ndecision every 15 minutes, 26 decisions and 1,000,000 reported tokens per UTC day,\nwith fresh conversation context per decision. A once-daily time and IANA timezone\nare optional. Dollar limits require operator-supplied input/output prices and are\nestimates, not provider billing guarantees. Usage is checked after each response;\nbudget exhaustion or missing usage activates the existing kill switch. The agent\nmust not edit this operator state or raise its own spending limits.\n\nDefault ports are TWS paper `7497`, IB Gateway paper `4002`, TWS live-readonly\n`7496`, and IB Gateway live-readonly `4001`.\n\n### Config fields\n\n| Field | Required | Default | Description |\n|-------|----------|---------|-------------|\n| `type` | stdio: no, HTTP: yes | inferred only for stdio | Transport type. Use `sse` or `streamableHttp` for URL-based servers. |\n| `command` | stdio: yes | — | Executable to launch |\n| `args` | no | `[]` | Command arguments |\n| `env` | no | `{}` | Extra env vars for the subprocess |\n| `url` | HTTP: yes | — | Remote SSE / streamable HTTP endpoint URL |\n| `headers` | no | `{}` | Extra HTTP headers for SSE / streamable HTTP servers |\n| `toolTimeout` | no | `30` | Seconds before a tool call is cancelled |\n| `enabledTools` | no | `[\"*\"]` | Allowlist of remote tool names. `[\"*\"]` enables all |\n\nFor URL-based transports, `type` is required. The agent no longer guesses between SSE and streamable HTTP from the URL suffix.\n\n### Per-session override (API)\n\n> **Security — disabled by default.** `mcpServers` defines subprocess `command`/`args`/`env` and is therefore restricted to operator-level trust. API callers **cannot** inject MCP server definitions through `POST /sessions` unless the server operator explicitly opts in.\n\nTo enable session-level MCP injection, set the environment variable on the server before starting the agent:\n\n```bash\nexport ALLOW_SESSION_MCP_SERVERS=1\n```\n\nWith the opt-in active, pass `mcpServers` inside `session.config` to extend or replace the global config for that session only:\n\n```json\n{\n \"config\": {\n \"mcpServers\": {\n \"research\": {\n \"command\": \"uvx\",\n \"args\": [\"research-mcp\"],\n \"enabledTools\": [\"search\"]\n }\n }\n }\n}\n```\n\nWithout `ALLOW_SESSION_MCP_SERVERS=1`, any `mcpServers` key in `session.config` is silently stripped before config loading. The global operator config on disk (`~/.vibe-trading/agent.json`) is always respected regardless of this flag.\n\n### v1 limits\n\n- **Transport:** stdio, SSE, and streamable HTTP.\n- **Execution:** serial only. MCP tools never enter the parallel readonly path.\n- **Surfaces:** tools only. Resources and prompts are not exposed.\n- **Swarm:** MCP tools are excluded from Swarm worker registries in v1.\n- **Hot reload:** not supported. Restart the process to pick up config changes.\n\n### Failure handling\n\n| Case | Behavior |\n|------|----------|\n| Missing config file | falls back to empty config — no MCP servers loaded |\n| Invalid config file | logs a warning and falls back to empty config |\n| Server fails to start | that server is skipped; local tools and other servers still load |\n| Tool call times out | returns a normalized error payload instead of raising |\n| Two server names collide after sanitization | deterministic hash suffix appended; operator warning emitted |\n\n\n\n## Examples\n\n**Backtest a MACD strategy on Apple:**\n> Backtest AAPL with MACD crossover strategy (fast=12, slow=26, signal=9) for 2024\n\n**Analyze my trade journal and build a Shadow Account:**\n> Call analyze_trade_journal on ~/Downloads/tonghuashun.csv, then extract_shadow_strategy with min_support=3, then run_shadow_backtest for the last year, then render_shadow_report.\n\n**Run an investment committee review:**\n> Use run_swarm with investment_committee preset to evaluate NVDA. Variables: target=NVDA.US, market=US\n\n**Factor analysis on CSI 300:**\n> Run factor_analysis on CSI 300 stocks using pe_ttm factor from 2023 to 2024\n\n**Options analysis:**\n> Use analyze_options: spot=100, strike=105, 90 days, vol=25%, rate=3%\n\n**Multi-leg options payoff:**\n> Use analyze_options_payoff for a 95/105 bull call spread at spot 100 with 30 days remaining: long one 95 call at premium 8, short one 105 call at premium 3, multiplier 100, commission rate 0.001.\n","agent/src/skills/ashare-pre-st-filter/SKILL.md":"---\nname: ashare-pre-st-filter\ndescription: A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报，预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示，并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股，不预测财务造假。\ncategory: risk-analysis\n---\n\n> **依赖**：本 skill 必须配合 [tushare skill](../tushare/SKILL.md) 使用，缺数据时回退到 [akshare skill](../akshare/SKILL.md)。\n\n# A 股 ST/*ST 风险预测\n\n## 适用范围\n\n- 仅 A 股**主板 / 创业板 / 科创板**。港股、美股、加密、商品、**北交所**（`8xxxxx.BJ` 监管规则与本 skill 阈值不一致）均不适用，本 skill 直接拒绝。\n- 仅做\"下一财年是否会被 ST/*ST\"的前瞻预测，不做财务造假预测。\n- 输出双轴结果：**触线风险等级** + **预测可信度**。\n\n## 触发条件\n\n用户提问形如：\n- \"分析 000729.SZ 的 ST 风险\"\n- \"600xxx 下个年报会不会被 ST\"\n- \"帮我看一下 300xxx 是否有退市风险\"\n\n收到此类请求时，按下面\"分析流程\"执行；其他场景不要主动启用本 skill。\n\n## 数据获取规范（tushare 优先 → akshare 兜底）\n\n**铁律**：\n\n1. **所有财务/基本面/分红/审计/ST 状态数据，必须先调用 [tushare skill](../tushare/SKILL.md)**，按下表指定的接口名、`ts_code`、`period` 拉取。\n2. 当 tushare 接口因积分不足、token 缺失或返回空时，**回退到 [akshare skill](../akshare/SKILL.md)** 对应函数。\n3. **akshare 兜底数据必须在最终输出\"备注\"中显式标注**：`数据源：akshare（非官方聚合，不保证完整性，建议核对原始公告）`。**不允许只用 akshare 给出\"高可信度\"预测**——akshare 兜底的红线项可信度强制降一档（高→中高，中高→中，中→低）。\n4. 监管处罚（E2）走本 skill 自带的 [scripts/fetch_sina_penalties.py](scripts/fetch_sina_penalties.py)，不经过 tushare/akshare。\n\n### 数据需求映射表\n\n| 数据需求 | tushare 接口（首选） | 抓取参数 / 时间窗口 | akshare 兜底 | akshare 注意事项 |\n|---------|---------------------|-------------------|-------------|----------------|\n| 当前 ST 状态 | `stock_st` | 最近一个交易日 | `stock_zh_a_st_em()` | 仅返回当前 ST/退市整理板列表，历史不可查 |\n| 历史改名 | `namechange` | `ts_code` 全历史 | `stock_zh_a_new_em()`（仅新股，无改名） | akshare 无完整改名接口，必要时跳过 |\n| 股票基础信息（板块判断） | `stock_basic` | `list_status='L'` | `stock_individual_info_em(symbol)` | 字段名不同，需自行映射 |\n| 业绩预告 | `forecast` | `ts_code` + 最近 2 个 period（`{Y}0630`/`{Y}0930`/`{Y}1231`） | `stock_yjyg_em(date='YYYYMMDD')` | 按报告期日期反查，需先按 period 拉全市场再筛 ts_code |\n| 业绩快报 | `express` | 同上，最近 2 个 period | `stock_yjbb_em(date='YYYYMMDD')` | 同上，全市场快照需筛 |\n| 利润表 | `income` | `ts_code` + 最近 5 个 period（过去 4 年年报 `{Y}1231` + 最新中报/三季报） | `stock_financial_report_sina(stock='sh600000', symbol='利润表')` | 单位不一致（万元 vs 元），需做归一；季报缺失常见 |\n| 资产负债表 | `balancesheet` | 同上 | `stock_financial_report_sina(stock='sh600000', symbol='资产负债表')` | 同上 |\n| 现金流量表 | `cashflow` | 同上 | `stock_financial_report_sina(stock='sh600000', symbol='现金流量表')` | 同上 |\n| 财务指标（扣非净利润等） | `fina_indicator` | `ts_code` + 最近 5 个 period | `stock_financial_analysis_indicator(symbol='600000')` | 字段中文，扣非字段名为\"扣除非经常性损益后的净利润\" |\n| 审计意见 | `fina_audit` | `ts_code` + 最近 2 个年报 period | **akshare 无对应接口** | 缺失时在输出中标注\"审计意见数据缺失\"，**不得跳过**——必须提示用户去官网查 |\n| 分红 | `dividend` | `ts_code` + 近 5 年 `div_proc='实施'` | `stock_history_dividend_detail(symbol='600000', indicator='分红')` | 字段中文，需把\"派息\"金额×总股本得到现金分红总额 |\n| 日线行情（1 元退市预警） | `daily` | `ts_code` + 最近 30 个交易日 | `stock_zh_a_hist(symbol='600000', period='daily', adjust='')` | 必须用**不复权**价格判断 1 元退市线 |\n| 每日指标（市值） | `daily_basic` | `ts_code` + 最近一个交易日 | `stock_zh_a_spot_em()` 全市场快照筛 `代码=600000` | 字段名为\"总市值\"（**单位：元**，东方财富 push2 原始口径，akshare 不做缩放），换算成亿元需 `/1e8`。⚠️ 不要与 tushare `daily_basic.total_mv`（万元）混淆——本列是 akshare 兜底口径 |\n| 财报披露日期 | `disclosure_date` | `ts_code` + 当前年度 | **akshare 无对应接口** | 缺失时按经验估算（4/30 年报、8/31 中报、10/31 三季报） |\n| 监管处罚 | — | — | **走本 skill 自带 sina 脚本** | 见 E2 章节 |\n\n### 时间窗口口径统一\n\n按数据类型分别处理，**禁止用单一 60 天经验值统一对齐**：\n\n- **财务报表（income / balancesheet / cashflow / fina_indicator）**：以 `disclosure_date` 实际公告日为准；该接口不可用时按法定截止日（年报 4/30、中报 8/31、三季报 10/31、一季报 4/30）做兜底。在公告日 +1 天起方可视该 period 为\"最新可得\"。\n- **\"过去 N 年年报\"**：一律 `period = {Y}1231`，不要用日历年起止。\n- **业绩预告 forecast / 业绩快报 express**：只取 `ann_date ≥ 当前年度 1 月 1 日` 的记录；按 `ann_date` 倒序取最新一条，老 period 的预告对预测无效。\n- **dividend**：按 `(end_date, ann_date, cash_div_tax)` 三元组**强制去重**——tushare 同一笔分红会出现 3-4 行（预案/股东大会通过/实施），直接累加会三倍误算。\n\n## 第一步：当前状态核查（M0）\n\n**判断公司当前是否已被风险警示，并据此调整分析方向（不一刀切结束）**：\n\n调用顺序：\n1. 优先调用 `stock_st`（tushare skill，3000 积分），按当前最近交易日查询；记录 `type` / `type_name`。\n2. 如积分不足，回退到 `namechange`（免费）：拉取 `ts_code` 全部历史改名记录，取按 `start_date` 排序的最新一条；判断 `name` 前缀。\n3. 兜底用 `stock_basic.name` 做名称确认。\n\n**分支处理**：\n\n| 当前状态 | 分析方向 | 是否继续 |\n|---------|---------|---------|\n| 正常股票 | 预测下一财年是否会被 ST/*ST | **是**（默认流程） |\n| 已 ST（普通） | 改为预测：是否会进一步转 *ST 或退市；R1-R4 阈值需更严格（亏损链门槛降一年） | **是** |\n| 已 *ST | 改为预测：是否会被强制退市；重点看 R1 营收 + R2 净资产 + E1 审计意见 | **是** |\n| 已退市整理期 / 已摘牌 | 直接结束 | **否** |\n\n## 第二步：板块阈值表（决定红线）\n\n**先用 `ts_code` 前缀判断板块，再套用对应阈值。不要混用。**\n\n| 板块 | 代码识别 | 营收红线 | 市值退市线 | 三年累计分红阈值 |\n|------|---------|---------|-----------|----------------|\n| 主板 | `60xxxx.SH` / `00xxxx.SZ`（除 30 / 688 开头外） | 净利润为负 且 营收 < 3 亿 | 市值 < 5 亿 | 累计 < 5000 万 且 < 年均净利润 30% |\n| 创业板 | `30xxxx.SZ` | 净利润为负 且 营收 < 1 亿 | 市值 < 3 亿 | 累计 < 3000 万 且 < 年均净利润 30%（研发豁免：研发投入占营收 ≥ 5% 或近三年累计 ≥ 6000 万） |\n| 科创板 | `688xxx.SH` | 净利润为负 且 营收 < 1 亿 | 市值 < 3 亿 | 累计 < 3000 万 且 < 年均净利润 30%（研发豁免：研发投入占营收 ≥ 5% 或近三年累计 ≥ 6000 万） |\n\n## 第三步：预测时点选择\n\n按\"最新可得证据\"原则确定基准时点，从下到上择优：\n\n| 基准时点 | 数据接口 | 可信度基线 | 适用季节 |\n|---------|---------|-----------|---------|\n| **业绩预告 forecast** | `forecast` | **高** | 1 月底 / 7 月中旬 / 10 月底高发期 |\n| **业绩快报 express** | `express` | **高** | 1-4 月年报前夕 |\n| **三季报** | `income/balancesheet/cashflow` (`period=YYYYMMDD`，9 月末) | **中高** | 10 月底披露后到次年年报前 |\n| **中报** | 同上，6 月末 period | **中** | 8 月底披露后到三季报前 |\n| **去年年报** | 同上，12 月末 period | **低** | 仅作为基准对照 |\n\n**证据优先级**（同一指标多个来源时，高的覆盖低的）：\n\n```\nforecast / express > 三季报 > 中报 > 去年年报 > 经验外推\n```\n\n输出时**必须在每个预测项明示采用了哪一级证据**。如果存在 forecast 给出的全年净利润区间，**禁止再用三季报机械年化覆盖**。\n\n## 第四步：四项可预测红线\n\n### R1 营收 + 净利润红线\n\n**目标**：预测全年营收、归母净利润和**扣非净利润**，按监管口径\"扣非前后孰低者\"判定。\n\n**口径关键**：监管原文是 `min(n_income, profit_dedt) < 0` 且 `revenue < 板块阈值` —— **必须同时预测扣非净利润**，不能只用归母。仅看归母会漏掉\"归母为正但扣非为负\"的造壳公司。\n\n**预测方法（按基准时点选用，至少一种）**：\n\n- **forecast 直采法**：若 `forecast` 给出 `net_profit_min` / `net_profit_max`，直接取区间作为全年归母预测；扣非用过去两年扣非率（`profit_dedt / n_income` 中位数）折算；可信度=高。营收红线仍需结合最新报表外推。\n- **express 直采法**：若 `express` 已披露，`revenue` / `n_income` 即为全年快报值；扣非同样按过去两年率折算（express 一般无扣非字段）；可信度=高。\n- **Q4 单季回补法**（三季报基准）：\n - `Q1Q3_revenue` = 三季报营业收入\n - `Q4_revenue_est` = 过去两年 Q4 营收占全年比例的中位数 × 当前年化预估值\n - `revenue_full_year_est = Q1Q3_revenue + Q4_revenue_est`\n - `n_income` / `profit_dedt` 同法处理\n - 可信度=中高\n- **下半年情景外推法**（中报基准，强季节性公司必须用）：\n - 取过去两年 H2 营收占全年比例均值，给出悲观/基准/乐观三情景\n - `revenue_full_year_est = H1_revenue / H1_share_avg`\n - 可信度=中\n- **同比延续法**（中报基准，弱季节性公司可用）：\n - `revenue_full_year_est = H1_revenue × (last_year_full_revenue / last_year_H1_revenue)`\n - 可信度=中\n\n**禁止**：H1 直接乘 2、Q1-Q3 直接乘 4/3。任何机械年化**强制降到\"低\"可信度**并在输出中注明。\n\n**触线判定**（口径：`worst_profit = min(n_income_est, profit_dedt_est)`）：\n- 高风险：`worst_profit < 0` 且 `revenue_est < 板块阈值`\n- 中风险：`worst_profit < 0` 但营收达标；或营收逼近阈值（< 阈值 × 1.2）\n- 低风险：上述均不满足\n\n> **系数说明**：R1 中风险的 1.2 是预测项预警 buffer（预测本身有误差，需窄 buffer）；R4 高风险的 1.5 是双年联动判定 buffer（两年都亏且营收接近阈值才是真退市信号，需宽 buffer）。两个系数不同是有意为之，**不要错误地统一**。\n\n### R2 年末净资产红线\n\n**目标**：判断年末归母股东权益是否可能转负。\n\n**方法**：\n1. 取最新一期 `balancesheet.total_hldr_eqy_exc_min_int`（最新中报或三季报），记为 `current_eq`。\n2. 计算 `eq_year_end_est = current_eq + 剩余季度净利润预测 − 当前年度已实施现金分红总额`。\n - 剩余季度净利润：中报基准用 R1 的 H2 预测，三季报基准用 R1 的 Q4 预测。\n - 已实施分红总额：见 R3 的去重 + 量纲公式 `cash_div_tax / 10 × total_share`。\n\n**触线判定**（统一相对阈值，单位：元）：\n- 高风险：`current_eq < 0` 或 `eq_year_end_est < 0`\n- 中风险：`0 ≤ eq_year_end_est < 1e8`（1 亿元）\n- 低风险：`eq_year_end_est ≥ 1e8`\n\n**注意**：商誉减值、长期股权投资减值在 Q3/Q4 集中确认，应在备注提示\"若计提大额减值，净资产可能进一步下行\"，但**不得在没有公告依据时擅自减值**。\n\n### R3 分红达标前瞻\n\n**目标**：判断\"近三年累计现金分红 < 年均净利润 30% 且 < 板块累计阈值\"是否会在下一次年度考核时同时满足（监管原文：两条件**同时**触发才警示）。\n\n**单笔分红量纲公式**（**必须用此公式，不要用 `cash_div_tax × base_share`**）：\n\n```\ncash_dividend_total = cash_div_tax / 10 × total_share\n```\n\n其中 `cash_div_tax` 来自 `dividend`（每 10 股派现金额，含税），`total_share` 来自 `daily_basic.total_share`（最新总股本，单位万股 → 需 ×1e4 换成股）或 `stock_basic`。tushare `dividend.base_share` 字段对很多公司为 None，**禁止直接使用**。\n\n**方法**：\n1. 用 `dividend` 拉取该 `ts_code` 近五年记录，按 `(end_date, ann_date, cash_div_tax)` 三元组**强制去重**——同一笔分红 tushare 会返回\"预案/股东大会通过/实施\"3-4 行。\n2. 折算系数（按 `div_proc` 状态）：`实施=1.0` / `股东大会通过=0.7` / `预案=0.5`；同一 `end_date` 取系数最高那条。\n3. 用 `income` 拉取近三年完整年度归母净利润（period=YYYY1231）。\n4. 计算考核窗口 = `[当前年-2, 当前年]`，即\"过去两年已落地分红 + 本年度预计分红\"。\n5. 本年度预计分红：\n - 若 `dividend` 已查到本年度记录：按上面折算系数计入。\n - 否则用\"过去两年平均分红率\"× R1 预测全年净利润（基准情景）做估算，可信度降一档。\n6. 与年均净利润比较：\n - 条件 A：`cumulative_div_3y < 30% × avg_n_income_3y`\n - 条件 B：`cumulative_div_3y < 板块累计阈值`\n7. 创业板 / 科创板若研发投入占比满足豁免条件，备注提示\"研发达标可能豁免\"。\n\n**触线判定**：\n- 高风险：A 和 B 同时满足 **且** 过去三年至少一年净利润为正（即\"盈利年份未达标\"——监管以盈利年为基准）。\n- 中风险：仅一个条件满足；或本年度分红尚未公告。\n- 低风险：累计分红已稳定达标；或过去三年累计净利润 ≤ 0（无分红基础，按 R1 联动）。\n\n**禁止**：本年度尚未公告分红时，不得断言\"必然不达标\"，只能给出概率。\n\n### R4 连续亏损 / 扣非亏损链\n\n**目标**：判断是否会触发\"连续两年扣非前后净利润孰低者为负\" → *ST。\n\n**口径关键**：监管原文是**连续两年**（不是三年），且 *ST 财务类强制退市的法定条件是 **\"亏损 + 营收<板块阈值\" 必须同时满足**——只看亏损链会系统性高估风险（实证：闻泰科技 2024 年亏损但营收超千亿，不会被 *ST）。\n\n**方法**：\n1. 取过去一年完整年报的 `n_income`、`fina_indicator.profit_dedt`，记 `worst_last = min(n_income, profit_dedt)`、`revenue_last`。\n2. 用 R1 已经预测好的 `worst_profit_est` 和 `revenue_full_year_est`（基准情景），对应当前年度。\n3. 检查两年的 `worst < 0` 组合，**并叠加营收联动条件**。\n\n**触线判定**（双条件，亏损链 + 营收联动）：\n- 高风险：`worst_last < 0` 且 `worst_profit_est < 0`，**且**（R1 当前年命中高风险 **或** `revenue_full_year_est < 板块阈值 × 1.5`）——即两年均亏 + 营收处于退市风险区间。\n- 中风险：(a) 连续两年均亏但营收远超阈值（≥ 阈值 × 1.5）——亏损链成立但不会触发财务类 *ST，仍可能因经营恶化转 ST；(b) 仅 `worst_profit_est < 0`（当前年预测亏损，去年盈利，未形成连续）；(c) 仅 `worst_last < 0` 但当前年预测为正且接近 0（< 板块阈值 × 0.5）。\n- 低风险：`worst_last ≥ 0` 且 `worst_profit_est ≥ 0`。\n\n**为什么要叠加营收**：A 股 *ST 财务类强制退市的法定条件是\"扣非前后净利润孰低 < 0 **且** 营收 < 板块阈值\"，二者必须同时满足。仅亏损不达营收阈值的公司（如大型周期股短期亏损）会被市场和监管视作\"周期性亏损\"而非\"持续经营存疑\"。\n\n## 第五步：三项事实/临界证据面（不参与预测可信度）\n\n### E1 审计意见（事实）\n\n调用 `fina_audit`，取最近 2 个完整年报：\n- 高风险：最新年报 `audit_result` 含\"无法表示意见\"或\"否定意见\"；**或单次出现\"保留意见\"**（实证支持：上交所近年规范类 *ST 案例多为单次保留即触发）。\n- 中风险：连续两年同一负面结论但本期已修正；或带强调事项段的无保留意见涉及持续经营。\n- 低风险：标准无保留意见。\n\n**禁止预测下一份审计意见**——审计是事后行为。\n\n**akshare 兜底缺失时的人工核查路径**（`fina_audit` 无 akshare 替代接口，必须给出可点击 URL）：\n\n| 来源 | URL 模板 | 查询方式 |\n|------|---------|---------|\n| 巨潮资讯网（公告原文，权威） | `http://www.cninfo.com.cn/new/disclosure/stock?orgId=&stockCode={code6}` | 进入页面后筛选\"定期报告 → 年度报告\"，下载 PDF 在第十节\"财务报告 → 审计报告\"查 `审计意见类型` |\n| 巨潮高级搜索（按关键词） | `http://www.cninfo.com.cn/new/fulltextSearch/full?searchkey={code6}+审计报告&sdate=&edate=&isfulltext=false&sortName=time&sortType=desc` | 直接命中含\"审计报告\"标题的公告 |\n| 上交所信息披露 | `http://www.sse.com.cn/disclosure/listedinfo/regular/?stockCode={code6}` | 仅限 60xxxx / 688xxx |\n| 深交所信息披露 | `http://www.szse.cn/disclosure/listed/notice/index.html?stock={code6}` | 仅限 00xxxx / 30xxxx |\n\n其中 `{code6}` 为 6 位股票代码（去掉 `.SH` / `.SZ` 后缀）。**强制要求**：当 `fina_audit` 返回空时，输出报告的\"备注\"必须列出上面巨潮的两条 URL 之一，便于用户人工核查；不得只写一句\"审计意见数据缺失\"了事。\n\n### E2 监管处罚（事实，双窗口：上一财年 + 过去 12 个月）\n\n**这是本 skill 的关键证据面。**\n\n**窗口规则（双窗口策略，单次抓取在内存中切片）**：\n\n- 窗口 A — `[当前年-1 的 1 月 1 日, 当前年-1 的 12 月 31 日]`（上一个完整财年）：用于 reason_normalized 单条评级。\n- 窗口 B — `[分析日 - 365 天, 分析日]`（过去 12 个月滚动）：用于**频次增强规则**。\n- 若分析时点已经在 5 月之后且最新年报已披露完毕，窗口 A 可改为 `[最新年报 end_date - 365 天, 最新年报 end_date]`。\n\n**调用方式（推荐：单次拉全量 + 内存切片）**：\n\n```bash\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --no-filter\n```\n\n返回页面全量记录后，由 LLM 在内存中按窗口 A、窗口 B 切两份。也可以用 `--start-date / --end-date` 单独抓取，但同一次分析**避免发起两次 HTTP**。\n\n输出 JSON 列表，字段：`ann_date / event_type / title / reason / reason_normalized / content / issuer / issuer_normalized / subject_normalized / target_relevance / e2_countable / source_url`。\n\n**相关性铁律**：调用脚本时必须传入 `--stock-name {股票简称}`（来自 `stock_basic.name`），必要时用 `--alias` 补充曾用名/简称。新浪页面可能收录“某个人证券账户交易列表里出现目标股票”的记录，这类记录只说明目标股票被交易过，**不是上市公司/股东/董监高处罚**。脚本会将其标为 `target_relevance=security_mention_only` 且 `e2_countable=false`，E2 频次统计必须排除。\n\n**`subject_normalized` 字段**（用于频次权重折算）：\n- `company` — 处罚主体是公司本身（默认；不含个人身份关键词时归此类）\n- `shareholder` — 控股股东 / 实控人 / 5% 以上大股东\n- `officer` — 董事 / 监事 / 高管（董事长、总经理、董秘、财务总监等）\n\n**风险等级判定**：\n\n| 命中 `reason_normalized` | 等级贡献 |\n|---|---|\n| 财务造假 / 虚假陈述 / 信息披露违规 | 高（强烈预警） |\n| 违规担保 / 占用资金 | 高（直接触及 ST 红线） |\n| 内幕交易 / 市场操纵 / 违规减持 | 中 |\n| 其他/unknown | 低（仅作背景） |\n\n**频次增强规则（治理质量预警，窗口 B）**：\n\n监管函本身反映公司治理与内控质量，密集出现是\"规范类 ST\"（保留意见、内控非标、信披重大缺陷）的强领先指标。但**处罚主体不同，治理含义不同**：股东减持违规、董监高短线交易反映的是个人合规问题，不应等同于\"公司治理失序\"。\n\n**主体加权计数**（按 `subject_normalized` 折算后再查表）：\n\n```\n加权条数 = Σ 单条权重\n 其中：subject_normalized == \"company\" → 权重 1.0\n subject_normalized == \"officer\" → 权重 0.5\n subject_normalized == \"shareholder\" → 权重 0.5\n```\n\n| 过去 12 个月监管函/问询/警示函**加权条数** | 频次等级 |\n|---|---|\n| ≥ 5.0 | **极高** |\n| 3.0 - 4.5 | 高 |\n| 2.0 - 2.5 | 中 |\n| < 2.0 | 低 |\n\n**判定来源**：仅当 `e2_countable=true` 时，脚本输出的 `event_type ∈ {警示, 问讯, 监管关注, 监管函}` 或 `issuer_normalized ∈ {上交所, 深交所, 北交所, 证监会, 地方证监局}` 才计入条数；条数按 `subject_normalized` 折算后再查表。`target_relevance=security_mention_only` 的记录必须排除。\n\n**输出要求**：必须同时给出\"原始条数\"和\"加权条数\"两个数字，便于用户审阅折算逻辑。例如：`窗口 B 命中 6 条（公司 2 条 + 股东 3 条 + 高管 1 条 → 加权 4.0 条），频次等级：高`。\n\n**E2 综合等级合成规则**（解决单条评级与频次叠加的问题）：\n\n```\nE2_单条等级 = 窗口 A 内所有 reason_normalized 单条等级的最大值\nE2_频次等级 = 窗口 B 频次表查得\nE2_综合等级 = max(E2_单条等级, E2_频次等级)\n```\n\n**叠加加成**：若同时满足 `E2_频次等级 ≥ 高` 且 `E2_单条等级 ≥ 高`（含财务造假/虚假陈述/违规担保/占用资金）→ **直升极高**。\n\n**为什么要这条规则**：纯量化红线只能捕捉\"财务类 ST\"，无法预警\"规范类 ST\"。频次规则把\"治理失序\"作为前瞻信号纳入。\n\n**注意**：处罚证据**只抬升风险等级，不影响预测可信度**——这是事实证据，不是预测项。\n\n### E3 交易类临界预警（事实）\n\n调用 `daily`（近 30 个交易日，**必须 `adjust=''` 不复权**）和 `daily_basic`（最新一日，单位：万元）：\n\n**1 元退市预警**（用 `daily.close`，**不复权**）：\n- 高风险：近 20 个交易日中收盘价 < 1 元的天数 ≥ 10\n- 中风险：≥ 1 但 < 10\n- 低风险：0 天\n\n**市值退市预警**（用 `daily_basic.total_mv`，单位万元 → ×1e4 换算成元 → /1e8 换算成亿元）：\n\n| 板块 | 高风险阈值 | 中风险阈值 |\n|---|---|---|\n| 主板 | < 5 亿 | < 5 × 1.5 = 7.5 亿 |\n| 创业板 / 科创板 | < 3 亿 | < 3 × 1.5 = 4.5 亿 |\n\n## 第六步：双轴输出模板\n\n每次分析必须按下面模板输出。**风险等级 + 可信度两个维度都要给**。\n\n```markdown\n## ST 风险预测：{name}（{ts_code}）\n\n### 当前状态\n- 是否已 ST/*ST：{是 / 否}\n- 板块：{主板 / 创业板 / 科创板}\n- 基准预测时点：{forecast / express / 三季报（period） / 中报（period）}\n\n### 量化红线预测（双轴）\n\n| 红线 | 基准情景结论 | 风险等级 | 可信度 | 主要证据 |\n|------|-------------|---------|-------|---------|\n| R1 营收+净利润 | 全年营收预测 X 亿，净利润 Y 亿 | 高/中/低 | 高/中高/中/低 | forecast/三季报/... |\n| R2 年末净资产 | 当前 X 亿，年末预计 Y 亿 | 高/中/低 | 高/中高/中/低 | balancesheet + R1 |\n| R3 分红达标 | 三年累计预计 X，对应阈值 Y | 高/中/低 | 中/低 | dividend + income |\n| R4 连续亏损链 | 过去两年 + 当前年是否均亏 | 高/中/低 | 同 R1 | income + fina_indicator |\n\n### 事实证据面（不参与预测可信度）\n\n| 证据 | 结论 | 影响 |\n|------|------|------|\n| E1 审计意见 | 最新结论 | 抬升/无影响 |\n| E2 监管处罚（窗口 A 上一财年 / 窗口 B 过去 12 个月） | A 命中 Na 条（最高单条等级：…），B 原始 Nb_raw 条 / 加权 Nb_w 条（公司 a + 股东 b + 高管 c → a×1.0+b×0.5+c×0.5），频次等级：…，E2_综合等级：… | 抬升/无影响 |\n| E3 交易类临界 | 收盘价/市值情况 | 抬升/无影响 |\n\n### 综合结论\n- **下一财年被 ST/*ST 风险等级**：{极高 / 高 / 中 / 低}\n- **预测综合可信度**：{高 / 中高 / 中 / 低}\n- **关键风险点**（最多 3 条）：...\n- **缓解信号**（如有）：...\n\n### 备注\n- 数据时点：{所用最新报告期}\n- 已忽略的不可预测项：财务造假、未来审计意见\n- 季节性提醒（如适用）：...\n```\n\n## 禁止事项\n\n1. 不预测财务造假（事前不可知）。\n2. 不预测下一份审计意见（事后才出）。\n3. 不在没有公告依据时擅自计提减值。\n4. 不混用板块阈值（主板 vs 创业板/科创板的营收/市值/分红阈值不同）。\n5. 不把机械年化（H1×2、Q1Q3×4/3）伪装成\"高可信度\"。\n6. 不在港股 / 美股 / 加密 / **北交所**（`8xxxxx.BJ`，监管规则与本 skill 阈值不一致）上启用本 skill。\n7. 不把\"违规处罚记录\"计入预测可信度（仅抬升风险等级）。\n8. 不在 R3 分红计算中使用 `cash_div_tax × base_share`（base_share 经常为 None，会得 0）；必须用 `cash_div_tax / 10 × total_share`，且按 `(end_date, ann_date, cash_div_tax)` 三元组去重。\n9. 不在 R1 触线判定中只用 `n_income`；必须用 `min(n_income, profit_dedt)`，否则会漏掉造壳公司。\n10. 不把 R4 写成\"连续三年\"；监管原文是\"连续两年\"。**且 R4 高风险必须叠加营收联动条件**——仅\"连续两年亏损\"不构成 *ST 财务类强制退市，必须同时满足\"营收 < 板块阈值 × 1.5\"或 R1 已命中高风险，否则只能给中风险。\n11. 不把 E2 监管处罚\"按主体一刀切\"计数——必须用 `subject_normalized` 加权（公司 ×1.0 / 股东 ×0.5 / 董监高 ×0.5），否则股东动荡的公司会被系统性高估。\n\n## 依赖的 tushare 接口清单\n\n| 用途 | 接口 | 备注 |\n|------|------|------|\n| 当前 ST 状态 | `stock_st` | 3000 积分；不可用时回退 `namechange` |\n| 历史改名 | `namechange` | 免费 |\n| 板块判断 | `stock_basic` | 免费 |\n| 利润表 | `income` | 2000 积分，多 period 拉取 |\n| 资产负债表 | `balancesheet` | 2000 积分 |\n| 财务指标（扣非等） | `fina_indicator` | 2000 积分 |\n| 业绩预告 | `forecast` | 2000 积分 |\n| 业绩快报 | `express` | 2000 积分 |\n| 审计意见 | `fina_audit` | 500 积分 |\n| 分红 | `dividend` | 2000 积分 |\n| 日线 | `daily` | 免费 |\n| 每日指标（市值） | `daily_basic` | 免费 |\n| 财报披露日期 | `disclosure_date` | 500 积分 |\n\n## 自带脚本\n\n- [scripts/fetch_sina_penalties.py](scripts/fetch_sina_penalties.py) — 抓取新浪财经 vGP_GetOutOfLine 处罚页，stdlib 实现，无外部依赖；reason / issuer 标准化。\n\n调用方式：\n\n```bash\n# 单次拉全量（推荐：本 skill 双窗口策略由 LLM 在内存中切片）\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --no-filter\n\n# 或指定单一窗口\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --start-date 2025-01-01 --end-date 2025-12-31\n```\n\n正常情况下输出 JSON 到 stdout；网络/解析失败或参数非法时返回 `{\"source\": \"unavailable\", \"error\": \"...\"}` 到 stdout 并以非零退出码结束（不抛 traceback），便于 LLM 兜底处理。\n\n## 端到端调用伪代码\n\n用于减少 LLM 调用顺序漂移，按下列骨架组织：\n\n```python\nimport os, json, subprocess\nimport tushare as ts\n\nts_code = '000729.SZ'\npro = ts.pro_api(os.environ['TUSHARE_TOKEN'])\n\n# === Step 0: M0 当前状态 ===\nbasic = pro.stock_basic(ts_code=ts_code, fields='ts_code,name,market').to_dict('records')[0]\nst_hit = pro.stock_st() # 失败则回退 namechange\nname_chg = pro.namechange(ts_code=ts_code).sort_values('start_date', ascending=False)\n\n# === Step 1: 板块判断 ===\nboard = '科创板' if ts_code.startswith('688') else '创业板' if ts_code.startswith('30') else '主板'\nthreshold_revenue = 1e8 if board != '主板' else 3e8\nthreshold_mv = 3e8 if board != '主板' else 5e8\n\n# === Step 2: 拉财务（最新 5 期 + 过去 4 年年报） ===\nincome = pro.income(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nbalance = pro.balancesheet(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nfina_ind = pro.fina_indicator(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nforecast = pro.forecast(ts_code=ts_code).sort_values('ann_date', ascending=False)\nexpress = pro.express(ts_code=ts_code).sort_values('ann_date', ascending=False)\n\n# === Step 3: R1-R4 计算（按各章节口径，注意 min(n_income, profit_dedt)） ===\n# ... 略\n\n# === Step 4: E1 审计 ===\naudit = pro.fina_audit(ts_code=ts_code).sort_values('end_date', ascending=False).head(2)\n\n# === Step 5: E2 监管处罚（双窗口 + 主体加权）===\nfrom datetime import date, timedelta\nresult = subprocess.run([\n 'python', 'agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py',\n '--ts-code', ts_code, '--stock-name', basic['name'], '--no-filter',\n], capture_output=True, text=True)\npenalties = json.loads(result.stdout).get('records', [])\n\n# 在内存中切双窗口（避免两次 HTTP）\nthis_year = date.today().year\nwin_a_start, win_a_end = f'{this_year-1}-01-01', f'{this_year-1}-12-31'\nwin_b_start = (date.today() - timedelta(days=365)).isoformat()\nwin_b_end = date.today().isoformat()\n\ndef _in(rec, s, e):\n d = rec.get('ann_date') or ''\n return bool(d) and s <= d <= e\n\nwin_a = [r for r in penalties if _in(r, win_a_start, win_a_end)]\nwin_b = [r for r in penalties if _in(r, win_b_start, win_b_end)]\n\n# 窗口 A：单条等级取最高\nREASON_LEVEL = {\n '财务造假': 3, '虚假陈述': 3, '信息披露违规': 3,\n '违规担保': 3, '占用资金': 3,\n '内幕交易': 2, '市场操纵': 2, '违规减持': 2,\n}\nlevels_a = [REASON_LEVEL.get(r['reason_normalized'], 1) for r in win_a]\ne2_single = max(levels_a) if levels_a else 0\n\n# 窗口 B：原始条数 + 主体加权条数\nFREQ_EVENT_TYPES = {'警示', '问讯', '监管关注', '监管函', '警示函'}\nFREQ_ISSUERS = {'上交所', '深交所', '北交所', '证监会', '地方证监局'}\nSUBJECT_WEIGHT = {'company': 1.0, 'shareholder': 0.5, 'officer': 0.5}\n\nfreq_pool = [r for r in win_b\n if r.get('e2_countable', True)\n and (r['event_type'] in FREQ_EVENT_TYPES\n or r['issuer_normalized'] in FREQ_ISSUERS)]\nnb_raw = len(freq_pool)\nnb_weighted = round(sum(SUBJECT_WEIGHT.get(r['subject_normalized'], 1.0)\n for r in freq_pool), 1)\n\n# 频次等级（基于加权条数）\nif nb_weighted >= 5.0: e2_freq = 4 # 极高\nelif nb_weighted >= 3.0: e2_freq = 3 # 高\nelif nb_weighted >= 2.0: e2_freq = 2 # 中\nelse: e2_freq = 1 # 低\n\ne2_overall = max(e2_single, e2_freq)\n# 叠加加成：频次高 + 单条高同时命中 → 直升极高\nif e2_freq >= 3 and e2_single >= 3:\n e2_overall = 4\n\n# === Step 6: E3 交易临界 ===\ndaily = pro.daily(ts_code=ts_code).sort_values('trade_date', ascending=False).head(30)\ndaily_basic = pro.daily_basic(ts_code=ts_code).sort_values('trade_date', ascending=False).head(1).iloc[0]\ntotal_mv_yi = daily_basic['total_mv'] * 1e4 / 1e8 # 万元 → 亿元\n\n# === Step 7: 双轴合成输出 ===\n# 风险等级 = max(R1, R2, R3, R4, E1, E2_综合, E3)\n# 可信度 = 加权平均(R1, R2, R3, R4 各项可信度)，akshare 兜底项降一档\n```\n","agent/src/skills/ccxt/SKILL.md":"---\nname: ccxt\ncategory: data-source\ndescription: CCXT unified crypto exchange library (100+ exchanges). Free public market data. Fallback when OKX is unavailable.\n---\n\n## Overview\n\nCCXT is a unified cryptocurrency exchange trading library supporting 100+ exchanges including Binance, Bybit, OKX, Coinbase, Kraken, and more. Public market data (OHLCV, tickers, order books) requires no API key.\n\n- GitHub: https://github.com/ccxt/ccxt (35k+ stars)\n- Install: `pip install ccxt`\n\n## Quick Start\n\n```python\nimport ccxt\n\nexchange = ccxt.binance({\"enableRateLimit\": True})\n\n# Fetch daily OHLCV\nohlcv = exchange.fetch_ohlcv(\"BTC/USDT\", \"1d\", limit=100)\n# Returns: [[timestamp, open, high, low, close, volume], ...]\n\n# Fetch ticker\nticker = exchange.fetch_ticker(\"ETH/USDT\")\nprint(f\"ETH price: {ticker['last']}\")\n```\n\n## Key Methods\n\n| Method | Description | Returns |\n|--------|-------------|---------|\n| `fetch_ohlcv(symbol, timeframe, since, limit)` | Historical candles | `[[ts, o, h, l, c, v], ...]` |\n| `fetch_ticker(symbol)` | Latest quote | `{last, bid, ask, volume, ...}` |\n| `fetch_tickers(symbols)` | Batch quotes | `{symbol: ticker}` |\n| `fetch_order_book(symbol, limit)` | Order book | `{bids, asks, timestamp}` |\n| `fetch_trades(symbol, since, limit)` | Recent trades | `[{price, amount, side, timestamp}, ...]` |\n\n## Timeframes\n\n`1m`, `3m`, `5m`, `15m`, `30m`, `1h`, `2h`, `4h`, `6h`, `12h`, `1d`, `1w`, `1M`\n\nNote: not all exchanges support all timeframes. Use `exchange.timeframes` to check.\n\n## Symbol Format\n\nCCXT uses slash format: `BTC/USDT`, `ETH/BTC`, `SOL/USDT`\n\nThe project's DataLoader automatically converts `BTC-USDT` (hyphen) to `BTC/USDT` (slash).\n\n## Exchange Selection\n\nSet via environment variable: `CCXT_EXCHANGE=binance` (default)\n\nPopular exchanges: `binance`, `bybit`, `okx`, `coinbase`, `kraken`, `bitget`, `gate`\n\n## Built-in Loader\n\nThe project has a built-in CCXT DataLoader at `backtest/loaders/ccxt_loader.py`. It serves as a fallback when the OKX loader is unavailable.\n\n## Pagination\n\nFor long history, CCXT paginates via the `since` parameter (millisecond timestamp). The built-in loader handles this automatically (up to 200 pages).\n","agent/src/skills/candlestick/SKILL.md":"---\nname: candlestick\ndescription: Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores.\ncategory: strategy\n---\n# Candlestick Pattern Recognition\n\n## Purpose\n\nIdentifies 15 classic candlestick patterns and generates trading signals:\n\n### Single-Candle Patterns (5)\n| Pattern | Signal | Description |\n|------|------|------|\n| Hammer | Bullish | Long lower shadow with a small body at the top |\n| Inverted Hammer | Bullish | Long upper shadow with a small body at the bottom |\n| Shooting Star | Bearish | Long upper shadow with a small body at the bottom (appears after an uptrend) |\n| Doji | Neutral | Open and close are nearly equal |\n| Spinning Top | Neutral | Small body with roughly equal upper and lower shadows |\n\n### Double-Candle Patterns (5)\n| Pattern | Signal |\n|------|------|\n| Bullish Engulfing | Bullish |\n| Bearish Engulfing | Bearish |\n| Bullish Harami | Bullish |\n| Bearish Harami | Bearish |\n| Piercing Line | Bullish |\n| Dark Cloud Cover | Bearish |\n\n### Triple-Candle Patterns (4)\n| Pattern | Signal |\n|------|------|\n| Morning Star | Bullish |\n| Evening Star | Bearish |\n| Three White Soldiers | Bullish |\n| Three Black Crows | Bearish |\n\n## Signal Logic\n\nBullish patterns score +1, bearish patterns score -1. Go long when the total score is > 0, go short when it is < 0, and stand aside when it equals 0.\n\n## Parameters\n\n| Parameter | Default | Description |\n|------|--------|------|\n| body_pct | 0.1 | Threshold for body-to-range ratio in a doji |\n| shadow_ratio | 2.0 | Ratio of shadow length to body length |\n\n## Dependencies\n\n```bash\npip install pandas numpy requests\n```\n\n## Signal Convention\n\n- `1` = long, `-1` = short, `0` = stand aside\n","agent/src/skills/chanlun/SKILL.md":"---\nname: chanlun\ndescription: 基于缠论（缠中说禅）的形态识别引擎，使用czsc库自动检测K线分型、笔、中枢，并生成一买/一卖/二买/二卖/三买/三卖等买卖点信号。支持多周期分析和形态分类（3/5/7/9/11笔形态）。\ncategory: strategy\n---\n# 缠论形态识别\n\n## 用途\n\n基于**缠中说禅**理论的价格形态识别。缠论是一套完全基于价格结构的技术分析方法，核心链路：\n\n```\n原始K线 → 去包含处理 → 分型识别 → 笔检测 → 中枢构建 → 买卖点判定\n```\n\n适用于任何有 OHLCV 数据的市场（A股、加密货币、期货等）。\n\n## 核心概念\n\n| 概念 | 说明 | 详细文档 |\n| --- | --- | --- |\n| 分型（FX） | 顶分型：中间K线最高；底分型：中间K线最低 | [分型](references/核心概念/分型.md) |\n| 笔（BI） | 相邻顶底分型之间的一段走势，最小单元 | [笔](references/核心概念/笔.md) |\n| 中枢（ZS） | 至少3笔构成的价格重叠区域，趋势的核心 | [中枢](references/核心概念/中枢.md) |\n\n## 买卖点体系\n\n| 买卖点 | 含义 | 详细文档 |\n| --- | --- | --- |\n| 一买/一卖 | 趋势结束后的第一个反转信号（背驰点） | [一买一卖](references/买卖点/一买一卖.md) |\n| 二买/二卖 | 一买/一卖后回调不破底/顶的确认信号 | [二买二卖](references/买卖点/二买二卖.md) |\n| 三买/三卖 | 中枢上移/下移后回调不进入前中枢的信号 | [三买三卖](references/买卖点/三买三卖.md) |\n\n## 依赖安装\n\n```bash\npip install czsc requests pandas\n```\n\n## 快速上手\n\n```python\nfrom czsc import CZSC, RawBar, Freq\nfrom datetime import datetime\n\n# 准备 RawBar 列表（需按时间正序排列）\nbars = [\n RawBar(symbol=\"BTC-USDT\", id=0, dt=datetime(2026,1,1),\n freq=Freq.D, open=70000, close=71000,\n high=72000, low=69000, vol=1000, amount=71000000),\n # ... 更多K线\n]\n\n# 创建分析器，自动检测分型/笔/中枢\nc = CZSC(bars)\n\n# 访问结果\nprint(c.bi_list) # 已完成的笔\nprint(c.bars_ubi) # 未完成笔中的K线\n```\n\n## 可用信号函数（czsc.signals.cxt）\n\nczsc 内置 43 个缠论信号函数，核心如下：\n\n| 函数 | 说明 | 类型 |\n| --- | --- | --- |\n| `cxt_first_buy_V221126` | 一买信号 | 买卖点 |\n| `cxt_first_sell_V221126` | 一卖信号 | 买卖点 |\n| `cxt_second_bs_V230320` | 均线辅助二买二卖 | 买卖点 |\n| `cxt_third_bs_V230318` | 均线辅助三买三卖 | 买卖点 |\n| `cxt_third_buy_V230228` | 笔三买辅助 | 买卖点 |\n| `cxt_double_zs_V230311` | 两中枢组合判断BS1 | 中枢 |\n| `cxt_three_bi_V230618` | 三笔形态分类 | 形态 |\n| `cxt_five_bi_V230619` | 五笔形态分类 | 形态 |\n| `cxt_seven_bi_V230620` | 七笔形态分类 | 形态 |\n| `cxt_nine_bi_V230621` | 九笔形态分类 | 形态 |\n| `cxt_eleven_bi_V230622` | 十一笔形态分类 | 形态 |\n| `cxt_bi_base_V230228` | BI基础信号（方向/转折） | 基础 |\n| `cxt_bi_end_V230312` | MACD辅助笔结束 | 辅助 |\n| `cxt_range_oscillation_V230620` | 区间震荡判断 | 辅助 |\n| `cxt_zhong_shu_gong_zhen_V221221` | 大小级别中枢共振 | 中枢 |\n\n## 信号约定\n\n- 信号引擎输出：`1`=做多，`-1`=做空，`0`=观望\n- 做多条件：一买信号 或 三笔向上盘背 或 五笔类一买\n- 做空条件：一卖信号 或 三笔向下盘背 或 五笔类一卖\n- 中枢辅助：价格在中枢下沿附近做多优势，上沿附近做空优势\n\n## 数据格式\n\nczsc 接受 `List[RawBar]`，每个 RawBar 包含：\n\n| 字段 | 类型 | 说明 |\n| --- | --- | --- |\n| symbol | str | 标的代码 |\n| id | int | 序号（从0开始） |\n| dt | datetime | 时间 |\n| freq | Freq | 频率：`Freq.F1/F5/F15/F30/F60/D/W/M` |\n| open | float | 开盘价 |\n| close | float | 收盘价 |\n| high | float | 最高价 |\n| low | float | 最低价 |\n| vol | float | 成交量 |\n| amount | float | 成交额 |\n\n## 实现方式\n\n使用 [czsc](https://github.com/waditu/czsc) 库（v0.9.68+），基于纯 Python 实现（可选 Rust 加速后端）。支持增量更新，适合实时分析。\n","agent/src/skills/bottleneck-hunter/SKILL.md":"---\nname: bottleneck-hunter\ncategory: strategy\ndescription: \"Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.\"\n---\n\n# Supply-Chain Bottleneck Hunter\n\nDecompose a super-trend (user-specified, e.g. \"AI infrastructure\", \"energy transition\") into its physical supply chain and hunt for bottleneck-arbitrage opportunities.\n\n## Core Idea\n\nDon't ask \"which AI stock to buy\" — ask \"if this trend keeps expanding, which link runs out first?\"\n\nTraditional research chases leaders and known tracks. This skill inverts: **start from the choke points of the physical supply chain and find companies nobody notices but that the whole industry must wait on when they run short.**\n\nThe edge: Layer-1 bottlenecks (GPU, HBM, power) are already priced in. The real alpha is in **Layer 2 and Layer 3** — optical modules, lasers, InP substrates, SOI wafers, epitaxy equipment, wafer-level test, IC substrates, specialty fiberglass.\n\n## Step 1: Super-Trend Confirmation\n\n### Trend Filter Criteria\n\n| Criterion | Requirement | How to verify |\n|-----------|-------------|---------------|\n| Durability | ≥3-5 years of certain growth | Search industry forecasts, capex plans |\n| Physicality | Needs real hardware/material/equipment build | Distinguish \"software upgrade\" from \"physical expansion\" |\n| Scale | Global capex >$50B/year | Search top players' capex guidance |\n| Acceleration | Demand growth > supply expansion | Compare demand growth vs capacity plans |\n\nUse `web_search` to verify. Reference super-trends: AI infrastructure, energy transition (nuclear/grid/storage), defense modernization, semiconductor reshoring, space economy.\n\n## Step 2: Physical Supply-Chain Decomposition\n\nDon't stop at concepts — decompose to physical entities.\n\n```\nLayer 0 (end): final product/service\nLayer 1 (core component): already closely watched → priced in, limited alpha\nLayer 2 (sub-component/material): low attention, alpha-rich\nLayer 3 (upstream equipment/raw material)\nLayer 4 (infrastructure): power, cooling, land, talent, certifications\n```\n\n### AI Infrastructure Example\n\n```\nLayer 0: AI model training/inference services\nLayer 1: GPU/accelerators, HBM, servers, data centers\nLayer 2 (focus zone):\n - Network interconnect: optical modules, fiber, switch ASICs, copper cables\n - Optical comms core: lasers (EML/VCSEL/CW), modulators, photodetectors\n - Semiconductor materials: InP substrates, GaAs substrates, SOI wafers, SiC substrates\n - Advanced packaging: CoWoS interposers, HBM TSV, ABF substrate film\n - PCB/substrate: high-frequency PCB, IC substrates, specialty fiberglass\n - Test: wafer-level test (probe cards), burn-in, ATE\n - Thermal/cooling: liquid cooling, CDU, immersion fluid\n - Power connection: busbars, UPS, distribution, transformers\nLayer 3: epitaxy equipment (MOCVD/MBE), lithography/etch, high-purity metals (In/Ga/Ge), specialty gases, sputtering targets, certifications (MSA/Telcordia)\nLayer 4: power (nuclear/gas/transmission), cooling water, data-center land/permits\n```\n\nFor other trends, use `web_search` with queries like `{trend} supply chain bottleneck`, `{trend} shortage critical component`, `{trend} capacity constraint`, `{trend} sole source supplier`.\n\n## Step 3: Bottleneck Identification — Finding \"Choke Points\"\n\nFor each Layer 2-3 link, evaluate 6 criteria:\n\n| # | Criterion | Question | Score |\n|---|-----------|----------|-------|\n| 1 | Supply concentration | ≤3 global suppliers? | 🔴 ≤2 / 🟡 3-5 / 🟢 >5 |\n| 2 | Expansion lead time | How long to add capacity? | 🔴 >2y / 🟡 1-2y / 🟢 <1y |\n| 3 | Substitutability | Can other tech/material replace it? | 🔴 irreplaceable / 🟡 partial / 🟢 easy |\n| 4 | Capacity utilization | Current utilization? | 🔴 >90% / 🟡 70-90% / 🟢 <70% |\n| 5 | Demand growth | Downstream demand growth? | 🔴 >50%/yr / 🟡 20-50% / 🟢 <20% |\n| 6 | Customer qualification cycle | How long for a new supplier to qualify? | 🔴 >1y / 🟡 6-12m / 🟢 <6m |\n\n**Bottleneck grade**: 🔴×≥4 → **S-grade** (single-point failure, highest priority); 🔴×3 → **A-grade** (severely constrained); 🔴×1-2 → **B-grade** (stressed but manageable); no 🔴 → not a bottleneck, skip.\n\n## Step 4: Company Screening — From Bottleneck to Tickers\n\nFor each S/A-grade bottleneck, use `web_search` / `screen_market` to find listed companies.\n\n### Initial Screen\n\n| Criterion | Requirement |\n|-----------|-------------|\n| Listing status | Listed (A/HK/US/JP/TW/EU) |\n| Bottleneck revenue share | >30% of revenue from the bottleneck link |\n| Market cap | Prefer <$10B (large caps already priced) |\n| Liquidity | Average daily turnover >$1M |\n\n### Valuation Gate (mandatory, never skip)\n\n**A real bottleneck ≠ an investment opportunity.** For every company, compute PE/PB/ROE/FCF yield with `financial_rigor` (`command=verify_valuation`), and run `financial_rigor` (`command=three_scenario`) for scenario valuation:\n\n- **Red light** (any one → signal strength capped at ★★, flag \"valuation stretched\"): market cap >20% of TAM; PS>30x with revenue growth <100%; market cap >10× 5-year optimistic revenue forecast; stock doubled within 60 days of a follow-on offering.\n- **Yellow light** (needs extra justification, else downgrade): loss-making + PS>15x; PS >5× a profitable peer; PE>80x (compute PEG).\n- **Green light** (bonus): PS<10x with revenue growing; PE<30x with a moat (flag \"margin of safety\").\n\n**Sanity check (mandatory)**: with `financial_rigor` (`command=three_scenario`), answer — \"buying at current market cap, if the most optimistic scenario fully plays out and I exit at 25× PE in 10 years, what's the annualized return?\" <10%/yr → flag \"no margin of safety at current price\".\n\n## Step 5: Cross-Validation — Don't Trust a Single Story\n\n### Positive checks\n\n| Check | Question |\n|-------|----------|\n| Customer validation | Have top customers signed/imported? (check announcements, customer filings) |\n| Revenue validation | Is the bottleneck already showing in revenue growth? (last 2-3 quarters) |\n| Price validation | Is the product raising price? (industry quotes, analyst reports) |\n| Capacity validation | Is capacity really tight? (lead times, customer complaints) |\n| Capital validation | Is there expansion capex? (company guidance) |\n\nUse `get_financial_statements` / `get_stock_news` / `web_search`.\n\n### Reverse checks (Munger inversion)\n\n- Why don't smart people buy this stock?\n- Can the bottleneck be bypassed? Alternative routes?\n- Can China / other players quickly replicate capacity?\n- If end-demand drops 50%, what happens to this company?\n- Has management diluted at highs before?\n- What growth assumption does the current valuation imply?\n\n## Step 6: Output — Bottleneck Opportunity Board\n\n### Ranking Table\n\n| Rank | Company | Ticker | Mkt Cap | Revenue | PS | PE | Bottleneck link | Grade | Share | Growth | Signal | Valuation |\n|------|---------|--------|---------|---------|-----|-----|-----------------|-------|-------|--------|--------|-----------|\n\n**Market cap, revenue, PS, PE are mandatory** — never skip with \"TBD\". If financials can't be obtained, signal strength ≤★★.\n\nSignal strength (valuation gate directly affects):\n- ★★★★★ multi-cross-validated + customers imported + revenue confirmed + valuation green\n- ★★★★ most checks pass + valuation green/yellow (with explanation)\n- ★★★ logic holds but parts unverified + valuation yellow acceptable\n- ★★ early signal, or logic holds but valuation red\n- ★ pure concept, unverified\n\nAfter drafting, run `report_audit` (`command=extract` → verify each point → `command=verdict`) as a quality gate to ensure no hallucinated numbers.\n\n### One-Pager Template\n\n```\n🎯 {Company} ({Ticker}) — {one-line bottleneck positioning}\n\nWhy it's a bottleneck: (2-3 sentences)\nWhy this company: (2-3 sentences)\n\nCatalyst timeline:\n- Near-term (1-3m): [earnings / capacity / customer win]\n- Mid-term (3-12m): [industry trend / expansion node]\n\nKey risks: 1. 2.\n\nKey data: market cap / revenue / PS / PE / growth / bottleneck revenue share\nMargin of safety: 10y 25× PE exit method, annualized return XX%. Conclusion: yes/no.\n\nCross-validation status: ✅ customer / ✅ revenue / ⚠️ valuation stretched / ❌ unverified\n\nConclusion: deep research / watchlist / skip\n```\n\nSave with `write_file` to the reports directory (e.g. `reports/bottleneck-map/{trend}-bottleneck-{YYYYMMDD}.md`).\n\n## Step 7: Inventory Update — Maintain the Bottleneck Map\n\nOn each run: ① re-check identified bottlenecks (new suppliers? capacity expanded? substitute breakthrough?); ② scan new bottlenecks (`web_search` last 7 days supply chain / shortage / bottleneck news); ③ update grades (upgrade/downgrade/relieve).\n\n## AI Research Bias Self-Check\n\n| Bias | Symptom | Counter |\n|------|---------|---------|\n| Leader-bias | Search dominated by large caps | Deliberately search small-cap suppliers, add \"small cap\" |\n| English-bias | Miss JP/KR/TW players | Must search JP/KR/TW market suppliers |\n| Narrative-bias | Drawn to \"AI concept\" labels | Look only at actual supply-chain position, not market labels |\n| Confirmation-bias | After finding a bottleneck, only seek positive evidence | Force Step 5 reverse checks |\n| Recency-bias | Rely on stale info | Prefer last 30 days of data |\n\n## Core Principles (highest priority)\n\n1. **Don't ask the AI to recommend stocks — ask it to decompose supply chains.** The question matters more than the answer.\n2. **Physical first** — only links that need real physical product/material/equipment.\n3. **Layer 2 and Layer 3** — don't chase already-priced leaders.\n4. **Cross-validate** — every conclusion needs ≥2 independent sources.\n5. **Be honest about uncertainty** — if data is missing, say so; don't fill with speculation.\n6. **Bottlenecks are temporary** — every bottleneck gets resolved; the key is timing the window.\n7. **Small cap ≠ good opportunity** — a small cap can also be a bad company; it must pass financial quality.\n8. **A real bottleneck ≠ an investment** — at PS>30x or still loss-making, the current price is not a buy. **Valuation is a hard gate that cannot be overridden by bottleneck purity, signal strength, or narrative appeal.** Better to miss a bottleneck stock that ran than buy a loss-making company at 100× sales.\n","agent/src/skills/behavioral-finance/SKILL.md":"---\nname: behavioral-finance\ndescription: \"Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.\"\ncategory: analysis\n---\n\n# Behavioral Finance Applications\n\n## Overview\n\nTranslate behavioral-finance theory into quantifiable trading signals and risk-control rules. Core assumption: market participants systematically deviate from rational decision-making, and these biases can be predicted and exploited.\n\nApplicable scenarios:\n- Behavioral interpretation and parameter optimization for momentum / reversal strategies\n- Contrarian signals when market sentiment becomes extreme\n- Debiasing mechanisms in portfolio construction\n- Capturing behavior patterns specific to retail-driven China A-share markets\n\n## Core Concepts\n\n### Overreaction and Underreaction\n\n**Underreaction** → momentum effect:\n```\nMechanism: anchoring bias + conservatism\n Investors anchor on old information and update insufficiently to new information\n After an earnings beat, the stock price digests it gradually rather than all at once\nChina A-share evidence:\n - Earnings-guidance beats still produce 3-5% excess return over the following 20 days\n - After analyst rating upgrades, momentum often persists for 1-3 months\nQuant signal:\n SUE (standardized unexpected earnings) > 2σ -> buy and hold for 60 days\n Top 10% 20-day return -> continue holding for 20 days (China A-share momentum cycles are shorter)\n```\n\n**Overreaction** → reversal effect:\n```\nMechanism: representativeness heuristic + availability bias\n Investors extrapolate recent trends too aggressively and ignore mean reversion\n Panic / euphoria drives reactions beyond what fundamentals support\nChina A-share evidence:\n - Rebounds after consecutive limit-downs (after 3 limit-downs, the average 20-day rebound is 8%)\n - Big annual losers often earn 5-10% excess return the next year\nQuant signal:\n Bottom 10% of 250-day return -> buy and hold for 250 days\n RSI(5) < 10 -> short-term rebound signal (5-10 days)\n```\n\n**Key distinction**:\n| Dimension | Underreaction (Momentum) | Overreaction (Reversal) |\n|------|------------------|------------------|\n| Time scale | 1-12 months | <1 week or >12 months |\n| Information type | Clear events (earnings / announcements) | Ambiguous information (sentiment / trend) |\n| Best China A-share window | 20-60 days | 5-10 days (short term) / 1 year (long term) |\n\n### Cognitive Bias Checklist\n\n**Individual decision biases**:\n| Bias | Manifestation | Quant Detection | Debiasing Strategy |\n|------|------|----------|------------|\n| Loss aversion | Hold losing stocks, sell winners too early | Holding period: losing positions > winning positions by 2-3x | Pre-set stop-loss line and execute mechanically |\n| Overconfidence | Overtrading, concentrated positions | Monthly turnover > 100%, single-stock weight > 30% | Limit the number of trades per month |\n| Anchoring effect | Anchoring to entry price / historical highs | Abnormal volume expansion near the entry price | Use relative valuation instead of absolute price |\n| Confirmation bias | Focus only on information that supports the existing view | Single-source information, ignoring bearish news | Force reading the opposing view |\n| Recency bias | Overweight recent events | Recent gains/losses have too much influence on position size | Lengthen the evaluation window (≥60 days) |\n| Framing effect | Same information framed differently leads to different decisions | Decision differences between return format and absolute-PnL format | Evaluate consistently in return space |\n\n**Group behavior biases**:\n| Bias | Manifestation | China A-share Characteristics | Quant Indicator |\n|------|------|---------|----------|\n| Herding | Chasing rallies and panic-selling together | Extremely fast sector rotation (3-5 days) | Intra-sector stock correlation > 0.8 |\n| Information cascades | Ignoring private information and following public signals | Sector follow-through after a leader stock hits limit-up | Sector return on the day after leader-stock limit-up |\n| Attention effect | Buying stocks that attract attention | Explosive turnover in limit-up / news-driven stocks | Abnormal turnover > 3x average |\n\n### Investor Sentiment Cycle\n\n```\nFear -> Caution -> Optimism -> Excitement -> Euphoria -> Denial -> Panic -> Fear\n | | | | | | |\n Bottom Recovery Mid-uptrend Pre-top Top Early selloff Pre-bottom\n\nQuant sentiment indicators:\n 1. Closed-end fund discount: discount > 15% -> extreme fear -> buy signal\n 2. Margin-financing growth: monthly growth > 20% -> euphoria -> reduce position\n 3. New account openings: weekly openings > 2x average -> overheated market\n 4. Turnover ratio: All-A daily turnover > 3% -> euphoric; < 0.5% -> deeply depressed\n 5. Number of limit-up stocks: > 100 -> euphoric; < 10 -> weak\n```\n\n## Analysis Framework\n\n### 1. Disposition-Effect Signal\n\n**Principle**: investors tend to sell winners and hold losers. Once winning positions are largely cleared, selling pressure eases; when trapped holders are deeply underwater, selling pressure can also ease.\n\n```\nChina A-share application:\n Compute the profit ratio in the chip-distribution structure:\n - Profit ratio > 90% and shrinking volume -> winners are reluctant to sell -> may continue rising\n - Profit ratio > 90% and expanding volume -> winners are exiting -> topping signal\n - Profit ratio < 10% and shrinking volume -> low willingness to cut losses -> bottom stabilization\n - Profit ratio < 10% and expanding volume -> panic selling -> short-term oversold\n\nQuant implementation:\n capital_gain_overhang = (current_price - avg_cost) / avg_cost\n where avg_cost is approximated by 60-day VWAP\n CGO > 0.2 -> strong unrealized gains, watch for disposition-effect selling pressure\n CGO < -0.3 -> deeply trapped holders, selling pressure may actually ease\n```\n\n### 2. Composite Sentiment Indicator\n\n```python\n# Multi-dimensional sentiment score (0-100, 50 = neutral)\nsentiment_components = {\n 'turnover_ratio': normalize(all_a_turnover, historical_percentile), # weight 25%\n 'margin_growth': normalize(monthly_margin_growth, historical_percentile), # weight 25%\n 'new_high_ratio': normalize(new_high_ratio, historical_percentile), # weight 20%\n 'limit_up_count': normalize(limit_up_count, historical_percentile), # weight 15%\n 'fund_discount': normalize(closed_end_fund_discount, historical_percentile), # weight 15% (inverse)\n}\n\nsentiment_score = weighted_sum(components)\n# > 80: extreme greed -> cut exposure below 60%\n# 60-80: optimistic -> maintain normal exposure\n# 40-60: neutral -> keep exposure unchanged\n# 20-40: pessimistic -> add gradually\n# < 20: extreme fear -> increase exposure above 80%\n```\n\n### 3. Behavioral Optimization of Momentum Strategies\n\nTraditional momentum (sorting by past 12-month returns) is unstable in China A-shares. A behavioral-finance perspective suggests the following optimizations:\n\n```\nOptimization 1: Separate sentiment momentum from fundamental momentum\n Sentiment momentum = part of recent price rise with no fundamental support -> short-term reversal\n Fundamental momentum = price rise consistent with earnings revisions -> can persist\n Trade: buy stocks with \"strong fundamental momentum + weak sentiment momentum\"\n\nOptimization 2: Attention-weighted momentum\n High-attention retail names reverse faster\n Indicator: if abnormal turnover > 3x average, cut momentum holding period by 50%\n Example: if a normal momentum basket holds for 60 days, high-attention names hold only 30 days\n\nOptimization 3: Combine cross-sectional momentum and time-series momentum\n Cross-sectional: relative strength (top 20% in return ranking)\n Time-series: absolute trend (price > MA60)\n Both satisfied -> strong signal; only one satisfied -> half position\n```\n\n### 4. Contrarian Trading Signals\n\n```\nExtreme-fear buy conditions (at least 3 items):\n □ Shanghai Composite RSI(5) < 15\n □ All-A daily turnover < 0.5%\n □ Weekly margin-financing decline > 5%\n □ Limit-up count < 10 and limit-down count > 50\n □ Closed-end fund discount > 15%\n\nExtreme-greed sell conditions (at least 3 items):\n □ Shanghai Composite RSI(5) > 90\n □ All-A daily turnover > 3%\n □ Weekly margin-financing growth > 10%\n □ Limit-up count > 150\n □ Weekly increase in new account openings > 100%\n```\n\n## Output Format\n\nBehavioral-finance analysis report:\n```\n=== Market Sentiment Diagnosis ===\nDate: 2026-03-28\nSentiment score: 72/100 (optimistic bias)\nCurrent phase: transition from optimism to excitement\n\n=== Behavioral-Bias Signals ===\nOverreaction detection: 127 stocks rose > 15% in the past 5 days -> 65% probability of short-term reversal\nDisposition effect: winner-clearing ratio is low (35%) -> overhead selling pressure remains\nHerding effect: sector correlation 0.85 -> severe follow-the-leader behavior, divergence likely soon\n\n=== Strategy Recommendations ===\nMomentum strategy: shorten holding period from 60 days to 30 days (market attention is elevated)\nContrarian signal: not triggered (sentiment is not yet extreme)\nPosition suggestion: maintain 70% exposure, and prioritize names with \"strong fundamental momentum + weak sentiment momentum\"\n\n=== Debiasing Checklist ===\n□ Are you overconfident because of recent profits? -> check position concentration\n□ Are you anchored to your entry price? -> re-evaluate using current PE/PB\n□ Are you ignoring bearish information? -> force yourself to read bearish research reports\n```\n\n## Notes\n\n1. **High retail participation in China A-shares**: behavioral-bias signals are more pronounced than in US equities, but sector rotation is also faster, so momentum windows should be shorter\n2. **Lag in sentiment indicators**: margin-financing balance is released T+1, and new account openings are weekly, so they are not suitable for intraday trading\n3. **Structural changes**: after 2019, foreign capital and quant participation rose, so the effectiveness of traditional behavioral-finance signals may have weakened\n4. **Behavioral factors correlate with traditional factors**: disposition-effect factors correlate about 0.3-0.5 with momentum, so control collinearity\n5. **Overfitting risk**: behavioral stories are easy to explain after the fact, so out-of-sample validation is mandatory\n6. **Extreme sentiment is rare**: extreme fear / greed appears only 2-3 times per year, so strategy capacity is limited\n\n## Dependencies\n\n```bash\npip install pandas numpy scipy\n```\n","agent/src/skills/commodity-analysis/SKILL.md":"# commodity-analysis\n\nPath: `agent/src/skills/commodity-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/commodity-analysis/SKILL.md)","agent/src/skills/convertible-bond/SKILL.md":"# convertible-bond\n\nPath: `agent/src/skills/convertible-bond/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/convertible-bond/SKILL.md)","agent/src/skills/corporate-events/SKILL.md":"# corporate-events\n\nPath: `agent/src/skills/corporate-events/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/corporate-events/SKILL.md)","agent/src/skills/correlation-analysis/SKILL.md":"# correlation-analysis\n\nPath: `agent/src/skills/correlation-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-analysis/SKILL.md)","agent/src/skills/correlation-regime/SKILL.md":"# correlation-regime\n\nPath: `agent/src/skills/correlation-regime/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-regime/SKILL.md)","agent/src/skills/credit-analysis/SKILL.md":"# credit-analysis\n\nPath: `agent/src/skills/credit-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/credit-analysis/SKILL.md)","agent/src/skills/cross-market-strategy/SKILL.md":"# cross-market-strategy\n\nPath: `agent/src/skills/cross-market-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/cross-market-strategy/SKILL.md)","agent/src/skills/crypto-derivatives/SKILL.md":"# crypto-derivatives\n\nPath: `agent/src/skills/crypto-derivatives/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/crypto-derivatives/SKILL.md)","agent/src/skills/data-routing/SKILL.md":"# data-routing\n\nPath: `agent/src/skills/data-routing/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/data-routing/SKILL.md)","agent/src/skills/deep-company-series/SKILL.md":"# deep-company-series\n\nPath: `agent/src/skills/deep-company-series/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/deep-company-series/SKILL.md)","agent/src/skills/defi-yield/SKILL.md":"# defi-yield\n\nPath: `agent/src/skills/defi-yield/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/defi-yield/SKILL.md)","agent/src/skills/dividend-analysis/SKILL.md":"# dividend-analysis\n\nPath: `agent/src/skills/dividend-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/dividend-analysis/SKILL.md)","agent/src/skills/doc-reader/SKILL.md":"# doc-reader\n\nPath: `agent/src/skills/doc-reader/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/doc-reader/SKILL.md)","agent/src/skills/earnings-forecast/SKILL.md":"# earnings-forecast\n\nPath: `agent/src/skills/earnings-forecast/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/earnings-forecast/SKILL.md)","agent/src/skills/earnings-revision/SKILL.md":"# earnings-revision\n\nPath: `agent/src/skills/earnings-revision/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/earnings-revision/SKILL.md)","agent/src/skills/eastmoney/SKILL.md":"# eastmoney\n\nPath: `agent/src/skills/eastmoney/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/SKILL.md)","agent/src/skills/edgar-sec-filings/SKILL.md":"# edgar-sec-filings\n\nPath: `agent/src/skills/edgar-sec-filings/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/edgar-sec-filings/SKILL.md)","agent/src/skills/elliott-wave/SKILL.md":"# elliott-wave\n\nPath: `agent/src/skills/elliott-wave/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/SKILL.md)","agent/src/skills/etf-analysis/SKILL.md":"# etf-analysis\n\nPath: `agent/src/skills/etf-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/etf-analysis/SKILL.md)","agent/src/skills/event-driven/SKILL.md":"# event-driven\n\nPath: `agent/src/skills/event-driven/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/event-driven/SKILL.md)","agent/src/skills/execution-model/SKILL.md":"# execution-model\n\nPath: `agent/src/skills/execution-model/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/execution-model/SKILL.md)","agent/src/skills/factor-research/SKILL.md":"# factor-research\n\nPath: `agent/src/skills/factor-research/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/SKILL.md)","agent/src/skills/financial-statement/SKILL.md":"# financial-statement\n\nPath: `agent/src/skills/financial-statement/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/financial-statement/SKILL.md)","agent/src/skills/fund-analysis/SKILL.md":"# fund-analysis\n\nPath: `agent/src/skills/fund-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/fund-analysis/SKILL.md)","agent/src/skills/fundamental-filter/SKILL.md":"# fundamental-filter\n\nPath: `agent/src/skills/fundamental-filter/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/fundamental-filter/SKILL.md)","agent/src/skills/geopolitical-risk/SKILL.md":"# geopolitical-risk\n\nPath: `agent/src/skills/geopolitical-risk/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/geopolitical-risk/SKILL.md)","agent/src/skills/global-macro/SKILL.md":"# global-macro\n\nPath: `agent/src/skills/global-macro/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/global-macro/SKILL.md)","agent/src/skills/harmonic/SKILL.md":"# harmonic\n\nPath: `agent/src/skills/harmonic/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/SKILL.md)","agent/src/skills/hedging-strategy/SKILL.md":"# hedging-strategy\n\nPath: `agent/src/skills/hedging-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/hedging-strategy/SKILL.md)","agent/src/skills/hk-connect-flow/SKILL.md":"# hk-connect-flow\n\nPath: `agent/src/skills/hk-connect-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/hk-connect-flow/SKILL.md)","agent/src/skills/ichimoku/SKILL.md":"# ichimoku\n\nPath: `agent/src/skills/ichimoku/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/SKILL.md)","agent/src/skills/investor-lenses/SKILL.md":"# investor-lenses\n\nPath: `agent/src/skills/investor-lenses/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/SKILL.md)","agent/src/skills/liquidation-heatmap/SKILL.md":"# liquidation-heatmap\n\nPath: `agent/src/skills/liquidation-heatmap/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/liquidation-heatmap/SKILL.md)","agent/src/skills/macro-analysis/SKILL.md":"# macro-analysis\n\nPath: `agent/src/skills/macro-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/macro-analysis/SKILL.md)","agent/src/skills/management-deep-dive/SKILL.md":"# management-deep-dive\n\nPath: `agent/src/skills/management-deep-dive/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/management-deep-dive/SKILL.md)","agent/src/skills/market-microstructure/SKILL.md":"# market-microstructure\n\nPath: `agent/src/skills/market-microstructure/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/market-microstructure/SKILL.md)","agent/src/skills/minute-analysis/SKILL.md":"# minute-analysis\n\nPath: `agent/src/skills/minute-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/minute-analysis/SKILL.md)","agent/src/skills/ml-strategy/SKILL.md":"# ml-strategy\n\nPath: `agent/src/skills/ml-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ml-strategy/SKILL.md)","agent/src/skills/mootdx/SKILL.md":"# mootdx\n\nPath: `agent/src/skills/mootdx/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/mootdx/SKILL.md)","agent/src/skills/multi-factor/SKILL.md":"# multi-factor\n\nPath: `agent/src/skills/multi-factor/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/multi-factor/SKILL.md)","agent/src/skills/okx-market/SKILL.md":"# okx-market\n\nPath: `agent/src/skills/okx-market/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/SKILL.md)","agent/src/skills/onchain-analysis/SKILL.md":"# onchain-analysis\n\nPath: `agent/src/skills/onchain-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/onchain-analysis/SKILL.md)","agent/src/skills/options-advanced/SKILL.md":"# options-advanced\n\nPath: `agent/src/skills/options-advanced/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-advanced/SKILL.md)","agent/src/skills/options-payoff/SKILL.md":"# options-payoff\n\nPath: `agent/src/skills/options-payoff/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-payoff/SKILL.md)","agent/src/skills/options-strategy/SKILL.md":"# options-strategy\n\nPath: `agent/src/skills/options-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-strategy/SKILL.md)","agent/src/skills/pair-trading/SKILL.md":"# pair-trading\n\nPath: `agent/src/skills/pair-trading/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/pair-trading/SKILL.md)","agent/src/skills/performance-attribution/SKILL.md":"# performance-attribution\n\nPath: `agent/src/skills/performance-attribution/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/performance-attribution/SKILL.md)","agent/src/skills/perp-funding-basis/SKILL.md":"# perp-funding-basis\n\nPath: `agent/src/skills/perp-funding-basis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/perp-funding-basis/SKILL.md)","agent/src/skills/pine-script/SKILL.md":"# pine-script\n\nPath: `agent/src/skills/pine-script/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/pine-script/SKILL.md)","agent/src/skills/private-company-research/SKILL.md":"# private-company-research\n\nPath: `agent/src/skills/private-company-research/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/private-company-research/SKILL.md)","agent/src/skills/quant-statistics/SKILL.md":"# quant-statistics\n\nPath: `agent/src/skills/quant-statistics/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/quant-statistics/SKILL.md)","agent/src/skills/qveris/SKILL.md":"# qveris\n\nPath: `agent/src/skills/qveris/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/SKILL.md)","agent/src/skills/regulatory-knowledge/SKILL.md":"# regulatory-knowledge\n\nPath: `agent/src/skills/regulatory-knowledge/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/regulatory-knowledge/SKILL.md)","agent/src/skills/report-generate/SKILL.md":"# report-generate\n\nPath: `agent/src/skills/report-generate/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/report-generate/SKILL.md)","agent/src/skills/research-discipline/SKILL.md":"# research-discipline\n\nPath: `agent/src/skills/research-discipline/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/research-discipline/SKILL.md)","agent/src/skills/research-goal/SKILL.md":"# research-goal\n\nPath: `agent/src/skills/research-goal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/research-goal/SKILL.md)","agent/src/skills/risk-analysis/SKILL.md":"# risk-analysis\n\nPath: `agent/src/skills/risk-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/risk-analysis/SKILL.md)","agent/src/skills/seasonal/SKILL.md":"# seasonal\n\nPath: `agent/src/skills/seasonal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/seasonal/SKILL.md)","agent/src/skills/sec-edgar/SKILL.md":"# sec-edgar\n\nPath: `agent/src/skills/sec-edgar/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/SKILL.md)","agent/src/skills/sector-rotation/SKILL.md":"# sector-rotation\n\nPath: `agent/src/skills/sector-rotation/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sector-rotation/SKILL.md)","agent/src/skills/sentiment-analysis/SKILL.md":"# sentiment-analysis\n\nPath: `agent/src/skills/sentiment-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sentiment-analysis/SKILL.md)","agent/src/skills/shadow-account/SKILL.md":"# shadow-account\n\nPath: `agent/src/skills/shadow-account/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/shadow-account/SKILL.md)","agent/src/skills/smc/SKILL.md":"# smc\n\nPath: `agent/src/skills/smc/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/SKILL.md)","agent/src/skills/social-media-intelligence/SKILL.md":"# social-media-intelligence\n\nPath: `agent/src/skills/social-media-intelligence/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/social-media-intelligence/SKILL.md)","agent/src/skills/stablecoin-flow/SKILL.md":"# stablecoin-flow\n\nPath: `agent/src/skills/stablecoin-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/stablecoin-flow/SKILL.md)","agent/src/skills/strategy-dev-manager/SKILL.md":"# strategy-dev-manager\n\nPath: `agent/src/skills/strategy-dev-manager/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/SKILL.md)","agent/src/skills/strategy-discovery/SKILL.md":"# strategy-discovery\n\nPath: `agent/src/skills/strategy-discovery/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-discovery/SKILL.md)","agent/src/skills/strategy-generate/SKILL.md":"# strategy-generate\n\nPath: `agent/src/skills/strategy-generate/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-generate/SKILL.md)","agent/src/skills/technical-basic/SKILL.md":"# technical-basic\n\nPath: `agent/src/skills/technical-basic/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/technical-basic/SKILL.md)","agent/src/skills/thesis-tracker/SKILL.md":"# thesis-tracker\n\nPath: `agent/src/skills/thesis-tracker/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/thesis-tracker/SKILL.md)","agent/src/skills/token-unlock-treasury/SKILL.md":"# token-unlock-treasury\n\nPath: `agent/src/skills/token-unlock-treasury/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/token-unlock-treasury/SKILL.md)","agent/src/skills/trade-journal/SKILL.md":"# trade-journal\n\nPath: `agent/src/skills/trade-journal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/trade-journal/SKILL.md)","agent/src/skills/tushare/SKILL.md":"# tushare\n\nPath: `agent/src/skills/tushare/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/SKILL.md)","agent/src/skills/us-etf-flow/SKILL.md":"# us-etf-flow\n\nPath: `agent/src/skills/us-etf-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/us-etf-flow/SKILL.md)","agent/src/skills/valuation-model/SKILL.md":"# valuation-model\n\nPath: `agent/src/skills/valuation-model/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/valuation-model/SKILL.md)","agent/src/skills/vnpy-export/SKILL.md":"# vnpy-export\n\nPath: `agent/src/skills/vnpy-export/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/vnpy-export/SKILL.md)","agent/src/skills/volatility/SKILL.md":"# volatility\n\nPath: `agent/src/skills/volatility/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/volatility/SKILL.md)","agent/src/skills/web-reader/SKILL.md":"# web-reader\n\nPath: `agent/src/skills/web-reader/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/web-reader/SKILL.md)","agent/src/skills/yfinance/SKILL.md":"# yfinance\n\nPath: `agent/src/skills/yfinance/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/SKILL.md)","agent/evals/harness/README.md":"# Harness Skill\n\nPath: `agent/evals/harness/README.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/README.md)","agent/src/skills/strategy-dev-manager/examples.md":"# Strategy-dev-manager Skill\n\nPath: `agent/src/skills/strategy-dev-manager/examples.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/examples.md)","agent/src/skills/strategy-generate/examples.md":"# Strategy-generate Skill\n\nPath: `agent/src/skills/strategy-generate/examples.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-generate/examples.md)","agent/src/tools/paper_trade/README.md":"# paper_trade Documentation\n\nPath: `agent/src/tools/paper_trade/README.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/tools/paper_trade/README.md)","agent/src/tools/paper_trade/paper_trade_step-v1.1.json":"# Paper_trade Skill\n\nPath: `agent/src/tools/paper_trade/paper_trade_step-v1.1.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/tools/paper_trade/paper_trade_step-v1.1.json)","agent/evals/harness/cases/identity/explicit_a_share.json":"# Identity Skill\n\nPath: `agent/evals/harness/cases/identity/explicit_a_share.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/explicit_a_share.json)","agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md":"# References Skill\n\nPath: `agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md)","agent/src/skills/elliott-wave/references/波浪结构.md":"# References Skill\n\nPath: `agent/src/skills/elliott-wave/references/波浪结构.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/references/波浪结构.md)","agent/src/skills/factor-research/references/combination.md":"# References Skill\n\nPath: `agent/src/skills/factor-research/references/combination.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/references/combination.md)","agent/src/skills/factor-research/references/neutralization.md":"# References Skill\n\nPath: `agent/src/skills/factor-research/references/neutralization.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/references/neutralization.md)","agent/src/skills/harmonic/references/PRZ交易.md":"# References Skill\n\nPath: `agent/src/skills/harmonic/references/PRZ交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/references/PRZ交易.md)","agent/src/skills/harmonic/references/XABCD形态.md":"# References Skill\n\nPath: `agent/src/skills/harmonic/references/XABCD形态.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/references/XABCD形态.md)","agent/src/skills/ichimoku/references/五线计算.md":"# References Skill\n\nPath: `agent/src/skills/ichimoku/references/五线计算.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/references/五线计算.md)","agent/src/skills/ichimoku/references/信号系统.md":"# References Skill\n\nPath: `agent/src/skills/ichimoku/references/信号系统.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/references/信号系统.md)","agent/src/skills/investor-lenses/references/bogle-cost-drag.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/bogle-cost-drag.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/bogle-cost-drag.md)","agent/src/skills/investor-lenses/references/buffett-quality-franchise.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/buffett-quality-franchise.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/buffett-quality-franchise.md)","agent/src/skills/investor-lenses/references/chanos-forensic-short.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/chanos-forensic-short.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/chanos-forensic-short.md)","agent/src/skills/investor-lenses/references/dalio-debt-cycle.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/dalio-debt-cycle.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/dalio-debt-cycle.md)","agent/src/skills/investor-lenses/references/duan-right-business.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/duan-right-business.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/duan-right-business.md)","agent/src/skills/investor-lenses/references/fengliu-weak-system.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/fengliu-weak-system.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/fengliu-weak-system.md)","agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md)","agent/src/skills/investor-lenses/references/graham-deep-value.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/graham-deep-value.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/graham-deep-value.md)","agent/src/skills/investor-lenses/references/lynch-garp.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/lynch-garp.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/lynch-garp.md)","agent/src/skills/investor-lenses/references/marks-cycle-position.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/marks-cycle-position.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/marks-cycle-position.md)","agent/src/skills/investor-lenses/references/munger-inversion.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/munger-inversion.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/munger-inversion.md)","agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md)","agent/src/skills/qveris/references/coverage.md":"# References Skill\n\nPath: `agent/src/skills/qveris/references/coverage.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/references/coverage.md)","agent/src/skills/qveris/references/rest-api.md":"# References Skill\n\nPath: `agent/src/skills/qveris/references/rest-api.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/references/rest-api.md)","agent/src/skills/sec-edgar/references/endpoints_and_limits.md":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/endpoints_and_limits.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/endpoints_and_limits.md)","agent/src/skills/sec-edgar/references/forms_and_concepts.md":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/forms_and_concepts.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/forms_and_concepts.md)","agent/src/skills/sec-edgar/references/get_sec_filings_tool.md":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/get_sec_filings_tool.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/get_sec_filings_tool.md)","agent/src/skills/sec-edgar/references/sec_edgar_client.md":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/sec_edgar_client.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/sec_edgar_client.md)","agent/src/skills/smc/references/结构突破.md":"# References Skill\n\nPath: `agent/src/skills/smc/references/结构突破.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/references/结构突破.md)","agent/src/skills/smc/references/订单块与缺口.md":"# References Skill\n\nPath: `agent/src/skills/smc/references/订单块与缺口.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/references/订单块与缺口.md)","agent/src/skills/strategy-dev-manager/references/decay_thresholds.md":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/decay_thresholds.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/decay_thresholds.md)","agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md)","agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md)","agent/src/skills/strategy-dev-manager/references/strategy_metrics.md":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/strategy_metrics.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/strategy_metrics.md)","agent/src/skills/strategy-dev-manager/templates/decay_report.md":"# Templates Skill\n\nPath: `agent/src/skills/strategy-dev-manager/templates/decay_report.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/templates/decay_report.md)","agent/src/skills/yfinance/references/tool_get_options_chain.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/tool_get_options_chain.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/tool_get_options_chain.md)","agent/src/skills/yfinance/references/tool_get_stock_profile.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/tool_get_stock_profile.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/tool_get_stock_profile.md)","agent/src/skills/yfinance/references/yahoo_client_get_chart.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_chart.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_chart.md)","agent/src/skills/yfinance/references/yahoo_client_get_options.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_options.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_options.md)","agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md)","agent/src/skills/yfinance/references/yahoo_client_search.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_search.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_search.md)","agent/src/skills/chanlun/references/买卖点/一买一卖.md":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/一买一卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/一买一卖.md)","agent/src/skills/chanlun/references/买卖点/三买三卖.md":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/三买三卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/三买三卖.md)","agent/src/skills/chanlun/references/买卖点/二买二卖.md":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/二买二卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/二买二卖.md)","agent/src/skills/chanlun/references/核心概念/中枢.md":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/中枢.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/中枢.md)","agent/src/skills/chanlun/references/核心概念/分型.md":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/分型.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/分型.md)","agent/src/skills/chanlun/references/核心概念/笔.md":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/笔.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/笔.md)","agent/src/skills/eastmoney/references/参考数据/大宗交易.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/大宗交易.md)","agent/src/skills/eastmoney/references/参考数据/股东户数.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/股东户数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/股东户数.md)","agent/src/skills/eastmoney/references/参考数据/融资融券.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/融资融券.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/融资融券.md)","agent/src/skills/eastmoney/references/参考数据/限售解禁.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/限售解禁.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/限售解禁.md)","agent/src/skills/eastmoney/references/板块/行业概念板块.md":"# 板块 Skill\n\nPath: `agent/src/skills/eastmoney/references/板块/行业概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/板块/行业概念板块.md)","agent/src/skills/eastmoney/references/研报舆情/券商研报.md":"# 研报舆情 Skill\n\nPath: `agent/src/skills/eastmoney/references/研报舆情/券商研报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/研报舆情/券商研报.md)","agent/src/skills/eastmoney/references/研报舆情/财经新闻.md":"# 研报舆情 Skill\n\nPath: `agent/src/skills/eastmoney/references/研报舆情/财经新闻.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/研报舆情/财经新闻.md)","agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md":"# 财务报表 Skill\n\nPath: `agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md)","agent/src/skills/eastmoney/references/资金面/资金流向.md":"# 资金面 Skill\n\nPath: `agent/src/skills/eastmoney/references/资金面/资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/资金面/资金流向.md)","agent/src/skills/eastmoney/references/选股检索/代码搜索.md":"# 选股检索 Skill\n\nPath: `agent/src/skills/eastmoney/references/选股检索/代码搜索.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/选股检索/代码搜索.md)","agent/src/skills/eastmoney/references/选股检索/全市场选股.md":"# 选股检索 Skill\n\nPath: `agent/src/skills/eastmoney/references/选股检索/全市场选股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/选股检索/全市场选股.md)","agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md":"# 龙虎榜 Skill\n\nPath: `agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md)","agent/src/skills/okx-market/references/合约行情/历史资金费率.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/历史资金费率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/历史资金费率.md)","agent/src/skills/okx-market/references/合约行情/持仓量.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/持仓量.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/持仓量.md)","agent/src/skills/okx-market/references/合约行情/标记价格.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/标记价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/标记价格.md)","agent/src/skills/okx-market/references/合约行情/资金费率.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/资金费率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/资金费率.md)","agent/src/skills/okx-market/references/合约行情/限价.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/限价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/限价.md)","agent/src/skills/okx-market/references/指数行情/指数K线.md":"# 指数行情 Skill\n\nPath: `agent/src/skills/okx-market/references/指数行情/指数K线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/指数行情/指数K线.md)","agent/src/skills/okx-market/references/指数行情/指数行情.md":"# 指数行情 Skill\n\nPath: `agent/src/skills/okx-market/references/指数行情/指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/指数行情/指数行情.md)","agent/src/skills/okx-market/references/现货行情/K线数据.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/K线数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/K线数据.md)","agent/src/skills/okx-market/references/现货行情/交易产品列表.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/交易产品列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/交易产品列表.md)","agent/src/skills/okx-market/references/现货行情/单个行情.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/单个行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/单个行情.md)","agent/src/skills/okx-market/references/现货行情/批量行情.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/批量行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/批量行情.md)","agent/src/skills/okx-market/references/现货行情/最近成交.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/最近成交.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/最近成交.md)","agent/src/skills/okx-market/references/现货行情/深度数据.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/深度数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/深度数据.md)","agent/src/skills/tushare/references/ETF专题/ETF份额规模.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF份额规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF份额规模.md)","agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md)","agent/src/skills/tushare/references/ETF专题/ETF基准指数.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF基准指数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF基准指数.md)","agent/src/skills/tushare/references/ETF专题/ETF基本信息.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF基本信息.md)","agent/src/skills/tushare/references/ETF专题/ETF复权因子.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF复权因子.md)","agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md)","agent/src/skills/tushare/references/ETF专题/ETF实时日线.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF实时日线.md)","agent/src/skills/tushare/references/ETF专题/ETF日线行情.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF日线行情.md)","agent/src/skills/tushare/references/债券专题/债券回购日行情.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/债券回购日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/债券回购日行情.md)","agent/src/skills/tushare/references/债券专题/全球财经事件.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/全球财经事件.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/全球财经事件.md)","agent/src/skills/tushare/references/债券专题/可转债发行.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债发行.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债发行.md)","agent/src/skills/tushare/references/债券专题/可转债基础信息.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债基础信息.md)","agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md)","agent/src/skills/tushare/references/债券专题/可转债票面利率.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债票面利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债票面利率.md)","agent/src/skills/tushare/references/债券专题/可转债行情.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债行情.md)","agent/src/skills/tushare/references/债券专题/可转债赎回信息.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债赎回信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债赎回信息.md)","agent/src/skills/tushare/references/债券专题/可转债转股价变动.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债转股价变动.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债转股价变动.md)","agent/src/skills/tushare/references/债券专题/可转债转股结果.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债转股结果.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债转股结果.md)","agent/src/skills/tushare/references/债券专题/国债收益率曲线.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/国债收益率曲线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/国债收益率曲线.md)","agent/src/skills/tushare/references/债券专题/大宗交易.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/大宗交易.md)","agent/src/skills/tushare/references/债券专题/大宗交易明细.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/大宗交易明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/大宗交易明细.md)","agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md)","agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md)","agent/src/skills/tushare/references/公募基金/基金净值.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金净值.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金净值.md)","agent/src/skills/tushare/references/公募基金/基金分红.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金分红.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金分红.md)","agent/src/skills/tushare/references/公募基金/基金列表.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金列表.md)","agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md)","agent/src/skills/tushare/references/公募基金/基金持仓.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金持仓.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金持仓.md)","agent/src/skills/tushare/references/公募基金/基金管理人.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金管理人.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金管理人.md)","agent/src/skills/tushare/references/公募基金/基金经理.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金经理.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金经理.md)","agent/src/skills/tushare/references/公募基金/基金规模.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金规模.md)","agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md":"# 外汇数据 Skill\n\nPath: `agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md)","agent/src/skills/tushare/references/外汇数据/外汇日线行情.md":"# 外汇数据 Skill\n\nPath: `agent/src/skills/tushare/references/外汇数据/外汇日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/外汇数据/外汇日线行情.md)","agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md)","agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md)","agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md)","agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md)","agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md)","agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md)","agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md)","agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md)","agent/src/skills/tushare/references/指数专题/中信行业成分.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/中信行业成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/中信行业成分.md)","agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md)","agent/src/skills/tushare/references/指数专题/国际主要指数.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/国际主要指数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/国际主要指数.md)","agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md)","agent/src/skills/tushare/references/指数专题/指数历史分钟.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数历史分钟.md)","agent/src/skills/tushare/references/指数专题/指数周线行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数周线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数周线行情.md)","agent/src/skills/tushare/references/指数专题/指数基本信息.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数基本信息.md)","agent/src/skills/tushare/references/指数专题/指数实时分钟.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数实时分钟.md)","agent/src/skills/tushare/references/指数专题/指数实时日线.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数实时日线.md)","agent/src/skills/tushare/references/指数专题/指数成分和权重.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数成分和权重.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数成分和权重.md)","agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md)","agent/src/skills/tushare/references/指数专题/指数日线行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数日线行情.md)","agent/src/skills/tushare/references/指数专题/指数月线行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数月线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数月线行情.md)","agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md)","agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md)","agent/src/skills/tushare/references/指数专题/申万实时行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万实时行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万实时行情.md)","agent/src/skills/tushare/references/指数专题/申万行业分类.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业分类.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业分类.md)","agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md)","agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md)","agent/src/skills/tushare/references/期权数据/期权分钟行情.md":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权分钟行情.md)","agent/src/skills/tushare/references/期权数据/期权合约信息.md":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权合约信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权合约信息.md)","agent/src/skills/tushare/references/期权数据/期权日线行情.md":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权日线行情.md)","agent/src/skills/tushare/references/期货数据/交易日历.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/交易日历.md)","agent/src/skills/tushare/references/期货数据/仓单日报.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/仓单日报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/仓单日报.md)","agent/src/skills/tushare/references/期货数据/南华期货指数行情.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/南华期货指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/南华期货指数行情.md)","agent/src/skills/tushare/references/期货数据/历史分钟行情.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/历史分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/历史分钟行情.md)","agent/src/skills/tushare/references/期货数据/合约信息.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/合约信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/合约信息.md)","agent/src/skills/tushare/references/期货数据/实时分钟行情.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/实时分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/实时分钟行情.md)","agent/src/skills/tushare/references/期货数据/日线行情.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/日线行情.md)","agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md)","agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md)","agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md)","agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md)","agent/src/skills/tushare/references/期货数据/每日持仓排名.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/每日持仓排名.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/每日持仓排名.md)","agent/src/skills/tushare/references/期货数据/每日结算参数.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/每日结算参数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/每日结算参数.md)","agent/src/skills/tushare/references/港股数据/港股交易日历.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股交易日历.md)","agent/src/skills/tushare/references/港股数据/港股分钟行情.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股分钟行情.md)","agent/src/skills/tushare/references/港股数据/港股利润表.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股利润表.md)","agent/src/skills/tushare/references/港股数据/港股基础信息.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股基础信息.md)","agent/src/skills/tushare/references/港股数据/港股复权因子.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股复权因子.md)","agent/src/skills/tushare/references/港股数据/港股复权行情.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股复权行情.md)","agent/src/skills/tushare/references/港股数据/港股实时日线.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股实时日线.md)","agent/src/skills/tushare/references/港股数据/港股日线行情.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股日线行情.md)","agent/src/skills/tushare/references/港股数据/港股现金流量表.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股现金流量表.md)","agent/src/skills/tushare/references/港股数据/港股财务指标数据.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股财务指标数据.md)","agent/src/skills/tushare/references/港股数据/港股资产负债表.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股资产负债表.md)","agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md":"# 现货数据 Skill\n\nPath: `agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md)","agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md":"# 现货数据 Skill\n\nPath: `agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md)","agent/src/skills/tushare/references/美股数据/美股交易日历.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股交易日历.md)","agent/src/skills/tushare/references/美股数据/美股利润表.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股利润表.md)","agent/src/skills/tushare/references/美股数据/美股基础信息.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股基础信息.md)","agent/src/skills/tushare/references/美股数据/美股复权因子.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股复权因子.md)","agent/src/skills/tushare/references/美股数据/美股复权行情.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股复权行情.md)","agent/src/skills/tushare/references/美股数据/美股日线行情.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股日线行情.md)","agent/src/skills/tushare/references/美股数据/美股现金流量表.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股现金流量表.md)","agent/src/skills/tushare/references/美股数据/美股财务指标数据.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股财务指标数据.md)","agent/src/skills/tushare/references/美股数据/美股资产负债表.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股资产负债表.md)","agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json":"# Identity Skill\n\nPath: `agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json)","agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md)","agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md)","agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md)","agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md)","agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md)","agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md)","agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md)","agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md)","agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md)","agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md)","agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md)","agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md)","agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md)","agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md)","agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md)","agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md)","agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md)","agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md)","agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md)","agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md)","agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md)","agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md)","agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md)","agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md)","agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md)","agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md)","agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md)","agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md)","agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md)","agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md)","agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md)","agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md)","agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md)","agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md)","agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md)","agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md)","agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md)","agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md)","agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md)","agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md)","agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md)","agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md)","agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md)","agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md)","agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md)","agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md)","agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md)","agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md)","agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md)","agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md)","agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md)","agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md)","agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md)","agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md)","agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md)","agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md)","agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md)","agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md)","agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md)","agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md)","agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md)","agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md)","agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md)","agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md)","agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md)","agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md)","agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md)","agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md)","agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md)","agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md)","agent/src/skills/tushare/references/股票数据/财务数据/利润表.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/利润表.md)","agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md)","agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md)","agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md)","agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md)","agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md)","agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md)","agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md)","agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md)","agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md)","agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md)","agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md)","agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md)","agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md)","agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md)","agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md)","agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md)","agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md":"# 基金销售行业数据 Skill\n\nPath: `agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md)","agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md":"# 基金销售行业数据 Skill\n\nPath: `agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md":"# 价格指数 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md)","agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md":"# 价格指数 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md":"# 国民经济 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md)","agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md":"# 景气度 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md":"# 社会融资 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md)","agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md":"# 货币供应量 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md)"},"files":{"agent/src/skills/asset-allocation/SKILL.md":"---\nname: asset-allocation\ndescription: Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.\ncategory: asset-class\n---\n\n# Asset Allocation and Portfolio Optimization\n\n## Overview\n\nFrom asset allocation theory to practical implementation, this skill covers classical frameworks (MPT, BL, risk budgeting, all-weather) and the usage of the four optimizers built into this system. The output can be written directly into `config.json`.\n\n## Asset Allocation Theory\n\n### 1. Modern Portfolio Theory (MPT, Markowitz)\n\n**Core idea**: maximize expected return for a given level of risk (the efficient frontier).\n\n```\nOptimization problem:\nmin w'Σw (portfolio variance)\ns.t. w'μ = target_return\n Σw = 1\n w ≥ 0 (no shorting)\n```\n\n| Advantages | Disadvantages |\n|------|------|\n| Mathematically rigorous | Extremely sensitive to inputs (garbage in, garbage out) |\n| Efficient frontier is visualizable | Concentrated-allocation problem (often produces extreme weights) |\n| Foundational framework | Assumes normality and ignores fat tails |\n\n**Practical advice**: do not use raw MPT directly. Add constraints (upper/lower bounds, sector limits) or use a regularized version.\n\n### 2. Black-Litterman Model\n\n**Core idea**: start from market equilibrium and incorporate investor views.\n\n```\nSteps:\n1. Reverse-imply market equilibrium returns: π = δΣw_mkt\n2. Build the view matrices: P (selection matrix), Q (view returns), Ω (view uncertainty)\n3. Blend the posterior: μ_BL = [(τΣ)^-1 + P'Ω^-1 P]^-1 [(τΣ)^-1 π + P'Ω^-1 Q]\n4. Run Markowitz optimization using posterior μ_BL\n```\n\n**Example views**:\n- Absolute view: \"China A-shares will return 10% over the next year\" → `P=[1,0,0], Q=[0.10]`\n- Relative view: \"China A-shares will outperform US equities by 5%\" → `P=[1,-1,0], Q=[0.05]`\n\n**Parameter guidance**:\n- `τ` (uncertainty scaling): `0.025-0.05`\n- `Ω`: set according to view confidence, where higher confidence = smaller variance\n\n### 3. Risk Budgeting\n\n**Core idea**: allocate by risk contribution rather than by capital share.\n\n```\nRisk contribution: RC_i = w_i × (Σw)_i / σ_p\nTarget: RC_i / σ_p = budget_i (for all i)\n```\n\n| Strategy | Risk Budget | Best Use Case |\n|------|---------|---------|\n| Equal risk contribution | Each asset 1/N | When you do not know which asset is best |\n| Equity-tilted risk budget | Stocks 60%, bonds 30%, commodities 10% | When you want equities to contribute more risk |\n| Dynamic risk budget | Adjust dynamically by signal strength | When you have market-timing ability |\n\n### 4. All-Weather Strategy\n\n**Bridgewater framework**: allocate risk equally across economic environments.\n\n```\nEconomic environment Asset allocation\n───────── ─────────\nGrowth rising Equities + commodities + corporate bonds\nGrowth falling Government bonds + inflation-protected bonds\nInflation rising Commodities + inflation-protected bonds + EM debt\nInflation falling Equities + government bonds\n\nSimplified allocation example for China-focused portfolios:\n- 30% CSI 300 / CSI 500\n- 40% government bonds / credit bonds\n- 15% gold\n- 15% commodities / REITs\n```\n\n## Guide to the 5 Optimizers\n\n### Overview of the Built-In Optimizers\n\nConfigure them in `config.json` through `optimizer` and `optimizer_params`:\n\n| optimizer | Display Name | Core Idea | Best Use Case |\n|-----------|--------|---------|---------|\n| `equal_volatility` | Equal Volatility | Allocate weights by inverse volatility | Simple and effective baseline |\n| `risk_parity` | Risk Parity | Equalize risk contribution while accounting for correlation | Long-term robust allocation |\n| `mean_variance` | Mean-Variance | Maximize Sharpe ratio or minimize variance | When return forecasts are available |\n| `max_diversification` | Maximum Diversification | Maximize the diversification ratio | When pursuing a low-correlation portfolio |\n| `turnover_aware` | Turnover-Aware | Mean-variance utility with an L1 penalty on weight changes vs the previous rebalance | When trading costs matter; tune `turnover_penalty` to your data frequency |\n\n### 1. `equal_volatility`\n\n```json\n{\n \"optimizer\": \"equal_volatility\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: `w_i = (1/σ_i) / Σ(1/σ_j)`\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Volatility calculation window (trading days) |\n\n**Advantages**: simple and fast, no return forecast required, no correlation matrix required. \n**Disadvantages**: ignores cross-asset correlation.\n\n### 2. `risk_parity`\n\n```json\n{\n \"optimizer\": \"risk_parity\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: solve for weights such that each asset contributes the same amount of risk.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Covariance-matrix estimation window |\n\n**Advantages**: accounts for correlation, spreads risk more evenly, and is robust over long horizons. \n**Disadvantages**: requires iterative solving and is sensitive to covariance estimates.\n\n### 3. `mean_variance`\n\n```json\n{\n \"optimizer\": \"mean_variance\",\n \"optimizer_params\": {\n \"lookback\": 60,\n \"risk_free\": 0.0\n }\n}\n```\n\n**Principle**: Markowitz optimization that maximizes the Sharpe ratio.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Window for estimating means and covariances |\n| risk_free | 0.0 | Risk-free rate (annualized) |\n\n**Advantages**: theoretically optimal (if inputs are accurate). \n**Disadvantages**: extremely sensitive to inputs, prone to extreme weights, and often performs poorly out of sample. \n**Recommendation**: do not make `lookback` too short (`<30` easily overfits), and add upper/lower weight constraints.\n\n### 4. `max_diversification`\n\n```json\n{\n \"optimizer\": \"max_diversification\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: maximize `DR = (w'σ) / σ_p` (the diversification ratio).\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Calculation window |\n\n**Advantages**: does not require return forecasts and seeks true diversification. \n**Disadvantages**: effectiveness is limited in highly correlated environments.\n\n### 5. `turnover_aware`\n\n```json\n{\n \"optimizer\": \"turnover_aware\",\n \"optimizer_params\": {\n \"lookback\": 60,\n \"risk_aversion\": 1.0,\n \"turnover_penalty\": 0.5\n }\n}\n```\n\n**Principle**: minimize `-w'μ + λ·w'Σw + γ·||w - w_prev||₁` subject to long-only, fully-invested weights — mean-variance utility with an L1 penalty on weight changes versus the previous rebalance, so the optimizer only trades when the expected improvement outweighs the (implicit) cost.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Calculation window |\n| risk_aversion | 1.0 | Weight on the variance term (λ) |\n| turnover_penalty | 0.0 | Weight on the L1 turnover term (γ); 0 reduces to the mean-variance baseline |\n\n**Advantages**: dampens rebalancing churn, which usually dominates realized costs; the first rebalance is unpenalized so the cold start is undistorted. \n**Disadvantages**: `turnover_penalty` is scale-sensitive to the return frequency of the input window — for daily returns even γ ≈ 0.5 strongly prefers holding still, so tune it per data frequency.\n\n### Optimizer Selection Decision Tree\n\n```\nDo you have return forecasts?\n├── Yes → Do trading costs / churn matter?\n│ ├── Yes → turnover_aware (tune turnover_penalty to data frequency)\n│ └── No → mean_variance (remember to add constraints)\n└── No → Do you need to account for correlation?\n ├── Yes → risk_parity (recommended default)\n └── No → Are volatility differences across assets large?\n ├── Yes → equal_volatility\n └── No → max_diversification\n```\n\n## Rebalancing Strategy\n\n### Three Rebalancing Triggers\n\n| Method | Trigger Condition | Advantages | Disadvantages |\n|------|---------|------|------|\n| Periodic rebalancing | Fixed monthly / quarterly date | Simple, predictable trading cost | May miss or delay adjustments |\n| Threshold trigger | Deviation from target weight > X% | Trades only when needed | Frequent trading in high-volatility markets |\n| Volatility trigger | VIX / volatility breaks a threshold | Adapts to market regime | Parameter selection is difficult |\n\n### Suggested Rebalancing Frequency\n\n| Asset Class | Suggested Frequency | Threshold |\n|---------|---------|------|\n| Equity portfolio | Monthly | ±5% |\n| Stock-bond mix | Quarterly | ±10% |\n| Global macro | Quarterly / semiannual | ±10% |\n| Cryptocurrency | Weekly / biweekly | ±15% (high volatility) |\n\n### Rebalancing in Backtests\n\nImplement rebalancing logic in `signal_engine.py`:\n\n```python\n# Periodic rebalancing example (every 20 trading days)\nif bar_count % rebalance_freq == 0:\n # Recompute weights\n new_weights = calculate_target_weights(data_map)\n for code, weight in new_weights.items():\n signals[code].iloc[i] = weight\n```\n\n## Cross-Asset Correlation Analysis\n\n### Typical Correlation Matrix (China-Focused Portfolio Example)\n\n| | CSI 300 | CSI 500 | Government Bonds | Gold | BTC |\n|--|--------|--------|------|------|-----|\n| CSI 300 | 1.00 | 0.85 | -0.15 | 0.05 | 0.10 |\n| CSI 500 | 0.85 | 1.00 | -0.10 | 0.03 | 0.12 |\n| Government Bonds | -0.15 | -0.10 | 1.00 | 0.20 | -0.05 |\n| Gold | 0.05 | 0.03 | 0.20 | 1.00 | 0.15 |\n| BTC | 0.10 | 0.12 | -0.05 | 0.15 | 1.00 |\n\n**Key patterns**:\n- Negative stock-bond correlation is the foundation of allocation (but it does not always hold; in 2022 both stocks and bonds sold off)\n- Gold has low correlation with equities and serves as a hedge\n- BTC's correlation with traditional assets is unstable and tends to become positive in crises\n- Large-cap versus small-cap China A-shares have high correlation (`0.85`), so diversification benefits are limited\n\n## Output Format\n\n```markdown\n## Asset Allocation Recommendation\n\n### Allocation Plan\n| Asset | Weight | Risk Contribution | Expected Return (Annualized) |\n|------|------|---------|--------------|\n| CSI 300 | 30% | 45% | 8% |\n| Government Bond ETF | 40% | 15% | 3% |\n| Gold | 15% | 20% | 5% |\n| BTC | 15% | 20% | 15% |\n\n### Optimizer Configuration\n```json\n{\n \"optimizer\": \"risk_parity\",\n \"optimizer_params\": {\"lookback\": 60}\n}\n```\n\n### Expected Risk / Return\n| Metric | Value |\n|------|-----|\n| Expected annualized return | 7.2% |\n| Expected annualized volatility | 8.5% |\n| Expected Sharpe | 0.85 |\n| Expected maximum drawdown | -12% |\n\n### Rebalancing Rules\n- Frequency: quarterly (first trading day of March / June / September / December)\n- Threshold: trigger when any asset deviates from target by ±10%\n- Cost: estimated annual trading cost 0.15%\n```\n\n## Notes\n\n1. **The optimizer needs enough instruments**: at least 3 instruments are needed for meaningful optimization; with 2 instruments, `equal_volatility` is usually enough\n2. **`lookback` window**: too short (`<20`) is noisy, too long (`>120`) reacts slowly, and 60 is a reasonable default\n3. **`mean_variance` trap**: it is the easiest to overfit, and out-of-sample Sharpe is often cut by half or more\n4. **Rebalancing cost**: frequent rebalancing eats into returns; for China A-share portfolios, stamp duty of 0.05% plus commissions is material\n5. **Cross-market allocation**: use `\"source\": \"auto\"` in `config.json`, and let `codes` mix instruments from different markets\n6. **Leverage constraint**: the sum of weights must be ≤ 1.0, and leverage is not allowed unless explicitly specified\n7. **Survivorship bias**: historical correlations may be distorted by delistings and new listings\n","agent/src/skills/akshare/SKILL.md":"---\nname: akshare\ncategory: data-source\ndescription: AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance.\n---\n\n## Overview\n\nAKShare is a completely free, open-source Python financial data library. No registration or API key required. It aggregates data from public sources (Sina, East Money, etc.) covering Chinese and global markets.\n\n- GitHub: https://github.com/akfamily/akshare (18k+ stars)\n- Install: `pip install akshare`\n\n## Quick Start\n\n```python\nimport akshare as ak\n\n# A-share daily OHLCV (前复权)\ndf = ak.stock_zh_a_hist(symbol=\"000001\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n\n# US stock daily\ndf = ak.stock_us_hist(symbol=\"105.AAPL\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n\n# HK stock daily\ndf = ak.stock_hk_hist(symbol=\"00700\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n```\n\n## Top 10 High-Frequency Interfaces\n\n### A-shares\n\n| Function | Description | Key Params |\n|----------|-------------|------------|\n| `stock_zh_a_hist()` | A-share OHLCV | symbol, period, start_date, end_date, adjust |\n| `stock_zh_a_spot_em()` | Real-time A-share quotes | (none) |\n| `stock_individual_info_em()` | Stock basic info | symbol |\n| `stock_zh_a_hist_min_em()` | Intraday bars | symbol, period(1/5/15/30/60) |\n\n### US / HK\n\n| Function | Description | Key Params |\n|----------|-------------|------------|\n| `stock_us_hist()` | US stock OHLCV | symbol (e.g. \"105.AAPL\"), period, start_date, end_date |\n| `stock_hk_hist()` | HK stock OHLCV | symbol (e.g. \"00700\"), period, start_date, end_date |\n\n### Macro / Forex / Futures\n\n| Function | Description |\n|----------|-------------|\n| `macro_china_gdp()` | China GDP data |\n| `macro_china_cpi()` | China CPI data |\n| `futures_main_sina()` | Futures main contract quotes |\n| `currency_boc_sina()` | BOC forex rates |\n\n## Column Names\n\nAKShare returns Chinese column names by default:\n\n| Chinese | English | Description |\n|---------|---------|-------------|\n| 日期 | date | Trade date |\n| 开盘 | open | Open price |\n| 最高 | high | High price |\n| 最低 | low | Low price |\n| 收盘 | close | Close price |\n| 成交量 | volume | Volume |\n| 成交额 | amount | Turnover |\n| 涨跌幅 | pct_change | % change |\n| 换手率 | turnover_rate | Turnover rate |\n\n## Date Format\n\n- Input: `YYYYMMDD` string (e.g. `\"20240101\"`)\n- Output: `日期` column as string, convert with `pd.to_datetime()`\n\n## Symbol Format\n\n- A-shares: pure digits `\"000001\"` (no .SZ suffix)\n- US stocks: `\"105.AAPL\"` (NASDAQ prefix 105), `\"106.BABA\"` (NYSE prefix 106)\n- HK stocks: `\"00700\"` (5-digit zero-padded)\n\n## Built-in Loader\n\nThe project has a built-in AKShare DataLoader at `backtest/loaders/akshare_loader.py`. When backtesting, the runner automatically falls back to AKShare when tushare/yfinance are unavailable.\n\n## Reference Docs\n\nFor less common interfaces, see the `references/` subdirectory or the official docs at https://akshare.akfamily.xyz/\n","agent/src/skills/alpha-zoo/SKILL.md":"---\nname: alpha-zoo\ndescription: Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks \"which alphas exist\", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.\ncategory: research\n---\n# Alpha Zoo\n\n## Purpose\n\nWhen the user asks about prebuilt cross-sectional alphas — Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart — or wants to bench a whole zoo on an investable universe (CSI 300, S&P 500, BTC-USDT, ...), this skill orients you. The zoo is the curated library; the bench is the evaluator.\n\n## Tools Available\n\n| Tool | When to use |\n|------|------|\n| `alpha_zoo` | Browse the library. `action=list_alphas` to enumerate (filterable by zoo / theme / universe), `action=get_alpha` for one alpha's metadata, `action=health` for registry load status. |\n| `alpha_bench` | Run IC / IR on one alpha or a whole zoo over a universe + period. Emits an HTML report. |\n| `factor_analysis` | Ad-hoc factor evaluation from a user-supplied factor CSV + return CSV. Use this when the user has their own factor (not in the zoo). |\n\n## Decision Tree\n\n- \"list all momentum alphas\" → `alpha_zoo` with `action=list_alphas, theme=momentum`.\n- \"show me gtja191_alpha_001\" → `alpha_zoo` with `action=get_alpha, alpha_id=gtja191_alpha_001`.\n- \"bench all of GTJA 191 on CSI 300 from 2020 to 2024\" → `alpha_bench` with `zoo=gtja191, universe=csi300, period=2020-2024`.\n- \"is the registry healthy\" → `alpha_zoo` with `action=health` — surfaces `loaded`, `failed`, and per-error reasons.\n- User uploads `my_factor.csv` → `factor_analysis` (zoo tools are for prebuilt alphas only).\n\n## Zoo Inventory\n\n| Zoo | Description | Approx. count |\n|------|------|------|\n| `kakushadze101` | Formulaic alphas from Kakushadze's 2015 paper. Mix of momentum, reversal, volume, and microstructure. | ~101 |\n| `gtja191` | Guotai Junan 191 alphas — A-share focused cross-sectional factors. | ~191 |\n| `qlib158` | Microsoft Qlib's 158 alpha factors — features tuned for ML pipelines. | ~158 |\n| `classical` | Fama-French 3/5-factor + Carhart momentum. | <10 |\n\nCounts are nominal; check `alpha_zoo action=health` for the live count currently loaded.\n\n## Constraints\n\n- **No per-stock per-date factor values are surfaced to the agent.** IC results are aggregate stats (mean / std / IR / positive-ratio); the HTML report shows top-N by IR plus formulas, never the underlying panel.\n- **Lookahead is banned in the operator set.** `delta(df, d)` requires `d >= 1`; the negative-shift `Ref(df, -n)` form does not exist. See `docs/alpha-zoo/spec.md` for the full operator catalogue.\n- **Universe loaders may not be wired for every market yet.** When `alpha_bench` returns `universe loader for X not yet implemented`, that's the W2 scaffold — the universe is recognised but the data pull lands in W4.\n- **Do not expose absolute filesystem paths in agent output.** The bench tool writes to `~/.vibe-trading/reports/` by default; refer to it by that shorthand, not by the resolved absolute path.\n- **`alpha_zoo` is read-only.** `alpha_bench` writes a single HTML file per run — no scratch state elsewhere.\n\n## Common Pitfalls\n\n- Filter mismatch on `list_alphas`: theme / universe must match the alpha's declared metadata exactly (e.g. `equity_cn`, not `cn` or `china`).\n- Calling `alpha_bench` with both `alpha_id` and `zoo` set — they are mutually exclusive; pick one.\n- Empty registry (`loaded=0`) means no zoo modules are populated yet; treat it as \"zoos pending W3 porting\" rather than a bug.\n\n## Reference\n\n- Operator catalogue: `docs/alpha-zoo/spec.md`\n- Registry contract: `src/factors/registry.py` (frozen; do not modify)\n- IC / layered NAV math: `src/factors/factor_analysis_core.py`\n","agent/src/skills/backtest-diagnose/SKILL.md":"---\nname: backtest-diagnose\ndescription: Diagnose failed or underperforming backtests, locate the root cause, and fix the issue\ncategory: tool\n---\n\n# Backtest Diagnosis\n\n## Overview\n\nUse this skill when a user reports that a backtest failed, raised an error, or produced poor results.\n\n## Diagnostic Workflow\n\n1. **Read existing artifacts**: use `read_file` to inspect `artifacts/metrics.csv`, `equity.csv`, and `trades.csv`\n2. **Read the code**: use `read_file` to inspect `code/signal_engine.py` and `config.json`\n3. **Classify the issue**: determine the root cause using the error taxonomy below\n4. **Apply the fix**: use `edit_file` to modify the code, then rerun the backtest\n5. **Verify the fix**: use `read_file` to inspect the new `metrics.csv`\n\n## Error Taxonomy\n\n### Runtime Errors (`exit_code != 0`)\n\n| Error Type | Common Cause | Fix |\n|---------|---------|---------|\n| ImportError | Missing dependency | `bash(\"pip install xxx\")` |\n| KeyError | DataFrame column-name mismatch | Check the actual column names in `data_map` |\n| IndexError | Empty data or insufficient length | Add length checks |\n| TypeError | Incorrect signal type | Ensure the return value is `pd.Series` |\n\n### Logic Bugs (Backtest Succeeds but Results Are Abnormal)\n\n1. **Zero trades** (`trade_count=0`): signal-logic bug. Conditions are too strict, so the signal stays at 0. Check whether entry and exit logic is reasonable, and inspect the signal series to confirm it is not all zeros.\n2. **Late trades** (first trade occurs more than 2 years after the backtest start): data-filtering bug. The lookback window may be too long, or the initial data segment may have been dropped. Shorten the window or check whether `dropna` is too aggressive.\n3. **Capital utilization < 50%** (mostly in cash): position-sizing bug. Signal triggers may be too sparse, or the position-sizing logic may be wrong.\n4. **Open position at the end** (a position still exists when the backtest ends): exit-timing bug. Forced liquidation may be missing, or exit logic does not cover the final segment.\n\n### Data Errors\n\n| Symptom | Root Cause | Fix |\n|------|------|---------|\n| No data fetched | Invalid API token or code issue | Check `config.json` |\n| Too little data | Date range too narrow | Expand the date range |\n\n### Data-Source Error Ignore List\n\nIf you encounter the following keywords, **do not modify the code**. The problem is on the data-provider side:\n- a provider-side \"no data available\" response\n- `rate limit`\n- `API limit`\n- `daily limit`\n- `Information` (common in Tushare API responses)\n\nThese issues require the user to check the API token, switch data sources, or wait for the quota to reset.\n\n## Hard-Gate Checklist\n\n1. `artifacts/metrics.csv` exists and is non-empty\n2. `artifacts/equity.csv` exists and is non-empty\n3. `trade_count > 0` (`0` trades means a signal bug)\n4. The equity series contains no `NaN`\n5. `exit_code == 0`\n\n## Evidence hookup\n\nThis Hard-Gate Checklist is also the evidence ingestion gate for Strategy Discovery: a run failing any gate produces **no evidence rows** — never partial rows — and is skipped with a stable machine-readable token (`hard-gate:exit-nonzero`, `hard-gate:metrics-missing`, `hard-gate:zero-trades`, `hard-gate:equity-empty`, `hard-gate:equity-nan`). Diagnose and fix the failing gate as usual, rerun the backtest, then repopulate the evidence cache with `refresh_strategy_evidence` (agent tool / MCP tool, or `vibe-trading strategy-evidence refresh --manifest `) so the fixed run becomes queryable evidence. See the `strategy-discovery` skill for the manifest format and the full gate list.\n\n## Fixing Principles\n\n- Use **edit_file** to make precise code fixes instead of rewriting the entire file with `write_file`, unless the structure is fundamentally broken\n- Fix the bug only, do not change strategy logic unless the user explicitly asks\n- Fix one issue at a time, and rerun the backtest immediately after each fix\n- Limit yourself to at most 3 repair iterations\n\n## Post-Fix Validation Rules\n\nAfter modifying `signal_engine.py`, you must confirm:\n1. **AST syntax passes**: `bash(\"python -c \\\"import ast; ast.parse(open('code/signal_engine.py').read()); print('OK')\\\"\")`\n2. **Contains `class SignalEngine`**: the file must define `class SignalEngine`\n3. **Contains `def generate`**: the class must contain a `def generate` method\n4. **Rerun the backtest**: after the fix, rerun the backtest and verify the results\n\n## `action_items` Writing Rules\n\nAfter diagnosis, output actionable improvement suggestions:\n- Format: `\"Change X from A to B\"` or `\"Add X logic in signal_engine.py\"`\n- Be specific about parameter values, filenames, and function names\n- Provide at least 2 items\n- Examples:\n - `\"Change RSI threshold from 30 to 25 in signal_engine.py line 42\"`\n - `\"Add signals = signals.fillna(0) after signal calculation to prevent NaN propagation\"`\n - `\"Add a volume filter: skip buy signals when volume is below the 20-day average\"`\n","agent/src/skills/adr-hshare/SKILL.md":"---\nname: adr-hshare\ndescription: ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.\ncategory: flow\n---\n# ADR / H-Share / A-Share Cross-Listing Analysis\n\n## Overview\n\nMany Chinese companies are listed on multiple exchanges — A-shares in Shanghai/Shenzhen, H-shares in Hong Kong, and ADRs in the US. Pricing gaps between these listings create arbitrage opportunities and reveal market-specific sentiment differences. This skill provides frameworks for analyzing cross-listing premiums, identifying arbitrage signals, and assessing delisting risk for US-listed Chinese ADRs.\n\n## Core Concepts\n\n### 1. Cross-Listing Structures\n\n| Structure | Description | Examples |\n|-----------|-------------|---------|\n| A + H dual-listed | Same company listed on both A-share and HK exchange | PetroChina (601857.SH / 0857.HK), ICBC (601398.SH / 1398.HK) |\n| H + ADR dual-listed | HK-listed with US ADR | Alibaba (9988.HK / BABA), JD.com (9618.HK / JD) |\n| A + H + ADR triple-listed | All three markets | China Life (601628.SH / 2628.HK / LFC) |\n| HK primary + US secondary | Primary listing in HK, secondary ADR | Tencent (0700.HK / TCEHY OTC) |\n| US primary → HK secondary | Originally US, added HK listing | Alibaba (BABA → 9988.HK), Baidu (BIDU → 9888.HK) |\n\n### 2. AH Premium Analysis\n\n**AH Premium = (A-share price / H-share price in CNY terms - 1) × 100%**\n\n```python\ndef calculate_ah_premium(a_price_cny, h_price_hkd, usdcny, usdhkd):\n \"\"\"Calculate AH premium for a dual-listed stock.\"\"\"\n h_price_cny = h_price_hkd * (usdcny / usdhkd) # Convert HKD to CNY\n ah_premium = (a_price_cny / h_price_cny - 1) * 100\n return ah_premium\n\n# Example: PetroChina\n# A-share: 8.50 CNY, H-share: 6.20 HKD\n# USDCNY: 7.25, USDHKD: 7.82\n# H in CNY: 6.20 * (7.25/7.82) = 5.75 CNY\n# AH Premium: (8.50/5.75 - 1) * 100 = 47.8%\n```\n\n**AH Premium signal interpretation:**\n\n| Premium Level | Interpretation | Action |\n|--------------|----------------|--------|\n| >50% | Extreme A-share premium; A-share speculative bubble or H-share extreme undervaluation | Strong: buy H, sell/avoid A |\n| 30-50% | Elevated premium; normal for high-retail-participation names | Moderate: favor H if fundamentals same |\n| 10-30% | Normal range for most AH pairs | Neutral; no strong arbitrage signal |\n| 0-10% | Compressed premium; A-shares relatively cheap | Unusual; investigate catalyst |\n| <0% | H-share premium over A-share | Very rare; usually near-term event-driven |\n\n**Structural drivers of AH premium:**\n1. **Liquidity premium**: A-shares have much higher retail participation and turnover → liquidity premium\n2. **Access premium**: A-shares were historically hard for foreigners to access → scarcity premium\n3. **Currency expectations**: CNY depreciation expectations widen the premium\n4. **Regulatory arbitrage**: different trading rules (T+1 in A-shares vs T+0 in HK)\n5. **Investor composition**: A-share retail speculative premium vs HK institutional valuation discipline\n\n### 3. ADR Premium/Discount Analysis\n\n**ADR premium = (ADR price in USD / HK equivalent in USD - 1) × 100%**\n\n```python\ndef calculate_adr_premium(adr_price_usd, hk_price_hkd, adr_ratio, usdhkd):\n \"\"\"\n Calculate ADR premium over HK listing.\n adr_ratio: number of HK shares per 1 ADR (e.g., BABA: 1 ADR = 8 HK shares)\n \"\"\"\n hk_equivalent_usd = (hk_price_hkd * adr_ratio) / usdhkd\n premium = (adr_price_usd / hk_equivalent_usd - 1) * 100\n return premium\n\n# Example: Alibaba\n# BABA ADR: $85.00, 9988.HK: HKD 82.50\n# ADR ratio: 1 ADR = 8 HK shares\n# HK equivalent: (82.50 * 8) / 7.82 = $84.40\n# ADR premium: (85.00/84.40 - 1) * 100 = 0.71%\n```\n\n**Key ADR conversion ratios:**\n\n| Company | ADR Ticker | HK Ticker | ADR Ratio (HK:ADR) | ADR Exchange |\n|---------|-----------|-----------|---------------------|--------------|\n| Alibaba | BABA | 9988.HK | 8:1 | NYSE |\n| JD.com | JD | 9618.HK | 2:1 | NASDAQ |\n| Baidu | BIDU | 9888.HK | 8:1 | NASDAQ |\n| Bilibili | BILI | 9626.HK | 1:1 | NASDAQ |\n| NIO | NIO | 9866.HK | 1:1 | NYSE |\n| XPeng | XPEV | 9868.HK | 2:1 | NYSE |\n| Li Auto | LI | 2015.HK | 2:1 | NASDAQ |\n| NetEase | NTES | 9999.HK | 5:1 | NASDAQ |\n| Trip.com | TCOM | 9961.HK | 1:1 | NASDAQ |\n| Pinduoduo | PDD | N/A (US-only) | N/A | NASDAQ |\n\n**ADR premium drivers:**\n- US trading hours sentiment (earnings releases, macro data during US hours)\n- US-specific regulatory events (SEC, PCAOB audits)\n- Liquidity premium (ADR often more liquid for global funds)\n- Time zone gap: ADR closes at HK's open → overnight gap creates premium/discount\n\n### 4. Delisting Risk Assessment\n\n**HFCAA (Holding Foreign Companies Accountable Act) framework:**\n\nSince 2022, PCAOB gained access to audit workpapers of Chinese companies. Key risks:\n\n| Risk Level | Criteria | Impact |\n|------------|----------|--------|\n| Low | PCAOB inspection completed, no issues | ADR status stable |\n| Medium | PCAOB inspection completed, deficiencies noted | Monitor for resolution |\n| High | PCAOB access revoked or restricted | 3-year delisting countdown activated |\n| Critical | On SEC \"identified issuer\" list for 3 consecutive years | Forced delisting |\n\n**Delisting risk indicators:**\n```python\ndelisting_risk_factors = {\n \"pcaob_status\": \"inspected\", # inspected / pending / blocked\n \"sec_identified_years\": 0, # 0, 1, 2, or 3 (3 = delist)\n \"has_hk_listing\": True, # Backup listing reduces impact\n \"hk_listing_type\": \"primary\", # primary (can be in Connect) vs secondary\n \"vie_structure\": True, # Variable Interest Entity adds legal risk\n \"state_owned\": False, # SOE status adds geopolitical risk\n}\n\n# Companies with HK primary listing (BABA, JD, BIDU, NTES, etc.) have\n# a safety net if US delisting occurs → fungible conversion ADR → HK shares\n# Companies with US-only listing (PDD until HK listing) face higher risk\n```\n\n### 5. Cross-Listing Arbitrage Strategies\n\n**Strategy 1: AH Premium Mean-Reversion**\n```python\n# When AH premium for a specific stock diverges significantly from its historical average\nah_premium_current = 45 # current premium\nah_premium_mean_12m = 35 # 12-month average\nah_premium_std = 8 # standard deviation\n\nz_score = (ah_premium_current - ah_premium_mean_12m) / ah_premium_std\n\nif z_score > 2.0:\n signal = \"fade_premium\" # A-share overvalued vs H; buy H, avoid A\nelif z_score < -2.0:\n signal = \"buy_premium\" # A-share undervalued vs H; buy A, avoid H\nelse:\n signal = \"neutral\"\n```\n\n**Strategy 2: ADR-HK Intraday Arbitrage**\n- During overlapping trading hours (HK morning = US pre-market via ADR), price gaps can appear\n- Professional arbitrageurs use ADR↔HK fungible conversion to capture these gaps\n- For research purposes: tracking ADR premium trend indicates which market is leading sentiment\n\n**Strategy 3: Event-Driven Cross-Listing**\n- New HK listing announcement (US→HK): ADR typically dips 2-5% on dilution fear, then recovers\n- MSCI / FTSE index inclusion of HK listing: triggers passive fund buying in HK\n- Stock Connect inclusion (HK primary listing eligible): triggers mainland institutional buying\n\n## Data Access\n\n```python\nimport yfinance as yf\n\n# Fetch ADR and HK prices simultaneously\nbaba_adr = yf.download(\"BABA\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\nbaba_hk = yf.download(\"9988.HK\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\n\n# For A+H pairs\npetrochina_a = yf.download(\"601857.SS\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\npetrochina_h = yf.download(\"0857.HK\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\n\n# FX rates for premium calculation\ncny = yf.download(\"CNY=X\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False) # USD/CNY\nhkd = yf.download(\"HKD=X\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False) # USD/HKD\n\n# AH Premium Index (HSAHP)\n# Not directly on yfinance; use Hang Seng website or Tushare\n```\n\n## Output Format\n\n```\n## Cross-Listing Analysis — [Company Name]\n\n### Listing Structure\n- **A-share**: [code] @ [price CNY]\n- **H-share**: [code] @ [price HKD]\n- **ADR**: [ticker] @ [price USD] (ratio: X HK shares = 1 ADR)\n\n### Premium/Discount\n- **AH Premium**: [X%] (12m avg: X%, z-score: X.X)\n- **ADR-HK Premium**: [X%] (5d avg: X%)\n- **Direction**: [AH premium widening / narrowing / stable]\n\n### Valuation Comparison\n| Metric | A-share | H-share | ADR |\n|--------|---------|---------|-----|\n| PE (TTM) | XX.X | XX.X | XX.X |\n| PB | X.X | X.X | X.X |\n| Dividend yield | X.X% | X.X% | X.X% |\n\n### Delisting Risk (ADR)\n- **PCAOB status**: [inspected / pending]\n- **SEC identified years**: [0/1/2/3]\n- **HK backup**: [yes-primary / yes-secondary / no]\n- **Risk level**: [low / medium / high / critical]\n\n### Arbitrage Signal\n- **AH premium z-score**: [X.X] → [fade premium / neutral / buy premium]\n- **Best market to buy**: [A / H / ADR] — rationale\n- **Catalyst**: [index inclusion, Connect eligibility, earnings]\n\n### Investment Implication\n- **Preferred listing**: [H-share / ADR / A-share] for new position\n- **Risk**: [delisting, FX, regulatory, liquidity]\n```\n\n## Notes\n\n- AH premium arbitrage is not freely executable: A-shares and H-shares are NOT fungible (no direct conversion), so true arbitrage requires separate capital pools\n- ADR↔HK conversion IS possible for most dual-listed names (via depositary bank), but takes 2-3 business days and involves fees\n- Currency risk (CNY, HKD, USD) is a major driver of cross-listing premiums; always hedge or account for FX when comparing\n- VIE (Variable Interest Entity) structure adds a layer of legal risk for many Chinese ADRs; this is a structural risk, not a trading signal\n- Stock Connect eligibility requirements mean not all HK-listed Chinese companies are accessible to mainland investors\n- This framework is for research purposes only and does not constitute investment advice\n","agent/SKILL.md":"---\nname: vibe-trading\nversion: 0.1.18\ndescription: Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).\ndependencies:\n python: \">=3.11\"\n pip:\n - vibe-trading-ai\nenv:\n - name: TUSHARE_TOKEN\n description: \"Tushare API token for China A-share data (optional — HK/US/Canada/crypto work without any key)\"\n required: false\n - name: OPENAI_API_KEY\n description: \"OpenAI-compatible API key — only needed for run_swarm (multi-agent teams). All other tools work without it.\"\n required: false\n - name: LANGCHAIN_MODEL_NAME\n description: \"LLM model name for run_swarm (e.g. deepseek/deepseek-v4-pro). Only needed if using run_swarm.\"\n required: false\nmcp:\n command: vibe-trading-mcp\n args: []\n---\n\n# Vibe-Trading\n\nProfessional finance research toolkit with AI-powered backtesting (11 engines), multi-agent teams, 90 specialized skills, the **Alpha Zoo** (478 pre-built quantitative alphas across qlib158 / alpha101 / gtja191 / academic / fundamental with one-line CLI benchmarking), and the Shadow Account loop — extract your implicit trading rules from a journal, backtest them across A股/港股/美股/crypto, then see where they would have served you better.\n\n## Setup\n\n```bash\npip install vibe-trading-ai\n```\n\n> **Package name vs commands:** The PyPI package is `vibe-trading-ai`. Once installed, you get:\n>\n> | Command | Purpose |\n> |---------|---------|\n> | `vibe-trading` | Interactive CLI / TUI |\n> | `vibe-trading serve` | Launch FastAPI web server |\n> | `vibe-trading-mcp` | Start MCP server (for Claude Desktop, OpenClaw, Cursor, etc.) |\n\nAdd to your agent's MCP config:\n\n```json\n{\n \"mcpServers\": {\n \"vibe-trading\": {\n \"command\": \"vibe-trading-mcp\"\n }\n }\n}\n```\n\n### API Key Requirements\n\nCore research MCP tools work with zero API keys for HK/US/Canada/crypto. After `pip install`, backtesting, market data, factor analysis, options pricing, chart patterns, web search, document reading, trade journal analysis, shadow-account extraction/backtest/report, the Alpha Zoo (478 pre-built alphas), and all 90 skills are ready to use. IBKR tools require a local TWS / IB Gateway session; `run_swarm` requires an LLM key.\n\n| Feature | Key needed | When |\n|---------|-----------|------|\n| HK/US/Canada equities & crypto | None | Always free (yfinance / stooq / yahoo + OKX) |\n| China A-share data | None | Free via akshare / baostock / tencent / sina / eastmoney fallback (`TUSHARE_TOKEN` optional for premium quality) |\n| Premium US fundamentals/quotes | `FINNHUB_API_KEY` / `ALPHAVANTAGE_API_KEY` / `TIINGO_API_KEY` / `FMP_API_KEY` | Only for optional-key providers (graceful fallback to free sources) |\n| Multi-agent swarm (`run_swarm`) | `OPENAI_API_KEY` + `LANGCHAIN_MODEL_NAME` | Swarm spawns internal LLM workers |\n\n## What You Can Do\n\n### Shadow Account — flagship loop\n\nFeed a CSV broker export (同花顺 / 东财 / 富途 / generic), and the agent will:\n1. `analyze_trade_journal` — profile your behavior (holding period, win rate, disposition effect, chasing, overtrading, anchoring).\n2. `extract_shadow_strategy` — distill 3-5 if-then rules that describe your profitable roundtrips.\n3. `run_shadow_backtest` — backtest those rules across A/HK/US/crypto and compute delta-PnL vs your realized trades.\n4. `render_shadow_report` — produce an HTML/PDF report (8 sections + charts) with today's matching signals.\n5. `scan_shadow_signals` — list today's symbols that match your shadow's entry cadence (research only).\n\n### Backtesting\nCreate and run quantitative strategies across 11 engines (ChinaA, GlobalEquity, IndiaEquity, KoreaEquity, VietnamEquity, BrazilEquity, Crypto, ChinaFutures, GlobalFutures, Forex, cross-market Composite) plus an options portfolio engine, with 29 market-data sources (auto-detect + ordered fallback; the hosted forex `tickerall` source is explicit-only):\n- **HK/US equities** via yfinance / stooq / yahoo (free, no API key); optionally via **Longbridge** historical OHLCV (`longbridge`, requires the optional SDK and `LONGBRIDGE_APP_KEY` / `LONGBRIDGE_APP_SECRET` / `LONGBRIDGE_ACCESS_TOKEN`). To force it for a run, set `\"source\": \"longbridge\"` in `config.json`.\n- **Canada equities (TSX/TSXV)** via yahoo / yfinance using Yahoo's canonical `.TO` (TSX, e.g. `TD.TO`) or `.V` (TSXV, e.g. `PNG.V`) suffixes — free, no API key. The GlobalEquity engine uses CAD identity, whole-share orders, configurable Canadian commission/slippage, and the TSX/TSXV price-increment grid.\n- **India equities (NSE/BSE)** via yahoo / yfinance using `.NS` (NSE, e.g. `RELIANCE.NS`) or `.BO` (BSE, e.g. `500325.BO`) — free, no API key. The `IndiaEquityEngine` models T+1 delivery, no overnight shorts (set `allow_short` for intraday), configurable circuit bands, 1-share lots, and the STT/stamp-duty/exchange/GST cost stack. Optionally back-fill from your live broker via the `india_broker` source (Shoonya/Dhan; requires broker login).\n- **Korea equities (KRX: KOSPI/KOSDAQ)** via pykrx using `.KS` (KOSPI, e.g. `005930.KS`) or `.KQ` (KOSDAQ, e.g. `247540.KQ`) — free, no API key (`pip install \"vibe-trading-ai[krx]\"`; yahoo/yfinance fallback needs no extra). pykrx serves **daily bars only** (an intraday request falls through to another source) and its adjusted series is Naver-backed rather than a verbatim KRX print. The `KoreaEquityEngine` models same-day round trips (no T+1), the ±30% daily price limit measured from the previous close and quantized to the KRX tick grid, tick-rounded fills, the 0.20% sell-side transaction tax (2026 rate), and 1-share lots. It is **long-only**: `allow_short` is refused, because KRX covered-short and uptick rules cannot be enforced on daily bars.\n- **Vietnam equities (HOSE)** via yahoo / yfinance using `.VN` (e.g. `VIC.VN`) — no API key. Yahoo officially lists HOSE but not HNX or UPCOM; those venues are unsupported and need the `local` source. yfinance is an unofficial Yahoo client, so availability is best-effort and subject to Yahoo's personal-use terms. The `VietnamEquityEngine` approximates the formal T+2 settlement cycle — shares bought on day T normally become sellable during the afternoon session on T+2, informally called T+1.5 — as a two-bar hold on daily data (`vn_settlement_bars` covers scenario testing and future rule changes). It applies HOSE's normal ±7% band around the reference price, rounding the ceiling down and the floor up to the 10/50/100-VND tick grid, and uses 100-share round lots; odd-lot trading is not modelled. Costs are configurable brokerage plus, for individual investors, 0.1% sell-side personal income tax on gross proceeds. It is **long-only**, because operational cash-equity short selling is not generally available in Vietnam.\n- **Cryptocurrency** via OKX or CCXT/100+ exchanges (free, no API key)\n- **China A-shares** via AKShare / baostock / tencent / sina / eastmoney (free, no API key) — `TUSHARE_TOKEN` optional for premium quality\n- **Futures, forex, macro** via AKShare (free, no API key)\n- **Forex / metals with no local terminal** via the hosted **TickerAll** MetaTrader 5 feed (`source=\"tickerall\"`, `TICKERALL_API_KEY` + `TICKERALL_ACCOUNT_ID`, read-only) — the same broker feed as the `mt5` loader but over a hosted API on any OS. **Explicit-only** (never an automatic fallback).\n- **HK & A-share equities** via Futu (broker login required, optional)\n- **Local CSV/parquet bars** via the `local` loader (offline, no network)\n- **Premium cross-market data** via QVeris (optional API key)\n- **Premium US data** via optional-key finnhub / alphavantage / tiingo / fmp (graceful fallback to free sources)\n\nFactors: the Alpha101 and QLib158 zoos are tagged for the `equity_in` and `equity_kr` universes, so they compute on NSE/BSE and KRX bars (the GTJA191 zoo stays China-only). Live/paper India trading uses the Shoonya / Dhan connectors (paper + read-only live; live order placement is structurally disabled because those brokers expose no paper/live switch). Live/paper Korea equity trading uses the **KIS** connector (한국투자증권; genuine 모의투자 paper sandbox on a separate host/port from 실전투자 live, plus read-only live — no mandate-gated live-trade profile yet). Live/paper Korea crypto trading uses the **Upbit** connector (KRW markets; paper + read-only live — no runtime paper/live discriminator, so live order placement is structurally disabled, same as Shoonya/Dhan). Read-only Korea/US equity access is also available via the **Toss Securities** connector (토스증권) — no verified sandbox, so order placement always refuses, same as Trading 212.\n\nExample workflow:\n1. Use `list_skills()` to discover strategy patterns\n2. Use `load_skill(\"strategy-generate\")` for the strategy creation guide\n3. Use `write_file()` to create `config.json` and `code/signal_engine.py`\n4. Use `backtest()` to run and get metrics (Sharpe, return, drawdown, etc.)\n\n### Multi-Agent Swarm Teams\n30 pre-built agent teams for complex research:\n- **Investment Committee**: bull/bear debate → risk review → PM decision\n- **Global Equities Desk**: A-share + HK/US + crypto → global strategist\n- **Crypto Trading Desk**: funding/basis + liquidation + flow → risk manager\n- **Earnings Research Desk**: fundamentals + revisions + options → earnings strategist\n- **Macro/Rates/FX Desk**: rates + FX + commodities → macro PM\n- **Quant Strategy Desk**: screening → factor research → backtest → risk audit\n- **Risk Committee**: drawdown, tail risk, regime analysis\n- And 23 more specialized teams\n\nUse `list_swarm_presets()` to see all teams, then `run_swarm()` to execute.\n\n### Alpha Zoo (478 pre-built alphas)\nOne-line cross-sectional IC / IR / alive-reversed-dead categorisation across five bundled zoos:\n- **qlib158** (154 alphas) — Microsoft Qlib's `Alpha158` feature handler, Apache-2.0 with pinned commit SHA.\n- **alpha101** (101 alphas) — Kakushadze (2015) \"101 Formulaic Alphas\" (arXiv:1601.00991), written from the paper appendix.\n- **gtja191** (191 alphas) — Guotai Junan 2014 \"191 Short-period Trading Alpha Factors\" research report.\n- **academic** (12 factors) — Fama-French 5 + Carhart momentum + Jegadeesh reversal + George-Hwang 52-week-high + Amihud illiquidity + Harvey-Siddique skew + Frazzini-Pedersen betting-against-beta (price-based proxies) + a correlation-rewiring stability score (from the in-repo correlation-regime skill).\n- **fundamental** (20 factors) — PIT-safe earnings yield, ROE, gross profitability and asset growth, plus ten SEC XBRL additions: book-to-market, FCF yield, accruals, ROA, CFO yield, operating margin, cash-to-assets, leverage, revenue growth and earnings growth, from daily fundamental panels; three CN A-share daily-valuation factors (PB, PS_TTM, dividend yield) fed by the ``val:*`` panel namespace from tushare daily_basic; and three industry-neutralized variants of those CN value factors, z-scored within Shenwan L1 peers against a PIT SW2021 industry tag from tushare index_member_all (membership is point-in-time; the SW2021 taxonomy itself is retroactive).\n\nEach alpha ships with `__alpha_meta__` (formula LaTeX + theme + universe + warmup + columns required), guarded by an AST purity gate + 300-row lookahead sentinel test. Use the `vibe-trading alpha {list,show,bench,compare,export-manifest}` CLI, the `/alpha/*` REST routes (browser at `/alpha-zoo`), or compose multi-factor signals via `ZooSignalEngine.from_zoo(...)`.\n\n### Finance Skills (90)\nComprehensive knowledge base covering:\n- Technical analysis (candlestick, Elliott wave, Ichimoku, SMC, harmonic, chanlun)\n- Quantitative methods (factor research, ML strategy, pair trading, multi-factor)\n- Risk management (VaR/CVaR, stress testing, hedging)\n- Options (Black-Scholes, Greeks, multi-leg strategies, payoff diagrams)\n- HK/US equities (SEC filings, earnings revisions, ETF flows, ADR/H-share arbitrage)\n- Crypto trading desk (funding rates, liquidation heatmaps, stablecoin flows, token unlocks, DeFi yields)\n- Behavioral finance, trade journal diagnostics, shadow account\n- Macro analysis, credit research, sector rotation, and more\n\nUse `load_skill(name)` to access full methodology docs with code templates.\n\n## Available MCP Tools (82)\n\n| Tool | Description | API Key |\n|------|-------------|---------|\n| `list_skills` | List all 90 finance skills | None |\n| `load_skill` | Load full skill documentation | None |\n| `start_research_goal` | Create an auditable research goal | None |\n| `get_research_goal` | Read the current research goal | None |\n| `add_goal_evidence` | Attach evidence to a research goal | None |\n| `update_research_goal_status` | Update goal lifecycle status | None |\n| `backtest` | Run vectorized backtest engine | None* |\n| `paper_trade_step` | Deterministic paper-trading matching engine; never places real orders | None* |\n| `factor_analysis` | IC/IR analysis + layered backtest | None* |\n| `alpha_zoo` | Browse bundled alpha metadata and registry health | None |\n| `alpha_bench` | Benchmark one alpha or a complete zoo | None* |\n| `analyze_options` | Black-Scholes price + Greeks | None |\n| `analyze_options_payoff` | Multi-leg expiry payoff + spot/IV scenarios | None |\n| `pattern_recognition` | Detect chart patterns (H&S, double top, etc.) | None |\n| `get_market_data` | Fetch OHLCV data (auto-detect + ordered fallback across 29 sources) | None* |\n| `get_fund_flow` | Capital fund-flow (main/retail net inflow) | None* |\n| `get_dragon_tiger` | Dragon-tiger list (龙虎榜) top buyer/seller seats | None* |\n| `get_northbound_flow` | Northbound (Stock Connect) net flow | None* |\n| `get_southbound_flow` | Southbound (Stock Connect) net buy into HK (Eastmoney/HKEX) | None |\n| `get_margin_trading` | Margin trading & short-selling balances | None* |\n| `get_block_trades` | Block-trade (大宗交易) records | None* |\n| `get_shareholder_count` | Shareholder-count history per symbol | None* |\n| `get_lockup_expiry` | Restricted-share lockup release schedule | None* |\n| `get_sector_info` | Sector / industry constituents & performance | None* |\n| `get_research_reports` | Sell-side analyst research reports | None* |\n| `get_stock_news` | Market & company news headlines | None* |\n| `get_sec_filings` | SEC EDGAR filings (10-K/10-Q/8-K, etc.) | None |\n| `get_financial_statements` | Income / balance / cash-flow statements | None* |\n| `get_options_chain` | Options chain (strikes, IV, OI, Greeks) | None* |\n| `get_stock_profile` | Valuation, analyst estimates & institutional holdings (US/HK) | None |\n| `screen_market` | Market screener with fundamental/technical filters | None* |\n| `search_symbol` | Symbol / ticker search across markets | None |\n| `get_macro_series` | FRED macroeconomic series | FRED_API_KEY |\n| `get_fund_nav` | Chinese off-exchange fund NAV history | GILDATA_TOKEN |\n| `get_cn_macro_series` | China macro/industry EDB series (NL query) | GILDATA_TOKEN |\n| `get_cn_announcements` | A-share/HK/fund announcement search (NL query) | GILDATA_TOKEN |\n| `search_broker_reports` | Broker research search over the Juyuan library (NL query) | GILDATA_TOKEN |\n| `iwencai_search` | A-share natural-language research search | IWENCAI_KEY |\n| `qveris_search` | Search QVeris premium data/tool marketplace (free discovery) | QVERIS_API_KEY + paid mode |\n| `qveris_inspect` | Inspect QVeris tool schemas before executing (free) | QVERIS_API_KEY + paid mode |\n| `qveris_execute` | Execute a QVeris capability; budget-bounded, may be billable | QVERIS_API_KEY + paid mode |\n| `web_search` | Search the web via DuckDuckGo | None |\n| `read_url` | Fetch web page as Markdown | None |\n| `read_document` | Extract text from PDF/DOCX/XLSX/PPTX/images | None |\n| `write_file` | Write files (config, strategy code) | None |\n| `read_file` | Read file contents | None |\n| `read_run_artifact` | Structured run-artifact reads (rows / downsample / meta) | None |\n| `list_strategies` | Browse discoverable strategies (Alpha Zoo + SDM store) | None |\n| `query_strategies` | Evidence-gated query: regime / Sharpe / quality / cost filters | None |\n| `get_strategy_evidence` | Per-regime evidence rows for one strategy | None |\n| `adapt_strategy` | Prepare an evidence-gated child strategy for generation and its own backtest |\n| `refresh_strategy_evidence` | Rebuild the disposable strategy-evidence cache from run artifacts | None |\n| `analyze_trade_journal` | Parse broker CSV → profile + behavior diagnostics | None |\n| `extract_shadow_strategy` | Distill 3-5 if-then rules from profitable roundtrips | None |\n| `run_shadow_backtest` | Multi-market backtest + delta-PnL attribution | None* |\n| `render_shadow_report` | HTML/PDF shadow report (8 sections + charts) | None |\n| `scan_shadow_signals` | Today's symbols matching the shadow's cadence | None |\n| `list_swarm_presets` | List multi-agent team presets | None |\n| `run_swarm` | Execute a multi-agent research team | LLM key |\n| `get_swarm_status` | Poll swarm run status without blocking | None |\n| `get_run_result` | Get final report and task summaries | None |\n| `list_runs` | List recent swarm runs with metadata | None |\n| `reap_stale_runs` | Finalize stale swarm runs | None |\n| `retry_run` | Re-run a failed/stale swarm run | LLM key |\n| `trading_connections` | List selectable connector profiles | None |\n| `trading_select_connection` | Select the default connector profile | None |\n| `trading_check` | Check connector readiness | Connector app/OAuth |\n| `trading_account` | Read account summary from selected connector | Connector app/OAuth |\n| `trading_positions` | Read positions from selected connector | Connector app/OAuth |\n| `trading_orders` | Read open orders from selected connector | Connector app/OAuth |\n| `trading_quote` | Read a quote snapshot from selected connector | Connector app/OAuth |\n| `trading_history` | Read historical bars from selected connector | Connector app/OAuth |\n| `get_institutional_holdings` | SEC 13F-HR holdings by manager/ticker + quarter-over-quarter position diffs | None |\n| `etf_holdings` | ETF look-through — SEC N-PORT (US) and full-book A-share fund reports | None |\n| `prediction_market` | Event-contract search/market/history as labelled implied probability | None |\n| `research_papers` | arXiv + OpenAlex search/read with source-anchored claim extraction | None |\n| `quantlib_call` | Pure-compute finance math — 308 functions across 24 quantlib modules | None |\n| `cashflow_performance` | XIRR / MOIC / DPI / TVPI / TWR / Modified Dietz over dated cash flows | None |\n| `orderbook_depth` | Crypto L2 ladder — spread bps, depth imbalance, impact cost | None |\n| `sentiment` | Local lexicon text scoring + crypto Fear & Greed Index | None |\n| `technical_indicators` | RSI / MACD / Bollinger / SMA / EMA through the existing loaders | None* |\n| `get_fundamentals` | PIT-safe SEC fundamentals panels (filed-date anchored) | None |\n\n*A-share symbols require `TUSHARE_TOKEN`. HK/US/Canada/crypto are free. Trading connector rows use the selected connector profile, e.g. IBKR local TWS/Gateway or Robinhood MCP OAuth.\n\n## Quick Start\n\n```bash\npip install vibe-trading-ai\n```\n\nThat's it — no API keys needed for HK/US/Canada/crypto markets. Start using `backtest`, `get_market_data`, `analyze_options`, `analyze_trade_journal`, `extract_shadow_strategy`, `web_search`, the **Alpha Zoo** (`vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025`), and all 90 skills immediately.\n\n## Loading Tools from External MCP Servers\n\nThe built-in agent can load tools from your own external MCP servers in addition to its local toolset.\n\n> **Note:** This is the *MCP client* path — the opposite of the MCP plugin listed above. The plugin above makes Vibe-Trading's tools available to your agents. This section lets Vibe-Trading's own agent call tools from *your* servers.\n\n### Setup\n\nCreate `~/.vibe-trading/agent.json`:\n\n```json\n{\n \"mcpServers\": {\n \"my-server\": {\n \"command\": \"uvx\",\n \"args\": [\"my-mcp-server\"],\n \"toolTimeout\": 30,\n \"enabledTools\": [\"*\"]\n }\n }\n}\n```\n\nOrdinary external MCP tools appear automatically in every `vibe-trading run` / `vibe-trading chat` call. They are injected after local tools under stable names: `mcp__`. Live-broker MCP servers are consumed through the connector-scoped `trading_*` tools instead of exposing raw `mcp__*` tools to the agent.\n\n### Official IBKR MCP read-only probe\n\nAdd Interactive Brokers' official MCP endpoint as a read-only external server:\n\n```json\n{\n \"mcpServers\": {\n \"ibkr\": {\n \"type\": \"streamableHttp\",\n \"url\": \"https://api.ibkr.com/v1/api/mcp-public\",\n \"auth\": {\n \"type\": \"oauth\",\n \"scopes\": [\"mcp.read\"],\n \"clientName\": \"Vibe-Trading\",\n \"cacheDir\": \"~/.vibe-trading/live/ibkr/oauth\"\n },\n \"enabledTools\": [\"*\"]\n }\n }\n}\n```\n\nAuthorize it with `vibe-trading connector authorize ibkr-live-official-mcp-readonly`. The wildcard is accepted\nonly for this `mcp.read` probe. Generic `trading_account` and `trading_positions`\ncalls stay disabled until IBKR publishes stable read tool names that Vibe-Trading\ncan map safely; `mcp.write` requires an explicit tool allowlist and live\norder-guard handling. If IBKR issues a pre-registered OAuth client, add\n`clientId` and `clientSecret` inside `auth`.\n\n### Official eToro Public API MCP (discovery + dev)\n\neToro ships a hosted MCP at `https://mcp.public-api.etoro.com` with live OpenAPI\nroute discovery (`get-all-routes`, `get-route-spec`) and optional execution\n(`execute-read`, `execute-write`). Use it for **API exploration and codegen** —\nproduction agent trading in Vibe-Trading goes through the built-in `etoro-*`\nconnector profiles and `trading_*` / `etoro_*` tools (mandate gate on live writes).\n\nAdd to `~/.vibe-trading/agent.json` (credentials on the connection, not in chat):\n\n```json\n{\n \"mcpServers\": {\n \"etoro-public-api\": {\n \"type\": \"streamableHttp\",\n \"url\": \"https://mcp.public-api.etoro.com\",\n \"headers\": {\n \"x-api-key\": \"YOUR_PUBLIC_API_KEY\",\n \"x-user-key\": \"YOUR_USER_KEY\"\n },\n \"enabledTools\": [\"get-all-routes\", \"get-route-spec\", \"execute-read\"]\n }\n }\n}\n```\n\nOmit `execute-write` unless you want the MCP to place trades directly (bypasses\nVibe-Trading's live mandate gate). Install skill:\n`https://mcp.public-api.etoro.com/skill`\n\n### Trading connector profiles\n\nThe public trading surface is connector-first. Choose a connector profile, then\npaper/live is just an attribute under that connector.\n\n```bash\npip install \"vibe-trading-ai[ibkr]\"\nvibe-trading connector list\nvibe-trading connector use ibkr-paper-local\nvibe-trading connector configure ibkr-paper-local --yes\nvibe-trading connector check\nvibe-trading connector account\nvibe-trading connector positions\nvibe-trading connector orders\nvibe-trading connector quote AAPL\nvibe-trading connector history AAPL --duration \"30 D\" --bar-size \"1 day\"\n```\n\nLive-runner model spending is independent of mandate trade limits. Operators\nconfigure it through Runtime / the chat runner panel, or\n`vibe-trading connector runner-settings --profile robinhood-live-mcp`\n(`--file settings.json` saves settings while stopped). Defaults are one model\ndecision every 15 minutes, 26 decisions and 1,000,000 reported tokens per UTC day,\nwith fresh conversation context per decision. A once-daily time and IANA timezone\nare optional. Dollar limits require operator-supplied input/output prices and are\nestimates, not provider billing guarantees. Usage is checked after each response;\nbudget exhaustion or missing usage activates the existing kill switch. The agent\nmust not edit this operator state or raise its own spending limits.\n\nDefault ports are TWS paper `7497`, IB Gateway paper `4002`, TWS live-readonly\n`7496`, and IB Gateway live-readonly `4001`.\n\n### Config fields\n\n| Field | Required | Default | Description |\n|-------|----------|---------|-------------|\n| `type` | stdio: no, HTTP: yes | inferred only for stdio | Transport type. Use `sse` or `streamableHttp` for URL-based servers. |\n| `command` | stdio: yes | — | Executable to launch |\n| `args` | no | `[]` | Command arguments |\n| `env` | no | `{}` | Extra env vars for the subprocess |\n| `url` | HTTP: yes | — | Remote SSE / streamable HTTP endpoint URL |\n| `headers` | no | `{}` | Extra HTTP headers for SSE / streamable HTTP servers |\n| `toolTimeout` | no | `30` | Seconds before a tool call is cancelled |\n| `enabledTools` | no | `[\"*\"]` | Allowlist of remote tool names. `[\"*\"]` enables all |\n\nFor URL-based transports, `type` is required. The agent no longer guesses between SSE and streamable HTTP from the URL suffix.\n\n### Per-session override (API)\n\n> **Security — disabled by default.** `mcpServers` defines subprocess `command`/`args`/`env` and is therefore restricted to operator-level trust. API callers **cannot** inject MCP server definitions through `POST /sessions` unless the server operator explicitly opts in.\n\nTo enable session-level MCP injection, set the environment variable on the server before starting the agent:\n\n```bash\nexport ALLOW_SESSION_MCP_SERVERS=1\n```\n\nWith the opt-in active, pass `mcpServers` inside `session.config` to extend or replace the global config for that session only:\n\n```json\n{\n \"config\": {\n \"mcpServers\": {\n \"research\": {\n \"command\": \"uvx\",\n \"args\": [\"research-mcp\"],\n \"enabledTools\": [\"search\"]\n }\n }\n }\n}\n```\n\nWithout `ALLOW_SESSION_MCP_SERVERS=1`, any `mcpServers` key in `session.config` is silently stripped before config loading. The global operator config on disk (`~/.vibe-trading/agent.json`) is always respected regardless of this flag.\n\n### v1 limits\n\n- **Transport:** stdio, SSE, and streamable HTTP.\n- **Execution:** serial only. MCP tools never enter the parallel readonly path.\n- **Surfaces:** tools only. Resources and prompts are not exposed.\n- **Swarm:** MCP tools are excluded from Swarm worker registries in v1.\n- **Hot reload:** not supported. Restart the process to pick up config changes.\n\n### Failure handling\n\n| Case | Behavior |\n|------|----------|\n| Missing config file | falls back to empty config — no MCP servers loaded |\n| Invalid config file | logs a warning and falls back to empty config |\n| Server fails to start | that server is skipped; local tools and other servers still load |\n| Tool call times out | returns a normalized error payload instead of raising |\n| Two server names collide after sanitization | deterministic hash suffix appended; operator warning emitted |\n\n\n\n## Examples\n\n**Backtest a MACD strategy on Apple:**\n> Backtest AAPL with MACD crossover strategy (fast=12, slow=26, signal=9) for 2024\n\n**Analyze my trade journal and build a Shadow Account:**\n> Call analyze_trade_journal on ~/Downloads/tonghuashun.csv, then extract_shadow_strategy with min_support=3, then run_shadow_backtest for the last year, then render_shadow_report.\n\n**Run an investment committee review:**\n> Use run_swarm with investment_committee preset to evaluate NVDA. Variables: target=NVDA.US, market=US\n\n**Factor analysis on CSI 300:**\n> Run factor_analysis on CSI 300 stocks using pe_ttm factor from 2023 to 2024\n\n**Options analysis:**\n> Use analyze_options: spot=100, strike=105, 90 days, vol=25%, rate=3%\n\n**Multi-leg options payoff:**\n> Use analyze_options_payoff for a 95/105 bull call spread at spot 100 with 30 days remaining: long one 95 call at premium 8, short one 105 call at premium 3, multiplier 100, commission rate 0.001.\n","agent/src/skills/ashare-pre-st-filter/SKILL.md":"---\nname: ashare-pre-st-filter\ndescription: A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报，预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示，并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股，不预测财务造假。\ncategory: risk-analysis\n---\n\n> **依赖**：本 skill 必须配合 [tushare skill](../tushare/SKILL.md) 使用，缺数据时回退到 [akshare skill](../akshare/SKILL.md)。\n\n# A 股 ST/*ST 风险预测\n\n## 适用范围\n\n- 仅 A 股**主板 / 创业板 / 科创板**。港股、美股、加密、商品、**北交所**（`8xxxxx.BJ` 监管规则与本 skill 阈值不一致）均不适用，本 skill 直接拒绝。\n- 仅做\"下一财年是否会被 ST/*ST\"的前瞻预测，不做财务造假预测。\n- 输出双轴结果：**触线风险等级** + **预测可信度**。\n\n## 触发条件\n\n用户提问形如：\n- \"分析 000729.SZ 的 ST 风险\"\n- \"600xxx 下个年报会不会被 ST\"\n- \"帮我看一下 300xxx 是否有退市风险\"\n\n收到此类请求时，按下面\"分析流程\"执行；其他场景不要主动启用本 skill。\n\n## 数据获取规范（tushare 优先 → akshare 兜底）\n\n**铁律**：\n\n1. **所有财务/基本面/分红/审计/ST 状态数据，必须先调用 [tushare skill](../tushare/SKILL.md)**，按下表指定的接口名、`ts_code`、`period` 拉取。\n2. 当 tushare 接口因积分不足、token 缺失或返回空时，**回退到 [akshare skill](../akshare/SKILL.md)** 对应函数。\n3. **akshare 兜底数据必须在最终输出\"备注\"中显式标注**：`数据源：akshare（非官方聚合，不保证完整性，建议核对原始公告）`。**不允许只用 akshare 给出\"高可信度\"预测**——akshare 兜底的红线项可信度强制降一档（高→中高，中高→中，中→低）。\n4. 监管处罚（E2）走本 skill 自带的 [scripts/fetch_sina_penalties.py](scripts/fetch_sina_penalties.py)，不经过 tushare/akshare。\n\n### 数据需求映射表\n\n| 数据需求 | tushare 接口（首选） | 抓取参数 / 时间窗口 | akshare 兜底 | akshare 注意事项 |\n|---------|---------------------|-------------------|-------------|----------------|\n| 当前 ST 状态 | `stock_st` | 最近一个交易日 | `stock_zh_a_st_em()` | 仅返回当前 ST/退市整理板列表，历史不可查 |\n| 历史改名 | `namechange` | `ts_code` 全历史 | `stock_zh_a_new_em()`（仅新股，无改名） | akshare 无完整改名接口，必要时跳过 |\n| 股票基础信息（板块判断） | `stock_basic` | `list_status='L'` | `stock_individual_info_em(symbol)` | 字段名不同，需自行映射 |\n| 业绩预告 | `forecast` | `ts_code` + 最近 2 个 period（`{Y}0630`/`{Y}0930`/`{Y}1231`） | `stock_yjyg_em(date='YYYYMMDD')` | 按报告期日期反查，需先按 period 拉全市场再筛 ts_code |\n| 业绩快报 | `express` | 同上，最近 2 个 period | `stock_yjbb_em(date='YYYYMMDD')` | 同上，全市场快照需筛 |\n| 利润表 | `income` | `ts_code` + 最近 5 个 period（过去 4 年年报 `{Y}1231` + 最新中报/三季报） | `stock_financial_report_sina(stock='sh600000', symbol='利润表')` | 单位不一致（万元 vs 元），需做归一；季报缺失常见 |\n| 资产负债表 | `balancesheet` | 同上 | `stock_financial_report_sina(stock='sh600000', symbol='资产负债表')` | 同上 |\n| 现金流量表 | `cashflow` | 同上 | `stock_financial_report_sina(stock='sh600000', symbol='现金流量表')` | 同上 |\n| 财务指标（扣非净利润等） | `fina_indicator` | `ts_code` + 最近 5 个 period | `stock_financial_analysis_indicator(symbol='600000')` | 字段中文，扣非字段名为\"扣除非经常性损益后的净利润\" |\n| 审计意见 | `fina_audit` | `ts_code` + 最近 2 个年报 period | **akshare 无对应接口** | 缺失时在输出中标注\"审计意见数据缺失\"，**不得跳过**——必须提示用户去官网查 |\n| 分红 | `dividend` | `ts_code` + 近 5 年 `div_proc='实施'` | `stock_history_dividend_detail(symbol='600000', indicator='分红')` | 字段中文，需把\"派息\"金额×总股本得到现金分红总额 |\n| 日线行情（1 元退市预警） | `daily` | `ts_code` + 最近 30 个交易日 | `stock_zh_a_hist(symbol='600000', period='daily', adjust='')` | 必须用**不复权**价格判断 1 元退市线 |\n| 每日指标（市值） | `daily_basic` | `ts_code` + 最近一个交易日 | `stock_zh_a_spot_em()` 全市场快照筛 `代码=600000` | 字段名为\"总市值\"（**单位：元**，东方财富 push2 原始口径，akshare 不做缩放），换算成亿元需 `/1e8`。⚠️ 不要与 tushare `daily_basic.total_mv`（万元）混淆——本列是 akshare 兜底口径 |\n| 财报披露日期 | `disclosure_date` | `ts_code` + 当前年度 | **akshare 无对应接口** | 缺失时按经验估算（4/30 年报、8/31 中报、10/31 三季报） |\n| 监管处罚 | — | — | **走本 skill 自带 sina 脚本** | 见 E2 章节 |\n\n### 时间窗口口径统一\n\n按数据类型分别处理，**禁止用单一 60 天经验值统一对齐**：\n\n- **财务报表（income / balancesheet / cashflow / fina_indicator）**：以 `disclosure_date` 实际公告日为准；该接口不可用时按法定截止日（年报 4/30、中报 8/31、三季报 10/31、一季报 4/30）做兜底。在公告日 +1 天起方可视该 period 为\"最新可得\"。\n- **\"过去 N 年年报\"**：一律 `period = {Y}1231`，不要用日历年起止。\n- **业绩预告 forecast / 业绩快报 express**：只取 `ann_date ≥ 当前年度 1 月 1 日` 的记录；按 `ann_date` 倒序取最新一条，老 period 的预告对预测无效。\n- **dividend**：按 `(end_date, ann_date, cash_div_tax)` 三元组**强制去重**——tushare 同一笔分红会出现 3-4 行（预案/股东大会通过/实施），直接累加会三倍误算。\n\n## 第一步：当前状态核查（M0）\n\n**判断公司当前是否已被风险警示，并据此调整分析方向（不一刀切结束）**：\n\n调用顺序：\n1. 优先调用 `stock_st`（tushare skill，3000 积分），按当前最近交易日查询；记录 `type` / `type_name`。\n2. 如积分不足，回退到 `namechange`（免费）：拉取 `ts_code` 全部历史改名记录，取按 `start_date` 排序的最新一条；判断 `name` 前缀。\n3. 兜底用 `stock_basic.name` 做名称确认。\n\n**分支处理**：\n\n| 当前状态 | 分析方向 | 是否继续 |\n|---------|---------|---------|\n| 正常股票 | 预测下一财年是否会被 ST/*ST | **是**（默认流程） |\n| 已 ST（普通） | 改为预测：是否会进一步转 *ST 或退市；R1-R4 阈值需更严格（亏损链门槛降一年） | **是** |\n| 已 *ST | 改为预测：是否会被强制退市；重点看 R1 营收 + R2 净资产 + E1 审计意见 | **是** |\n| 已退市整理期 / 已摘牌 | 直接结束 | **否** |\n\n## 第二步：板块阈值表（决定红线）\n\n**先用 `ts_code` 前缀判断板块，再套用对应阈值。不要混用。**\n\n| 板块 | 代码识别 | 营收红线 | 市值退市线 | 三年累计分红阈值 |\n|------|---------|---------|-----------|----------------|\n| 主板 | `60xxxx.SH` / `00xxxx.SZ`（除 30 / 688 开头外） | 净利润为负 且 营收 < 3 亿 | 市值 < 5 亿 | 累计 < 5000 万 且 < 年均净利润 30% |\n| 创业板 | `30xxxx.SZ` | 净利润为负 且 营收 < 1 亿 | 市值 < 3 亿 | 累计 < 3000 万 且 < 年均净利润 30%（研发豁免：研发投入占营收 ≥ 5% 或近三年累计 ≥ 6000 万） |\n| 科创板 | `688xxx.SH` | 净利润为负 且 营收 < 1 亿 | 市值 < 3 亿 | 累计 < 3000 万 且 < 年均净利润 30%（研发豁免：研发投入占营收 ≥ 5% 或近三年累计 ≥ 6000 万） |\n\n## 第三步：预测时点选择\n\n按\"最新可得证据\"原则确定基准时点，从下到上择优：\n\n| 基准时点 | 数据接口 | 可信度基线 | 适用季节 |\n|---------|---------|-----------|---------|\n| **业绩预告 forecast** | `forecast` | **高** | 1 月底 / 7 月中旬 / 10 月底高发期 |\n| **业绩快报 express** | `express` | **高** | 1-4 月年报前夕 |\n| **三季报** | `income/balancesheet/cashflow` (`period=YYYYMMDD`，9 月末) | **中高** | 10 月底披露后到次年年报前 |\n| **中报** | 同上，6 月末 period | **中** | 8 月底披露后到三季报前 |\n| **去年年报** | 同上，12 月末 period | **低** | 仅作为基准对照 |\n\n**证据优先级**（同一指标多个来源时，高的覆盖低的）：\n\n```\nforecast / express > 三季报 > 中报 > 去年年报 > 经验外推\n```\n\n输出时**必须在每个预测项明示采用了哪一级证据**。如果存在 forecast 给出的全年净利润区间，**禁止再用三季报机械年化覆盖**。\n\n## 第四步：四项可预测红线\n\n### R1 营收 + 净利润红线\n\n**目标**：预测全年营收、归母净利润和**扣非净利润**，按监管口径\"扣非前后孰低者\"判定。\n\n**口径关键**：监管原文是 `min(n_income, profit_dedt) < 0` 且 `revenue < 板块阈值` —— **必须同时预测扣非净利润**，不能只用归母。仅看归母会漏掉\"归母为正但扣非为负\"的造壳公司。\n\n**预测方法（按基准时点选用，至少一种）**：\n\n- **forecast 直采法**：若 `forecast` 给出 `net_profit_min` / `net_profit_max`，直接取区间作为全年归母预测；扣非用过去两年扣非率（`profit_dedt / n_income` 中位数）折算；可信度=高。营收红线仍需结合最新报表外推。\n- **express 直采法**：若 `express` 已披露，`revenue` / `n_income` 即为全年快报值；扣非同样按过去两年率折算（express 一般无扣非字段）；可信度=高。\n- **Q4 单季回补法**（三季报基准）：\n - `Q1Q3_revenue` = 三季报营业收入\n - `Q4_revenue_est` = 过去两年 Q4 营收占全年比例的中位数 × 当前年化预估值\n - `revenue_full_year_est = Q1Q3_revenue + Q4_revenue_est`\n - `n_income` / `profit_dedt` 同法处理\n - 可信度=中高\n- **下半年情景外推法**（中报基准，强季节性公司必须用）：\n - 取过去两年 H2 营收占全年比例均值，给出悲观/基准/乐观三情景\n - `revenue_full_year_est = H1_revenue / H1_share_avg`\n - 可信度=中\n- **同比延续法**（中报基准，弱季节性公司可用）：\n - `revenue_full_year_est = H1_revenue × (last_year_full_revenue / last_year_H1_revenue)`\n - 可信度=中\n\n**禁止**：H1 直接乘 2、Q1-Q3 直接乘 4/3。任何机械年化**强制降到\"低\"可信度**并在输出中注明。\n\n**触线判定**（口径：`worst_profit = min(n_income_est, profit_dedt_est)`）：\n- 高风险：`worst_profit < 0` 且 `revenue_est < 板块阈值`\n- 中风险：`worst_profit < 0` 但营收达标；或营收逼近阈值（< 阈值 × 1.2）\n- 低风险：上述均不满足\n\n> **系数说明**：R1 中风险的 1.2 是预测项预警 buffer（预测本身有误差，需窄 buffer）；R4 高风险的 1.5 是双年联动判定 buffer（两年都亏且营收接近阈值才是真退市信号，需宽 buffer）。两个系数不同是有意为之，**不要错误地统一**。\n\n### R2 年末净资产红线\n\n**目标**：判断年末归母股东权益是否可能转负。\n\n**方法**：\n1. 取最新一期 `balancesheet.total_hldr_eqy_exc_min_int`（最新中报或三季报），记为 `current_eq`。\n2. 计算 `eq_year_end_est = current_eq + 剩余季度净利润预测 − 当前年度已实施现金分红总额`。\n - 剩余季度净利润：中报基准用 R1 的 H2 预测，三季报基准用 R1 的 Q4 预测。\n - 已实施分红总额：见 R3 的去重 + 量纲公式 `cash_div_tax / 10 × total_share`。\n\n**触线判定**（统一相对阈值，单位：元）：\n- 高风险：`current_eq < 0` 或 `eq_year_end_est < 0`\n- 中风险：`0 ≤ eq_year_end_est < 1e8`（1 亿元）\n- 低风险：`eq_year_end_est ≥ 1e8`\n\n**注意**：商誉减值、长期股权投资减值在 Q3/Q4 集中确认，应在备注提示\"若计提大额减值，净资产可能进一步下行\"，但**不得在没有公告依据时擅自减值**。\n\n### R3 分红达标前瞻\n\n**目标**：判断\"近三年累计现金分红 < 年均净利润 30% 且 < 板块累计阈值\"是否会在下一次年度考核时同时满足（监管原文：两条件**同时**触发才警示）。\n\n**单笔分红量纲公式**（**必须用此公式，不要用 `cash_div_tax × base_share`**）：\n\n```\ncash_dividend_total = cash_div_tax / 10 × total_share\n```\n\n其中 `cash_div_tax` 来自 `dividend`（每 10 股派现金额，含税），`total_share` 来自 `daily_basic.total_share`（最新总股本，单位万股 → 需 ×1e4 换成股）或 `stock_basic`。tushare `dividend.base_share` 字段对很多公司为 None，**禁止直接使用**。\n\n**方法**：\n1. 用 `dividend` 拉取该 `ts_code` 近五年记录，按 `(end_date, ann_date, cash_div_tax)` 三元组**强制去重**——同一笔分红 tushare 会返回\"预案/股东大会通过/实施\"3-4 行。\n2. 折算系数（按 `div_proc` 状态）：`实施=1.0` / `股东大会通过=0.7` / `预案=0.5`；同一 `end_date` 取系数最高那条。\n3. 用 `income` 拉取近三年完整年度归母净利润（period=YYYY1231）。\n4. 计算考核窗口 = `[当前年-2, 当前年]`，即\"过去两年已落地分红 + 本年度预计分红\"。\n5. 本年度预计分红：\n - 若 `dividend` 已查到本年度记录：按上面折算系数计入。\n - 否则用\"过去两年平均分红率\"× R1 预测全年净利润（基准情景）做估算，可信度降一档。\n6. 与年均净利润比较：\n - 条件 A：`cumulative_div_3y < 30% × avg_n_income_3y`\n - 条件 B：`cumulative_div_3y < 板块累计阈值`\n7. 创业板 / 科创板若研发投入占比满足豁免条件，备注提示\"研发达标可能豁免\"。\n\n**触线判定**：\n- 高风险：A 和 B 同时满足 **且** 过去三年至少一年净利润为正（即\"盈利年份未达标\"——监管以盈利年为基准）。\n- 中风险：仅一个条件满足；或本年度分红尚未公告。\n- 低风险：累计分红已稳定达标；或过去三年累计净利润 ≤ 0（无分红基础，按 R1 联动）。\n\n**禁止**：本年度尚未公告分红时，不得断言\"必然不达标\"，只能给出概率。\n\n### R4 连续亏损 / 扣非亏损链\n\n**目标**：判断是否会触发\"连续两年扣非前后净利润孰低者为负\" → *ST。\n\n**口径关键**：监管原文是**连续两年**（不是三年），且 *ST 财务类强制退市的法定条件是 **\"亏损 + 营收<板块阈值\" 必须同时满足**——只看亏损链会系统性高估风险（实证：闻泰科技 2024 年亏损但营收超千亿，不会被 *ST）。\n\n**方法**：\n1. 取过去一年完整年报的 `n_income`、`fina_indicator.profit_dedt`，记 `worst_last = min(n_income, profit_dedt)`、`revenue_last`。\n2. 用 R1 已经预测好的 `worst_profit_est` 和 `revenue_full_year_est`（基准情景），对应当前年度。\n3. 检查两年的 `worst < 0` 组合，**并叠加营收联动条件**。\n\n**触线判定**（双条件，亏损链 + 营收联动）：\n- 高风险：`worst_last < 0` 且 `worst_profit_est < 0`，**且**（R1 当前年命中高风险 **或** `revenue_full_year_est < 板块阈值 × 1.5`）——即两年均亏 + 营收处于退市风险区间。\n- 中风险：(a) 连续两年均亏但营收远超阈值（≥ 阈值 × 1.5）——亏损链成立但不会触发财务类 *ST，仍可能因经营恶化转 ST；(b) 仅 `worst_profit_est < 0`（当前年预测亏损，去年盈利，未形成连续）；(c) 仅 `worst_last < 0` 但当前年预测为正且接近 0（< 板块阈值 × 0.5）。\n- 低风险：`worst_last ≥ 0` 且 `worst_profit_est ≥ 0`。\n\n**为什么要叠加营收**：A 股 *ST 财务类强制退市的法定条件是\"扣非前后净利润孰低 < 0 **且** 营收 < 板块阈值\"，二者必须同时满足。仅亏损不达营收阈值的公司（如大型周期股短期亏损）会被市场和监管视作\"周期性亏损\"而非\"持续经营存疑\"。\n\n## 第五步：三项事实/临界证据面（不参与预测可信度）\n\n### E1 审计意见（事实）\n\n调用 `fina_audit`，取最近 2 个完整年报：\n- 高风险：最新年报 `audit_result` 含\"无法表示意见\"或\"否定意见\"；**或单次出现\"保留意见\"**（实证支持：上交所近年规范类 *ST 案例多为单次保留即触发）。\n- 中风险：连续两年同一负面结论但本期已修正；或带强调事项段的无保留意见涉及持续经营。\n- 低风险：标准无保留意见。\n\n**禁止预测下一份审计意见**——审计是事后行为。\n\n**akshare 兜底缺失时的人工核查路径**（`fina_audit` 无 akshare 替代接口，必须给出可点击 URL）：\n\n| 来源 | URL 模板 | 查询方式 |\n|------|---------|---------|\n| 巨潮资讯网（公告原文，权威） | `http://www.cninfo.com.cn/new/disclosure/stock?orgId=&stockCode={code6}` | 进入页面后筛选\"定期报告 → 年度报告\"，下载 PDF 在第十节\"财务报告 → 审计报告\"查 `审计意见类型` |\n| 巨潮高级搜索（按关键词） | `http://www.cninfo.com.cn/new/fulltextSearch/full?searchkey={code6}+审计报告&sdate=&edate=&isfulltext=false&sortName=time&sortType=desc` | 直接命中含\"审计报告\"标题的公告 |\n| 上交所信息披露 | `http://www.sse.com.cn/disclosure/listedinfo/regular/?stockCode={code6}` | 仅限 60xxxx / 688xxx |\n| 深交所信息披露 | `http://www.szse.cn/disclosure/listed/notice/index.html?stock={code6}` | 仅限 00xxxx / 30xxxx |\n\n其中 `{code6}` 为 6 位股票代码（去掉 `.SH` / `.SZ` 后缀）。**强制要求**：当 `fina_audit` 返回空时，输出报告的\"备注\"必须列出上面巨潮的两条 URL 之一，便于用户人工核查；不得只写一句\"审计意见数据缺失\"了事。\n\n### E2 监管处罚（事实，双窗口：上一财年 + 过去 12 个月）\n\n**这是本 skill 的关键证据面。**\n\n**窗口规则（双窗口策略，单次抓取在内存中切片）**：\n\n- 窗口 A — `[当前年-1 的 1 月 1 日, 当前年-1 的 12 月 31 日]`（上一个完整财年）：用于 reason_normalized 单条评级。\n- 窗口 B — `[分析日 - 365 天, 分析日]`（过去 12 个月滚动）：用于**频次增强规则**。\n- 若分析时点已经在 5 月之后且最新年报已披露完毕，窗口 A 可改为 `[最新年报 end_date - 365 天, 最新年报 end_date]`。\n\n**调用方式（推荐：单次拉全量 + 内存切片）**：\n\n```bash\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --no-filter\n```\n\n返回页面全量记录后，由 LLM 在内存中按窗口 A、窗口 B 切两份。也可以用 `--start-date / --end-date` 单独抓取，但同一次分析**避免发起两次 HTTP**。\n\n输出 JSON 列表，字段：`ann_date / event_type / title / reason / reason_normalized / content / issuer / issuer_normalized / subject_normalized / target_relevance / e2_countable / source_url`。\n\n**相关性铁律**：调用脚本时必须传入 `--stock-name {股票简称}`（来自 `stock_basic.name`），必要时用 `--alias` 补充曾用名/简称。新浪页面可能收录“某个人证券账户交易列表里出现目标股票”的记录，这类记录只说明目标股票被交易过，**不是上市公司/股东/董监高处罚**。脚本会将其标为 `target_relevance=security_mention_only` 且 `e2_countable=false`，E2 频次统计必须排除。\n\n**`subject_normalized` 字段**（用于频次权重折算）：\n- `company` — 处罚主体是公司本身（默认；不含个人身份关键词时归此类）\n- `shareholder` — 控股股东 / 实控人 / 5% 以上大股东\n- `officer` — 董事 / 监事 / 高管（董事长、总经理、董秘、财务总监等）\n\n**风险等级判定**：\n\n| 命中 `reason_normalized` | 等级贡献 |\n|---|---|\n| 财务造假 / 虚假陈述 / 信息披露违规 | 高（强烈预警） |\n| 违规担保 / 占用资金 | 高（直接触及 ST 红线） |\n| 内幕交易 / 市场操纵 / 违规减持 | 中 |\n| 其他/unknown | 低（仅作背景） |\n\n**频次增强规则（治理质量预警，窗口 B）**：\n\n监管函本身反映公司治理与内控质量，密集出现是\"规范类 ST\"（保留意见、内控非标、信披重大缺陷）的强领先指标。但**处罚主体不同，治理含义不同**：股东减持违规、董监高短线交易反映的是个人合规问题，不应等同于\"公司治理失序\"。\n\n**主体加权计数**（按 `subject_normalized` 折算后再查表）：\n\n```\n加权条数 = Σ 单条权重\n 其中：subject_normalized == \"company\" → 权重 1.0\n subject_normalized == \"officer\" → 权重 0.5\n subject_normalized == \"shareholder\" → 权重 0.5\n```\n\n| 过去 12 个月监管函/问询/警示函**加权条数** | 频次等级 |\n|---|---|\n| ≥ 5.0 | **极高** |\n| 3.0 - 4.5 | 高 |\n| 2.0 - 2.5 | 中 |\n| < 2.0 | 低 |\n\n**判定来源**：仅当 `e2_countable=true` 时，脚本输出的 `event_type ∈ {警示, 问讯, 监管关注, 监管函}` 或 `issuer_normalized ∈ {上交所, 深交所, 北交所, 证监会, 地方证监局}` 才计入条数；条数按 `subject_normalized` 折算后再查表。`target_relevance=security_mention_only` 的记录必须排除。\n\n**输出要求**：必须同时给出\"原始条数\"和\"加权条数\"两个数字，便于用户审阅折算逻辑。例如：`窗口 B 命中 6 条（公司 2 条 + 股东 3 条 + 高管 1 条 → 加权 4.0 条），频次等级：高`。\n\n**E2 综合等级合成规则**（解决单条评级与频次叠加的问题）：\n\n```\nE2_单条等级 = 窗口 A 内所有 reason_normalized 单条等级的最大值\nE2_频次等级 = 窗口 B 频次表查得\nE2_综合等级 = max(E2_单条等级, E2_频次等级)\n```\n\n**叠加加成**：若同时满足 `E2_频次等级 ≥ 高` 且 `E2_单条等级 ≥ 高`（含财务造假/虚假陈述/违规担保/占用资金）→ **直升极高**。\n\n**为什么要这条规则**：纯量化红线只能捕捉\"财务类 ST\"，无法预警\"规范类 ST\"。频次规则把\"治理失序\"作为前瞻信号纳入。\n\n**注意**：处罚证据**只抬升风险等级，不影响预测可信度**——这是事实证据，不是预测项。\n\n### E3 交易类临界预警（事实）\n\n调用 `daily`（近 30 个交易日，**必须 `adjust=''` 不复权**）和 `daily_basic`（最新一日，单位：万元）：\n\n**1 元退市预警**（用 `daily.close`，**不复权**）：\n- 高风险：近 20 个交易日中收盘价 < 1 元的天数 ≥ 10\n- 中风险：≥ 1 但 < 10\n- 低风险：0 天\n\n**市值退市预警**（用 `daily_basic.total_mv`，单位万元 → ×1e4 换算成元 → /1e8 换算成亿元）：\n\n| 板块 | 高风险阈值 | 中风险阈值 |\n|---|---|---|\n| 主板 | < 5 亿 | < 5 × 1.5 = 7.5 亿 |\n| 创业板 / 科创板 | < 3 亿 | < 3 × 1.5 = 4.5 亿 |\n\n## 第六步：双轴输出模板\n\n每次分析必须按下面模板输出。**风险等级 + 可信度两个维度都要给**。\n\n```markdown\n## ST 风险预测：{name}（{ts_code}）\n\n### 当前状态\n- 是否已 ST/*ST：{是 / 否}\n- 板块：{主板 / 创业板 / 科创板}\n- 基准预测时点：{forecast / express / 三季报（period） / 中报（period）}\n\n### 量化红线预测（双轴）\n\n| 红线 | 基准情景结论 | 风险等级 | 可信度 | 主要证据 |\n|------|-------------|---------|-------|---------|\n| R1 营收+净利润 | 全年营收预测 X 亿，净利润 Y 亿 | 高/中/低 | 高/中高/中/低 | forecast/三季报/... |\n| R2 年末净资产 | 当前 X 亿，年末预计 Y 亿 | 高/中/低 | 高/中高/中/低 | balancesheet + R1 |\n| R3 分红达标 | 三年累计预计 X，对应阈值 Y | 高/中/低 | 中/低 | dividend + income |\n| R4 连续亏损链 | 过去两年 + 当前年是否均亏 | 高/中/低 | 同 R1 | income + fina_indicator |\n\n### 事实证据面（不参与预测可信度）\n\n| 证据 | 结论 | 影响 |\n|------|------|------|\n| E1 审计意见 | 最新结论 | 抬升/无影响 |\n| E2 监管处罚（窗口 A 上一财年 / 窗口 B 过去 12 个月） | A 命中 Na 条（最高单条等级：…），B 原始 Nb_raw 条 / 加权 Nb_w 条（公司 a + 股东 b + 高管 c → a×1.0+b×0.5+c×0.5），频次等级：…，E2_综合等级：… | 抬升/无影响 |\n| E3 交易类临界 | 收盘价/市值情况 | 抬升/无影响 |\n\n### 综合结论\n- **下一财年被 ST/*ST 风险等级**：{极高 / 高 / 中 / 低}\n- **预测综合可信度**：{高 / 中高 / 中 / 低}\n- **关键风险点**（最多 3 条）：...\n- **缓解信号**（如有）：...\n\n### 备注\n- 数据时点：{所用最新报告期}\n- 已忽略的不可预测项：财务造假、未来审计意见\n- 季节性提醒（如适用）：...\n```\n\n## 禁止事项\n\n1. 不预测财务造假（事前不可知）。\n2. 不预测下一份审计意见（事后才出）。\n3. 不在没有公告依据时擅自计提减值。\n4. 不混用板块阈值（主板 vs 创业板/科创板的营收/市值/分红阈值不同）。\n5. 不把机械年化（H1×2、Q1Q3×4/3）伪装成\"高可信度\"。\n6. 不在港股 / 美股 / 加密 / **北交所**（`8xxxxx.BJ`，监管规则与本 skill 阈值不一致）上启用本 skill。\n7. 不把\"违规处罚记录\"计入预测可信度（仅抬升风险等级）。\n8. 不在 R3 分红计算中使用 `cash_div_tax × base_share`（base_share 经常为 None，会得 0）；必须用 `cash_div_tax / 10 × total_share`，且按 `(end_date, ann_date, cash_div_tax)` 三元组去重。\n9. 不在 R1 触线判定中只用 `n_income`；必须用 `min(n_income, profit_dedt)`，否则会漏掉造壳公司。\n10. 不把 R4 写成\"连续三年\"；监管原文是\"连续两年\"。**且 R4 高风险必须叠加营收联动条件**——仅\"连续两年亏损\"不构成 *ST 财务类强制退市，必须同时满足\"营收 < 板块阈值 × 1.5\"或 R1 已命中高风险，否则只能给中风险。\n11. 不把 E2 监管处罚\"按主体一刀切\"计数——必须用 `subject_normalized` 加权（公司 ×1.0 / 股东 ×0.5 / 董监高 ×0.5），否则股东动荡的公司会被系统性高估。\n\n## 依赖的 tushare 接口清单\n\n| 用途 | 接口 | 备注 |\n|------|------|------|\n| 当前 ST 状态 | `stock_st` | 3000 积分；不可用时回退 `namechange` |\n| 历史改名 | `namechange` | 免费 |\n| 板块判断 | `stock_basic` | 免费 |\n| 利润表 | `income` | 2000 积分，多 period 拉取 |\n| 资产负债表 | `balancesheet` | 2000 积分 |\n| 财务指标（扣非等） | `fina_indicator` | 2000 积分 |\n| 业绩预告 | `forecast` | 2000 积分 |\n| 业绩快报 | `express` | 2000 积分 |\n| 审计意见 | `fina_audit` | 500 积分 |\n| 分红 | `dividend` | 2000 积分 |\n| 日线 | `daily` | 免费 |\n| 每日指标（市值） | `daily_basic` | 免费 |\n| 财报披露日期 | `disclosure_date` | 500 积分 |\n\n## 自带脚本\n\n- [scripts/fetch_sina_penalties.py](scripts/fetch_sina_penalties.py) — 抓取新浪财经 vGP_GetOutOfLine 处罚页，stdlib 实现，无外部依赖；reason / issuer 标准化。\n\n调用方式：\n\n```bash\n# 单次拉全量（推荐：本 skill 双窗口策略由 LLM 在内存中切片）\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --no-filter\n\n# 或指定单一窗口\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --start-date 2025-01-01 --end-date 2025-12-31\n```\n\n正常情况下输出 JSON 到 stdout；网络/解析失败或参数非法时返回 `{\"source\": \"unavailable\", \"error\": \"...\"}` 到 stdout 并以非零退出码结束（不抛 traceback），便于 LLM 兜底处理。\n\n## 端到端调用伪代码\n\n用于减少 LLM 调用顺序漂移，按下列骨架组织：\n\n```python\nimport os, json, subprocess\nimport tushare as ts\n\nts_code = '000729.SZ'\npro = ts.pro_api(os.environ['TUSHARE_TOKEN'])\n\n# === Step 0: M0 当前状态 ===\nbasic = pro.stock_basic(ts_code=ts_code, fields='ts_code,name,market').to_dict('records')[0]\nst_hit = pro.stock_st() # 失败则回退 namechange\nname_chg = pro.namechange(ts_code=ts_code).sort_values('start_date', ascending=False)\n\n# === Step 1: 板块判断 ===\nboard = '科创板' if ts_code.startswith('688') else '创业板' if ts_code.startswith('30') else '主板'\nthreshold_revenue = 1e8 if board != '主板' else 3e8\nthreshold_mv = 3e8 if board != '主板' else 5e8\n\n# === Step 2: 拉财务（最新 5 期 + 过去 4 年年报） ===\nincome = pro.income(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nbalance = pro.balancesheet(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nfina_ind = pro.fina_indicator(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nforecast = pro.forecast(ts_code=ts_code).sort_values('ann_date', ascending=False)\nexpress = pro.express(ts_code=ts_code).sort_values('ann_date', ascending=False)\n\n# === Step 3: R1-R4 计算（按各章节口径，注意 min(n_income, profit_dedt)） ===\n# ... 略\n\n# === Step 4: E1 审计 ===\naudit = pro.fina_audit(ts_code=ts_code).sort_values('end_date', ascending=False).head(2)\n\n# === Step 5: E2 监管处罚（双窗口 + 主体加权）===\nfrom datetime import date, timedelta\nresult = subprocess.run([\n 'python', 'agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py',\n '--ts-code', ts_code, '--stock-name', basic['name'], '--no-filter',\n], capture_output=True, text=True)\npenalties = json.loads(result.stdout).get('records', [])\n\n# 在内存中切双窗口（避免两次 HTTP）\nthis_year = date.today().year\nwin_a_start, win_a_end = f'{this_year-1}-01-01', f'{this_year-1}-12-31'\nwin_b_start = (date.today() - timedelta(days=365)).isoformat()\nwin_b_end = date.today().isoformat()\n\ndef _in(rec, s, e):\n d = rec.get('ann_date') or ''\n return bool(d) and s <= d <= e\n\nwin_a = [r for r in penalties if _in(r, win_a_start, win_a_end)]\nwin_b = [r for r in penalties if _in(r, win_b_start, win_b_end)]\n\n# 窗口 A：单条等级取最高\nREASON_LEVEL = {\n '财务造假': 3, '虚假陈述': 3, '信息披露违规': 3,\n '违规担保': 3, '占用资金': 3,\n '内幕交易': 2, '市场操纵': 2, '违规减持': 2,\n}\nlevels_a = [REASON_LEVEL.get(r['reason_normalized'], 1) for r in win_a]\ne2_single = max(levels_a) if levels_a else 0\n\n# 窗口 B：原始条数 + 主体加权条数\nFREQ_EVENT_TYPES = {'警示', '问讯', '监管关注', '监管函', '警示函'}\nFREQ_ISSUERS = {'上交所', '深交所', '北交所', '证监会', '地方证监局'}\nSUBJECT_WEIGHT = {'company': 1.0, 'shareholder': 0.5, 'officer': 0.5}\n\nfreq_pool = [r for r in win_b\n if r.get('e2_countable', True)\n and (r['event_type'] in FREQ_EVENT_TYPES\n or r['issuer_normalized'] in FREQ_ISSUERS)]\nnb_raw = len(freq_pool)\nnb_weighted = round(sum(SUBJECT_WEIGHT.get(r['subject_normalized'], 1.0)\n for r in freq_pool), 1)\n\n# 频次等级（基于加权条数）\nif nb_weighted >= 5.0: e2_freq = 4 # 极高\nelif nb_weighted >= 3.0: e2_freq = 3 # 高\nelif nb_weighted >= 2.0: e2_freq = 2 # 中\nelse: e2_freq = 1 # 低\n\ne2_overall = max(e2_single, e2_freq)\n# 叠加加成：频次高 + 单条高同时命中 → 直升极高\nif e2_freq >= 3 and e2_single >= 3:\n e2_overall = 4\n\n# === Step 6: E3 交易临界 ===\ndaily = pro.daily(ts_code=ts_code).sort_values('trade_date', ascending=False).head(30)\ndaily_basic = pro.daily_basic(ts_code=ts_code).sort_values('trade_date', ascending=False).head(1).iloc[0]\ntotal_mv_yi = daily_basic['total_mv'] * 1e4 / 1e8 # 万元 → 亿元\n\n# === Step 7: 双轴合成输出 ===\n# 风险等级 = max(R1, R2, R3, R4, E1, E2_综合, E3)\n# 可信度 = 加权平均(R1, R2, R3, R4 各项可信度)，akshare 兜底项降一档\n```\n","agent/src/skills/ccxt/SKILL.md":"---\nname: ccxt\ncategory: data-source\ndescription: CCXT unified crypto exchange library (100+ exchanges). Free public market data. Fallback when OKX is unavailable.\n---\n\n## Overview\n\nCCXT is a unified cryptocurrency exchange trading library supporting 100+ exchanges including Binance, Bybit, OKX, Coinbase, Kraken, and more. Public market data (OHLCV, tickers, order books) requires no API key.\n\n- GitHub: https://github.com/ccxt/ccxt (35k+ stars)\n- Install: `pip install ccxt`\n\n## Quick Start\n\n```python\nimport ccxt\n\nexchange = ccxt.binance({\"enableRateLimit\": True})\n\n# Fetch daily OHLCV\nohlcv = exchange.fetch_ohlcv(\"BTC/USDT\", \"1d\", limit=100)\n# Returns: [[timestamp, open, high, low, close, volume], ...]\n\n# Fetch ticker\nticker = exchange.fetch_ticker(\"ETH/USDT\")\nprint(f\"ETH price: {ticker['last']}\")\n```\n\n## Key Methods\n\n| Method | Description | Returns |\n|--------|-------------|---------|\n| `fetch_ohlcv(symbol, timeframe, since, limit)` | Historical candles | `[[ts, o, h, l, c, v], ...]` |\n| `fetch_ticker(symbol)` | Latest quote | `{last, bid, ask, volume, ...}` |\n| `fetch_tickers(symbols)` | Batch quotes | `{symbol: ticker}` |\n| `fetch_order_book(symbol, limit)` | Order book | `{bids, asks, timestamp}` |\n| `fetch_trades(symbol, since, limit)` | Recent trades | `[{price, amount, side, timestamp}, ...]` |\n\n## Timeframes\n\n`1m`, `3m`, `5m`, `15m`, `30m`, `1h`, `2h`, `4h`, `6h`, `12h`, `1d`, `1w`, `1M`\n\nNote: not all exchanges support all timeframes. Use `exchange.timeframes` to check.\n\n## Symbol Format\n\nCCXT uses slash format: `BTC/USDT`, `ETH/BTC`, `SOL/USDT`\n\nThe project's DataLoader automatically converts `BTC-USDT` (hyphen) to `BTC/USDT` (slash).\n\n## Exchange Selection\n\nSet via environment variable: `CCXT_EXCHANGE=binance` (default)\n\nPopular exchanges: `binance`, `bybit`, `okx`, `coinbase`, `kraken`, `bitget`, `gate`\n\n## Built-in Loader\n\nThe project has a built-in CCXT DataLoader at `backtest/loaders/ccxt_loader.py`. It serves as a fallback when the OKX loader is unavailable.\n\n## Pagination\n\nFor long history, CCXT paginates via the `since` parameter (millisecond timestamp). The built-in loader handles this automatically (up to 200 pages).\n","agent/src/skills/candlestick/SKILL.md":"---\nname: candlestick\ndescription: Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores.\ncategory: strategy\n---\n# Candlestick Pattern Recognition\n\n## Purpose\n\nIdentifies 15 classic candlestick patterns and generates trading signals:\n\n### Single-Candle Patterns (5)\n| Pattern | Signal | Description |\n|------|------|------|\n| Hammer | Bullish | Long lower shadow with a small body at the top |\n| Inverted Hammer | Bullish | Long upper shadow with a small body at the bottom |\n| Shooting Star | Bearish | Long upper shadow with a small body at the bottom (appears after an uptrend) |\n| Doji | Neutral | Open and close are nearly equal |\n| Spinning Top | Neutral | Small body with roughly equal upper and lower shadows |\n\n### Double-Candle Patterns (5)\n| Pattern | Signal |\n|------|------|\n| Bullish Engulfing | Bullish |\n| Bearish Engulfing | Bearish |\n| Bullish Harami | Bullish |\n| Bearish Harami | Bearish |\n| Piercing Line | Bullish |\n| Dark Cloud Cover | Bearish |\n\n### Triple-Candle Patterns (4)\n| Pattern | Signal |\n|------|------|\n| Morning Star | Bullish |\n| Evening Star | Bearish |\n| Three White Soldiers | Bullish |\n| Three Black Crows | Bearish |\n\n## Signal Logic\n\nBullish patterns score +1, bearish patterns score -1. Go long when the total score is > 0, go short when it is < 0, and stand aside when it equals 0.\n\n## Parameters\n\n| Parameter | Default | Description |\n|------|--------|------|\n| body_pct | 0.1 | Threshold for body-to-range ratio in a doji |\n| shadow_ratio | 2.0 | Ratio of shadow length to body length |\n\n## Dependencies\n\n```bash\npip install pandas numpy requests\n```\n\n## Signal Convention\n\n- `1` = long, `-1` = short, `0` = stand aside\n","agent/src/skills/chanlun/SKILL.md":"---\nname: chanlun\ndescription: 基于缠论（缠中说禅）的形态识别引擎，使用czsc库自动检测K线分型、笔、中枢，并生成一买/一卖/二买/二卖/三买/三卖等买卖点信号。支持多周期分析和形态分类（3/5/7/9/11笔形态）。\ncategory: strategy\n---\n# 缠论形态识别\n\n## 用途\n\n基于**缠中说禅**理论的价格形态识别。缠论是一套完全基于价格结构的技术分析方法，核心链路：\n\n```\n原始K线 → 去包含处理 → 分型识别 → 笔检测 → 中枢构建 → 买卖点判定\n```\n\n适用于任何有 OHLCV 数据的市场（A股、加密货币、期货等）。\n\n## 核心概念\n\n| 概念 | 说明 | 详细文档 |\n| --- | --- | --- |\n| 分型（FX） | 顶分型：中间K线最高；底分型：中间K线最低 | [分型](references/核心概念/分型.md) |\n| 笔（BI） | 相邻顶底分型之间的一段走势，最小单元 | [笔](references/核心概念/笔.md) |\n| 中枢（ZS） | 至少3笔构成的价格重叠区域，趋势的核心 | [中枢](references/核心概念/中枢.md) |\n\n## 买卖点体系\n\n| 买卖点 | 含义 | 详细文档 |\n| --- | --- | --- |\n| 一买/一卖 | 趋势结束后的第一个反转信号（背驰点） | [一买一卖](references/买卖点/一买一卖.md) |\n| 二买/二卖 | 一买/一卖后回调不破底/顶的确认信号 | [二买二卖](references/买卖点/二买二卖.md) |\n| 三买/三卖 | 中枢上移/下移后回调不进入前中枢的信号 | [三买三卖](references/买卖点/三买三卖.md) |\n\n## 依赖安装\n\n```bash\npip install czsc requests pandas\n```\n\n## 快速上手\n\n```python\nfrom czsc import CZSC, RawBar, Freq\nfrom datetime import datetime\n\n# 准备 RawBar 列表（需按时间正序排列）\nbars = [\n RawBar(symbol=\"BTC-USDT\", id=0, dt=datetime(2026,1,1),\n freq=Freq.D, open=70000, close=71000,\n high=72000, low=69000, vol=1000, amount=71000000),\n # ... 更多K线\n]\n\n# 创建分析器，自动检测分型/笔/中枢\nc = CZSC(bars)\n\n# 访问结果\nprint(c.bi_list) # 已完成的笔\nprint(c.bars_ubi) # 未完成笔中的K线\n```\n\n## 可用信号函数（czsc.signals.cxt）\n\nczsc 内置 43 个缠论信号函数，核心如下：\n\n| 函数 | 说明 | 类型 |\n| --- | --- | --- |\n| `cxt_first_buy_V221126` | 一买信号 | 买卖点 |\n| `cxt_first_sell_V221126` | 一卖信号 | 买卖点 |\n| `cxt_second_bs_V230320` | 均线辅助二买二卖 | 买卖点 |\n| `cxt_third_bs_V230318` | 均线辅助三买三卖 | 买卖点 |\n| `cxt_third_buy_V230228` | 笔三买辅助 | 买卖点 |\n| `cxt_double_zs_V230311` | 两中枢组合判断BS1 | 中枢 |\n| `cxt_three_bi_V230618` | 三笔形态分类 | 形态 |\n| `cxt_five_bi_V230619` | 五笔形态分类 | 形态 |\n| `cxt_seven_bi_V230620` | 七笔形态分类 | 形态 |\n| `cxt_nine_bi_V230621` | 九笔形态分类 | 形态 |\n| `cxt_eleven_bi_V230622` | 十一笔形态分类 | 形态 |\n| `cxt_bi_base_V230228` | BI基础信号（方向/转折） | 基础 |\n| `cxt_bi_end_V230312` | MACD辅助笔结束 | 辅助 |\n| `cxt_range_oscillation_V230620` | 区间震荡判断 | 辅助 |\n| `cxt_zhong_shu_gong_zhen_V221221` | 大小级别中枢共振 | 中枢 |\n\n## 信号约定\n\n- 信号引擎输出：`1`=做多，`-1`=做空，`0`=观望\n- 做多条件：一买信号 或 三笔向上盘背 或 五笔类一买\n- 做空条件：一卖信号 或 三笔向下盘背 或 五笔类一卖\n- 中枢辅助：价格在中枢下沿附近做多优势，上沿附近做空优势\n\n## 数据格式\n\nczsc 接受 `List[RawBar]`，每个 RawBar 包含：\n\n| 字段 | 类型 | 说明 |\n| --- | --- | --- |\n| symbol | str | 标的代码 |\n| id | int | 序号（从0开始） |\n| dt | datetime | 时间 |\n| freq | Freq | 频率：`Freq.F1/F5/F15/F30/F60/D/W/M` |\n| open | float | 开盘价 |\n| close | float | 收盘价 |\n| high | float | 最高价 |\n| low | float | 最低价 |\n| vol | float | 成交量 |\n| amount | float | 成交额 |\n\n## 实现方式\n\n使用 [czsc](https://github.com/waditu/czsc) 库（v0.9.68+），基于纯 Python 实现（可选 Rust 加速后端）。支持增量更新，适合实时分析。\n","agent/src/skills/bottleneck-hunter/SKILL.md":"---\nname: bottleneck-hunter\ncategory: strategy\ndescription: \"Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.\"\n---\n\n# Supply-Chain Bottleneck Hunter\n\nDecompose a super-trend (user-specified, e.g. \"AI infrastructure\", \"energy transition\") into its physical supply chain and hunt for bottleneck-arbitrage opportunities.\n\n## Core Idea\n\nDon't ask \"which AI stock to buy\" — ask \"if this trend keeps expanding, which link runs out first?\"\n\nTraditional research chases leaders and known tracks. This skill inverts: **start from the choke points of the physical supply chain and find companies nobody notices but that the whole industry must wait on when they run short.**\n\nThe edge: Layer-1 bottlenecks (GPU, HBM, power) are already priced in. The real alpha is in **Layer 2 and Layer 3** — optical modules, lasers, InP substrates, SOI wafers, epitaxy equipment, wafer-level test, IC substrates, specialty fiberglass.\n\n## Step 1: Super-Trend Confirmation\n\n### Trend Filter Criteria\n\n| Criterion | Requirement | How to verify |\n|-----------|-------------|---------------|\n| Durability | ≥3-5 years of certain growth | Search industry forecasts, capex plans |\n| Physicality | Needs real hardware/material/equipment build | Distinguish \"software upgrade\" from \"physical expansion\" |\n| Scale | Global capex >$50B/year | Search top players' capex guidance |\n| Acceleration | Demand growth > supply expansion | Compare demand growth vs capacity plans |\n\nUse `web_search` to verify. Reference super-trends: AI infrastructure, energy transition (nuclear/grid/storage), defense modernization, semiconductor reshoring, space economy.\n\n## Step 2: Physical Supply-Chain Decomposition\n\nDon't stop at concepts — decompose to physical entities.\n\n```\nLayer 0 (end): final product/service\nLayer 1 (core component): already closely watched → priced in, limited alpha\nLayer 2 (sub-component/material): low attention, alpha-rich\nLayer 3 (upstream equipment/raw material)\nLayer 4 (infrastructure): power, cooling, land, talent, certifications\n```\n\n### AI Infrastructure Example\n\n```\nLayer 0: AI model training/inference services\nLayer 1: GPU/accelerators, HBM, servers, data centers\nLayer 2 (focus zone):\n - Network interconnect: optical modules, fiber, switch ASICs, copper cables\n - Optical comms core: lasers (EML/VCSEL/CW), modulators, photodetectors\n - Semiconductor materials: InP substrates, GaAs substrates, SOI wafers, SiC substrates\n - Advanced packaging: CoWoS interposers, HBM TSV, ABF substrate film\n - PCB/substrate: high-frequency PCB, IC substrates, specialty fiberglass\n - Test: wafer-level test (probe cards), burn-in, ATE\n - Thermal/cooling: liquid cooling, CDU, immersion fluid\n - Power connection: busbars, UPS, distribution, transformers\nLayer 3: epitaxy equipment (MOCVD/MBE), lithography/etch, high-purity metals (In/Ga/Ge), specialty gases, sputtering targets, certifications (MSA/Telcordia)\nLayer 4: power (nuclear/gas/transmission), cooling water, data-center land/permits\n```\n\nFor other trends, use `web_search` with queries like `{trend} supply chain bottleneck`, `{trend} shortage critical component`, `{trend} capacity constraint`, `{trend} sole source supplier`.\n\n## Step 3: Bottleneck Identification — Finding \"Choke Points\"\n\nFor each Layer 2-3 link, evaluate 6 criteria:\n\n| # | Criterion | Question | Score |\n|---|-----------|----------|-------|\n| 1 | Supply concentration | ≤3 global suppliers? | 🔴 ≤2 / 🟡 3-5 / 🟢 >5 |\n| 2 | Expansion lead time | How long to add capacity? | 🔴 >2y / 🟡 1-2y / 🟢 <1y |\n| 3 | Substitutability | Can other tech/material replace it? | 🔴 irreplaceable / 🟡 partial / 🟢 easy |\n| 4 | Capacity utilization | Current utilization? | 🔴 >90% / 🟡 70-90% / 🟢 <70% |\n| 5 | Demand growth | Downstream demand growth? | 🔴 >50%/yr / 🟡 20-50% / 🟢 <20% |\n| 6 | Customer qualification cycle | How long for a new supplier to qualify? | 🔴 >1y / 🟡 6-12m / 🟢 <6m |\n\n**Bottleneck grade**: 🔴×≥4 → **S-grade** (single-point failure, highest priority); 🔴×3 → **A-grade** (severely constrained); 🔴×1-2 → **B-grade** (stressed but manageable); no 🔴 → not a bottleneck, skip.\n\n## Step 4: Company Screening — From Bottleneck to Tickers\n\nFor each S/A-grade bottleneck, use `web_search` / `screen_market` to find listed companies.\n\n### Initial Screen\n\n| Criterion | Requirement |\n|-----------|-------------|\n| Listing status | Listed (A/HK/US/JP/TW/EU) |\n| Bottleneck revenue share | >30% of revenue from the bottleneck link |\n| Market cap | Prefer <$10B (large caps already priced) |\n| Liquidity | Average daily turnover >$1M |\n\n### Valuation Gate (mandatory, never skip)\n\n**A real bottleneck ≠ an investment opportunity.** For every company, compute PE/PB/ROE/FCF yield with `financial_rigor` (`command=verify_valuation`), and run `financial_rigor` (`command=three_scenario`) for scenario valuation:\n\n- **Red light** (any one → signal strength capped at ★★, flag \"valuation stretched\"): market cap >20% of TAM; PS>30x with revenue growth <100%; market cap >10× 5-year optimistic revenue forecast; stock doubled within 60 days of a follow-on offering.\n- **Yellow light** (needs extra justification, else downgrade): loss-making + PS>15x; PS >5× a profitable peer; PE>80x (compute PEG).\n- **Green light** (bonus): PS<10x with revenue growing; PE<30x with a moat (flag \"margin of safety\").\n\n**Sanity check (mandatory)**: with `financial_rigor` (`command=three_scenario`), answer — \"buying at current market cap, if the most optimistic scenario fully plays out and I exit at 25× PE in 10 years, what's the annualized return?\" <10%/yr → flag \"no margin of safety at current price\".\n\n## Step 5: Cross-Validation — Don't Trust a Single Story\n\n### Positive checks\n\n| Check | Question |\n|-------|----------|\n| Customer validation | Have top customers signed/imported? (check announcements, customer filings) |\n| Revenue validation | Is the bottleneck already showing in revenue growth? (last 2-3 quarters) |\n| Price validation | Is the product raising price? (industry quotes, analyst reports) |\n| Capacity validation | Is capacity really tight? (lead times, customer complaints) |\n| Capital validation | Is there expansion capex? (company guidance) |\n\nUse `get_financial_statements` / `get_stock_news` / `web_search`.\n\n### Reverse checks (Munger inversion)\n\n- Why don't smart people buy this stock?\n- Can the bottleneck be bypassed? Alternative routes?\n- Can China / other players quickly replicate capacity?\n- If end-demand drops 50%, what happens to this company?\n- Has management diluted at highs before?\n- What growth assumption does the current valuation imply?\n\n## Step 6: Output — Bottleneck Opportunity Board\n\n### Ranking Table\n\n| Rank | Company | Ticker | Mkt Cap | Revenue | PS | PE | Bottleneck link | Grade | Share | Growth | Signal | Valuation |\n|------|---------|--------|---------|---------|-----|-----|-----------------|-------|-------|--------|--------|-----------|\n\n**Market cap, revenue, PS, PE are mandatory** — never skip with \"TBD\". If financials can't be obtained, signal strength ≤★★.\n\nSignal strength (valuation gate directly affects):\n- ★★★★★ multi-cross-validated + customers imported + revenue confirmed + valuation green\n- ★★★★ most checks pass + valuation green/yellow (with explanation)\n- ★★★ logic holds but parts unverified + valuation yellow acceptable\n- ★★ early signal, or logic holds but valuation red\n- ★ pure concept, unverified\n\nAfter drafting, run `report_audit` (`command=extract` → verify each point → `command=verdict`) as a quality gate to ensure no hallucinated numbers.\n\n### One-Pager Template\n\n```\n🎯 {Company} ({Ticker}) — {one-line bottleneck positioning}\n\nWhy it's a bottleneck: (2-3 sentences)\nWhy this company: (2-3 sentences)\n\nCatalyst timeline:\n- Near-term (1-3m): [earnings / capacity / customer win]\n- Mid-term (3-12m): [industry trend / expansion node]\n\nKey risks: 1. 2.\n\nKey data: market cap / revenue / PS / PE / growth / bottleneck revenue share\nMargin of safety: 10y 25× PE exit method, annualized return XX%. Conclusion: yes/no.\n\nCross-validation status: ✅ customer / ✅ revenue / ⚠️ valuation stretched / ❌ unverified\n\nConclusion: deep research / watchlist / skip\n```\n\nSave with `write_file` to the reports directory (e.g. `reports/bottleneck-map/{trend}-bottleneck-{YYYYMMDD}.md`).\n\n## Step 7: Inventory Update — Maintain the Bottleneck Map\n\nOn each run: ① re-check identified bottlenecks (new suppliers? capacity expanded? substitute breakthrough?); ② scan new bottlenecks (`web_search` last 7 days supply chain / shortage / bottleneck news); ③ update grades (upgrade/downgrade/relieve).\n\n## AI Research Bias Self-Check\n\n| Bias | Symptom | Counter |\n|------|---------|---------|\n| Leader-bias | Search dominated by large caps | Deliberately search small-cap suppliers, add \"small cap\" |\n| English-bias | Miss JP/KR/TW players | Must search JP/KR/TW market suppliers |\n| Narrative-bias | Drawn to \"AI concept\" labels | Look only at actual supply-chain position, not market labels |\n| Confirmation-bias | After finding a bottleneck, only seek positive evidence | Force Step 5 reverse checks |\n| Recency-bias | Rely on stale info | Prefer last 30 days of data |\n\n## Core Principles (highest priority)\n\n1. **Don't ask the AI to recommend stocks — ask it to decompose supply chains.** The question matters more than the answer.\n2. **Physical first** — only links that need real physical product/material/equipment.\n3. **Layer 2 and Layer 3** — don't chase already-priced leaders.\n4. **Cross-validate** — every conclusion needs ≥2 independent sources.\n5. **Be honest about uncertainty** — if data is missing, say so; don't fill with speculation.\n6. **Bottlenecks are temporary** — every bottleneck gets resolved; the key is timing the window.\n7. **Small cap ≠ good opportunity** — a small cap can also be a bad company; it must pass financial quality.\n8. **A real bottleneck ≠ an investment** — at PS>30x or still loss-making, the current price is not a buy. **Valuation is a hard gate that cannot be overridden by bottleneck purity, signal strength, or narrative appeal.** Better to miss a bottleneck stock that ran than buy a loss-making company at 100× sales.\n","agent/src/skills/behavioral-finance/SKILL.md":"---\nname: behavioral-finance\ndescription: \"Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.\"\ncategory: analysis\n---\n\n# Behavioral Finance Applications\n\n## Overview\n\nTranslate behavioral-finance theory into quantifiable trading signals and risk-control rules. Core assumption: market participants systematically deviate from rational decision-making, and these biases can be predicted and exploited.\n\nApplicable scenarios:\n- Behavioral interpretation and parameter optimization for momentum / reversal strategies\n- Contrarian signals when market sentiment becomes extreme\n- Debiasing mechanisms in portfolio construction\n- Capturing behavior patterns specific to retail-driven China A-share markets\n\n## Core Concepts\n\n### Overreaction and Underreaction\n\n**Underreaction** → momentum effect:\n```\nMechanism: anchoring bias + conservatism\n Investors anchor on old information and update insufficiently to new information\n After an earnings beat, the stock price digests it gradually rather than all at once\nChina A-share evidence:\n - Earnings-guidance beats still produce 3-5% excess return over the following 20 days\n - After analyst rating upgrades, momentum often persists for 1-3 months\nQuant signal:\n SUE (standardized unexpected earnings) > 2σ -> buy and hold for 60 days\n Top 10% 20-day return -> continue holding for 20 days (China A-share momentum cycles are shorter)\n```\n\n**Overreaction** → reversal effect:\n```\nMechanism: representativeness heuristic + availability bias\n Investors extrapolate recent trends too aggressively and ignore mean reversion\n Panic / euphoria drives reactions beyond what fundamentals support\nChina A-share evidence:\n - Rebounds after consecutive limit-downs (after 3 limit-downs, the average 20-day rebound is 8%)\n - Big annual losers often earn 5-10% excess return the next year\nQuant signal:\n Bottom 10% of 250-day return -> buy and hold for 250 days\n RSI(5) < 10 -> short-term rebound signal (5-10 days)\n```\n\n**Key distinction**:\n| Dimension | Underreaction (Momentum) | Overreaction (Reversal) |\n|------|------------------|------------------|\n| Time scale | 1-12 months | <1 week or >12 months |\n| Information type | Clear events (earnings / announcements) | Ambiguous information (sentiment / trend) |\n| Best China A-share window | 20-60 days | 5-10 days (short term) / 1 year (long term) |\n\n### Cognitive Bias Checklist\n\n**Individual decision biases**:\n| Bias | Manifestation | Quant Detection | Debiasing Strategy |\n|------|------|----------|------------|\n| Loss aversion | Hold losing stocks, sell winners too early | Holding period: losing positions > winning positions by 2-3x | Pre-set stop-loss line and execute mechanically |\n| Overconfidence | Overtrading, concentrated positions | Monthly turnover > 100%, single-stock weight > 30% | Limit the number of trades per month |\n| Anchoring effect | Anchoring to entry price / historical highs | Abnormal volume expansion near the entry price | Use relative valuation instead of absolute price |\n| Confirmation bias | Focus only on information that supports the existing view | Single-source information, ignoring bearish news | Force reading the opposing view |\n| Recency bias | Overweight recent events | Recent gains/losses have too much influence on position size | Lengthen the evaluation window (≥60 days) |\n| Framing effect | Same information framed differently leads to different decisions | Decision differences between return format and absolute-PnL format | Evaluate consistently in return space |\n\n**Group behavior biases**:\n| Bias | Manifestation | China A-share Characteristics | Quant Indicator |\n|------|------|---------|----------|\n| Herding | Chasing rallies and panic-selling together | Extremely fast sector rotation (3-5 days) | Intra-sector stock correlation > 0.8 |\n| Information cascades | Ignoring private information and following public signals | Sector follow-through after a leader stock hits limit-up | Sector return on the day after leader-stock limit-up |\n| Attention effect | Buying stocks that attract attention | Explosive turnover in limit-up / news-driven stocks | Abnormal turnover > 3x average |\n\n### Investor Sentiment Cycle\n\n```\nFear -> Caution -> Optimism -> Excitement -> Euphoria -> Denial -> Panic -> Fear\n | | | | | | |\n Bottom Recovery Mid-uptrend Pre-top Top Early selloff Pre-bottom\n\nQuant sentiment indicators:\n 1. Closed-end fund discount: discount > 15% -> extreme fear -> buy signal\n 2. Margin-financing growth: monthly growth > 20% -> euphoria -> reduce position\n 3. New account openings: weekly openings > 2x average -> overheated market\n 4. Turnover ratio: All-A daily turnover > 3% -> euphoric; < 0.5% -> deeply depressed\n 5. Number of limit-up stocks: > 100 -> euphoric; < 10 -> weak\n```\n\n## Analysis Framework\n\n### 1. Disposition-Effect Signal\n\n**Principle**: investors tend to sell winners and hold losers. Once winning positions are largely cleared, selling pressure eases; when trapped holders are deeply underwater, selling pressure can also ease.\n\n```\nChina A-share application:\n Compute the profit ratio in the chip-distribution structure:\n - Profit ratio > 90% and shrinking volume -> winners are reluctant to sell -> may continue rising\n - Profit ratio > 90% and expanding volume -> winners are exiting -> topping signal\n - Profit ratio < 10% and shrinking volume -> low willingness to cut losses -> bottom stabilization\n - Profit ratio < 10% and expanding volume -> panic selling -> short-term oversold\n\nQuant implementation:\n capital_gain_overhang = (current_price - avg_cost) / avg_cost\n where avg_cost is approximated by 60-day VWAP\n CGO > 0.2 -> strong unrealized gains, watch for disposition-effect selling pressure\n CGO < -0.3 -> deeply trapped holders, selling pressure may actually ease\n```\n\n### 2. Composite Sentiment Indicator\n\n```python\n# Multi-dimensional sentiment score (0-100, 50 = neutral)\nsentiment_components = {\n 'turnover_ratio': normalize(all_a_turnover, historical_percentile), # weight 25%\n 'margin_growth': normalize(monthly_margin_growth, historical_percentile), # weight 25%\n 'new_high_ratio': normalize(new_high_ratio, historical_percentile), # weight 20%\n 'limit_up_count': normalize(limit_up_count, historical_percentile), # weight 15%\n 'fund_discount': normalize(closed_end_fund_discount, historical_percentile), # weight 15% (inverse)\n}\n\nsentiment_score = weighted_sum(components)\n# > 80: extreme greed -> cut exposure below 60%\n# 60-80: optimistic -> maintain normal exposure\n# 40-60: neutral -> keep exposure unchanged\n# 20-40: pessimistic -> add gradually\n# < 20: extreme fear -> increase exposure above 80%\n```\n\n### 3. Behavioral Optimization of Momentum Strategies\n\nTraditional momentum (sorting by past 12-month returns) is unstable in China A-shares. A behavioral-finance perspective suggests the following optimizations:\n\n```\nOptimization 1: Separate sentiment momentum from fundamental momentum\n Sentiment momentum = part of recent price rise with no fundamental support -> short-term reversal\n Fundamental momentum = price rise consistent with earnings revisions -> can persist\n Trade: buy stocks with \"strong fundamental momentum + weak sentiment momentum\"\n\nOptimization 2: Attention-weighted momentum\n High-attention retail names reverse faster\n Indicator: if abnormal turnover > 3x average, cut momentum holding period by 50%\n Example: if a normal momentum basket holds for 60 days, high-attention names hold only 30 days\n\nOptimization 3: Combine cross-sectional momentum and time-series momentum\n Cross-sectional: relative strength (top 20% in return ranking)\n Time-series: absolute trend (price > MA60)\n Both satisfied -> strong signal; only one satisfied -> half position\n```\n\n### 4. Contrarian Trading Signals\n\n```\nExtreme-fear buy conditions (at least 3 items):\n □ Shanghai Composite RSI(5) < 15\n □ All-A daily turnover < 0.5%\n □ Weekly margin-financing decline > 5%\n □ Limit-up count < 10 and limit-down count > 50\n □ Closed-end fund discount > 15%\n\nExtreme-greed sell conditions (at least 3 items):\n □ Shanghai Composite RSI(5) > 90\n □ All-A daily turnover > 3%\n □ Weekly margin-financing growth > 10%\n □ Limit-up count > 150\n □ Weekly increase in new account openings > 100%\n```\n\n## Output Format\n\nBehavioral-finance analysis report:\n```\n=== Market Sentiment Diagnosis ===\nDate: 2026-03-28\nSentiment score: 72/100 (optimistic bias)\nCurrent phase: transition from optimism to excitement\n\n=== Behavioral-Bias Signals ===\nOverreaction detection: 127 stocks rose > 15% in the past 5 days -> 65% probability of short-term reversal\nDisposition effect: winner-clearing ratio is low (35%) -> overhead selling pressure remains\nHerding effect: sector correlation 0.85 -> severe follow-the-leader behavior, divergence likely soon\n\n=== Strategy Recommendations ===\nMomentum strategy: shorten holding period from 60 days to 30 days (market attention is elevated)\nContrarian signal: not triggered (sentiment is not yet extreme)\nPosition suggestion: maintain 70% exposure, and prioritize names with \"strong fundamental momentum + weak sentiment momentum\"\n\n=== Debiasing Checklist ===\n□ Are you overconfident because of recent profits? -> check position concentration\n□ Are you anchored to your entry price? -> re-evaluate using current PE/PB\n□ Are you ignoring bearish information? -> force yourself to read bearish research reports\n```\n\n## Notes\n\n1. **High retail participation in China A-shares**: behavioral-bias signals are more pronounced than in US equities, but sector rotation is also faster, so momentum windows should be shorter\n2. **Lag in sentiment indicators**: margin-financing balance is released T+1, and new account openings are weekly, so they are not suitable for intraday trading\n3. **Structural changes**: after 2019, foreign capital and quant participation rose, so the effectiveness of traditional behavioral-finance signals may have weakened\n4. **Behavioral factors correlate with traditional factors**: disposition-effect factors correlate about 0.3-0.5 with momentum, so control collinearity\n5. **Overfitting risk**: behavioral stories are easy to explain after the fact, so out-of-sample validation is mandatory\n6. **Extreme sentiment is rare**: extreme fear / greed appears only 2-3 times per year, so strategy capacity is limited\n\n## Dependencies\n\n```bash\npip install pandas numpy scipy\n```\n","agent/src/skills/commodity-analysis/SKILL.md":"# commodity-analysis\n\nPath: `agent/src/skills/commodity-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/commodity-analysis/SKILL.md)","agent/src/skills/convertible-bond/SKILL.md":"# convertible-bond\n\nPath: `agent/src/skills/convertible-bond/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/convertible-bond/SKILL.md)","agent/src/skills/corporate-events/SKILL.md":"# corporate-events\n\nPath: `agent/src/skills/corporate-events/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/corporate-events/SKILL.md)","agent/src/skills/correlation-analysis/SKILL.md":"# correlation-analysis\n\nPath: `agent/src/skills/correlation-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-analysis/SKILL.md)","agent/src/skills/correlation-regime/SKILL.md":"# correlation-regime\n\nPath: `agent/src/skills/correlation-regime/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-regime/SKILL.md)","agent/src/skills/credit-analysis/SKILL.md":"# credit-analysis\n\nPath: `agent/src/skills/credit-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/credit-analysis/SKILL.md)","agent/src/skills/cross-market-strategy/SKILL.md":"# cross-market-strategy\n\nPath: `agent/src/skills/cross-market-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/cross-market-strategy/SKILL.md)","agent/src/skills/crypto-derivatives/SKILL.md":"# crypto-derivatives\n\nPath: `agent/src/skills/crypto-derivatives/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/crypto-derivatives/SKILL.md)","agent/src/skills/data-routing/SKILL.md":"# data-routing\n\nPath: `agent/src/skills/data-routing/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/data-routing/SKILL.md)","agent/src/skills/deep-company-series/SKILL.md":"# deep-company-series\n\nPath: `agent/src/skills/deep-company-series/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/deep-company-series/SKILL.md)","agent/src/skills/defi-yield/SKILL.md":"# defi-yield\n\nPath: `agent/src/skills/defi-yield/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/defi-yield/SKILL.md)","agent/src/skills/dividend-analysis/SKILL.md":"# dividend-analysis\n\nPath: `agent/src/skills/dividend-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/dividend-analysis/SKILL.md)","agent/src/skills/doc-reader/SKILL.md":"# doc-reader\n\nPath: `agent/src/skills/doc-reader/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/doc-reader/SKILL.md)","agent/src/skills/earnings-forecast/SKILL.md":"# earnings-forecast\n\nPath: `agent/src/skills/earnings-forecast/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/earnings-forecast/SKILL.md)","agent/src/skills/earnings-revision/SKILL.md":"# earnings-revision\n\nPath: `agent/src/skills/earnings-revision/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/earnings-revision/SKILL.md)","agent/src/skills/eastmoney/SKILL.md":"# eastmoney\n\nPath: `agent/src/skills/eastmoney/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/SKILL.md)","agent/src/skills/edgar-sec-filings/SKILL.md":"# edgar-sec-filings\n\nPath: `agent/src/skills/edgar-sec-filings/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/edgar-sec-filings/SKILL.md)","agent/src/skills/elliott-wave/SKILL.md":"# elliott-wave\n\nPath: `agent/src/skills/elliott-wave/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/SKILL.md)","agent/src/skills/etf-analysis/SKILL.md":"# etf-analysis\n\nPath: `agent/src/skills/etf-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/etf-analysis/SKILL.md)","agent/src/skills/event-driven/SKILL.md":"# event-driven\n\nPath: `agent/src/skills/event-driven/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/event-driven/SKILL.md)","agent/src/skills/execution-model/SKILL.md":"# execution-model\n\nPath: `agent/src/skills/execution-model/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/execution-model/SKILL.md)","agent/src/skills/factor-research/SKILL.md":"# factor-research\n\nPath: `agent/src/skills/factor-research/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/SKILL.md)","agent/src/skills/financial-statement/SKILL.md":"# financial-statement\n\nPath: `agent/src/skills/financial-statement/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/financial-statement/SKILL.md)","agent/src/skills/fund-analysis/SKILL.md":"# fund-analysis\n\nPath: `agent/src/skills/fund-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/fund-analysis/SKILL.md)","agent/src/skills/fundamental-filter/SKILL.md":"# fundamental-filter\n\nPath: `agent/src/skills/fundamental-filter/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/fundamental-filter/SKILL.md)","agent/src/skills/geopolitical-risk/SKILL.md":"# geopolitical-risk\n\nPath: `agent/src/skills/geopolitical-risk/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/geopolitical-risk/SKILL.md)","agent/src/skills/global-macro/SKILL.md":"# global-macro\n\nPath: `agent/src/skills/global-macro/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/global-macro/SKILL.md)","agent/src/skills/harmonic/SKILL.md":"# harmonic\n\nPath: `agent/src/skills/harmonic/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/SKILL.md)","agent/src/skills/hedging-strategy/SKILL.md":"# hedging-strategy\n\nPath: `agent/src/skills/hedging-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/hedging-strategy/SKILL.md)","agent/src/skills/hk-connect-flow/SKILL.md":"# hk-connect-flow\n\nPath: `agent/src/skills/hk-connect-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/hk-connect-flow/SKILL.md)","agent/src/skills/ichimoku/SKILL.md":"# ichimoku\n\nPath: `agent/src/skills/ichimoku/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/SKILL.md)","agent/src/skills/investor-lenses/SKILL.md":"# investor-lenses\n\nPath: `agent/src/skills/investor-lenses/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/SKILL.md)","agent/src/skills/liquidation-heatmap/SKILL.md":"# liquidation-heatmap\n\nPath: `agent/src/skills/liquidation-heatmap/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/liquidation-heatmap/SKILL.md)","agent/src/skills/macro-analysis/SKILL.md":"# macro-analysis\n\nPath: `agent/src/skills/macro-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/macro-analysis/SKILL.md)","agent/src/skills/management-deep-dive/SKILL.md":"# management-deep-dive\n\nPath: `agent/src/skills/management-deep-dive/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/management-deep-dive/SKILL.md)","agent/src/skills/market-microstructure/SKILL.md":"# market-microstructure\n\nPath: `agent/src/skills/market-microstructure/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/market-microstructure/SKILL.md)","agent/src/skills/minute-analysis/SKILL.md":"# minute-analysis\n\nPath: `agent/src/skills/minute-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/minute-analysis/SKILL.md)","agent/src/skills/ml-strategy/SKILL.md":"# ml-strategy\n\nPath: `agent/src/skills/ml-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ml-strategy/SKILL.md)","agent/src/skills/mootdx/SKILL.md":"# mootdx\n\nPath: `agent/src/skills/mootdx/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/mootdx/SKILL.md)","agent/src/skills/multi-factor/SKILL.md":"# multi-factor\n\nPath: `agent/src/skills/multi-factor/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/multi-factor/SKILL.md)","agent/src/skills/okx-market/SKILL.md":"# okx-market\n\nPath: `agent/src/skills/okx-market/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/SKILL.md)","agent/src/skills/onchain-analysis/SKILL.md":"# onchain-analysis\n\nPath: `agent/src/skills/onchain-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/onchain-analysis/SKILL.md)","agent/src/skills/options-advanced/SKILL.md":"# options-advanced\n\nPath: `agent/src/skills/options-advanced/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-advanced/SKILL.md)","agent/src/skills/options-payoff/SKILL.md":"# options-payoff\n\nPath: `agent/src/skills/options-payoff/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-payoff/SKILL.md)","agent/src/skills/options-strategy/SKILL.md":"# options-strategy\n\nPath: `agent/src/skills/options-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-strategy/SKILL.md)","agent/src/skills/pair-trading/SKILL.md":"# pair-trading\n\nPath: `agent/src/skills/pair-trading/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/pair-trading/SKILL.md)","agent/src/skills/performance-attribution/SKILL.md":"# performance-attribution\n\nPath: `agent/src/skills/performance-attribution/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/performance-attribution/SKILL.md)","agent/src/skills/perp-funding-basis/SKILL.md":"# perp-funding-basis\n\nPath: `agent/src/skills/perp-funding-basis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/perp-funding-basis/SKILL.md)","agent/src/skills/pine-script/SKILL.md":"# pine-script\n\nPath: `agent/src/skills/pine-script/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/pine-script/SKILL.md)","agent/src/skills/private-company-research/SKILL.md":"# private-company-research\n\nPath: `agent/src/skills/private-company-research/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/private-company-research/SKILL.md)","agent/src/skills/quant-statistics/SKILL.md":"# quant-statistics\n\nPath: `agent/src/skills/quant-statistics/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/quant-statistics/SKILL.md)","agent/src/skills/qveris/SKILL.md":"# qveris\n\nPath: `agent/src/skills/qveris/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/SKILL.md)","agent/src/skills/regulatory-knowledge/SKILL.md":"# regulatory-knowledge\n\nPath: `agent/src/skills/regulatory-knowledge/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/regulatory-knowledge/SKILL.md)","agent/src/skills/report-generate/SKILL.md":"# report-generate\n\nPath: `agent/src/skills/report-generate/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/report-generate/SKILL.md)","agent/src/skills/research-discipline/SKILL.md":"# research-discipline\n\nPath: `agent/src/skills/research-discipline/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/research-discipline/SKILL.md)","agent/src/skills/research-goal/SKILL.md":"# research-goal\n\nPath: `agent/src/skills/research-goal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/research-goal/SKILL.md)","agent/src/skills/risk-analysis/SKILL.md":"# risk-analysis\n\nPath: `agent/src/skills/risk-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/risk-analysis/SKILL.md)","agent/src/skills/seasonal/SKILL.md":"# seasonal\n\nPath: `agent/src/skills/seasonal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/seasonal/SKILL.md)","agent/src/skills/sec-edgar/SKILL.md":"# sec-edgar\n\nPath: `agent/src/skills/sec-edgar/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/SKILL.md)","agent/src/skills/sector-rotation/SKILL.md":"# sector-rotation\n\nPath: `agent/src/skills/sector-rotation/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sector-rotation/SKILL.md)","agent/src/skills/sentiment-analysis/SKILL.md":"# sentiment-analysis\n\nPath: `agent/src/skills/sentiment-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sentiment-analysis/SKILL.md)","agent/src/skills/shadow-account/SKILL.md":"# shadow-account\n\nPath: `agent/src/skills/shadow-account/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/shadow-account/SKILL.md)","agent/src/skills/smc/SKILL.md":"# smc\n\nPath: `agent/src/skills/smc/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/SKILL.md)","agent/src/skills/social-media-intelligence/SKILL.md":"# social-media-intelligence\n\nPath: `agent/src/skills/social-media-intelligence/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/social-media-intelligence/SKILL.md)","agent/src/skills/stablecoin-flow/SKILL.md":"# stablecoin-flow\n\nPath: `agent/src/skills/stablecoin-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/stablecoin-flow/SKILL.md)","agent/src/skills/strategy-dev-manager/SKILL.md":"# strategy-dev-manager\n\nPath: `agent/src/skills/strategy-dev-manager/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/SKILL.md)","agent/src/skills/strategy-discovery/SKILL.md":"# strategy-discovery\n\nPath: `agent/src/skills/strategy-discovery/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-discovery/SKILL.md)","agent/src/skills/strategy-generate/SKILL.md":"# strategy-generate\n\nPath: `agent/src/skills/strategy-generate/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-generate/SKILL.md)","agent/src/skills/technical-basic/SKILL.md":"# technical-basic\n\nPath: `agent/src/skills/technical-basic/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/technical-basic/SKILL.md)","agent/src/skills/thesis-tracker/SKILL.md":"# thesis-tracker\n\nPath: `agent/src/skills/thesis-tracker/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/thesis-tracker/SKILL.md)","agent/src/skills/token-unlock-treasury/SKILL.md":"# token-unlock-treasury\n\nPath: `agent/src/skills/token-unlock-treasury/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/token-unlock-treasury/SKILL.md)","agent/src/skills/trade-journal/SKILL.md":"# trade-journal\n\nPath: `agent/src/skills/trade-journal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/trade-journal/SKILL.md)","agent/src/skills/tushare/SKILL.md":"# tushare\n\nPath: `agent/src/skills/tushare/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/SKILL.md)","agent/src/skills/us-etf-flow/SKILL.md":"# us-etf-flow\n\nPath: `agent/src/skills/us-etf-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/us-etf-flow/SKILL.md)","agent/src/skills/valuation-model/SKILL.md":"# valuation-model\n\nPath: `agent/src/skills/valuation-model/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/valuation-model/SKILL.md)","agent/src/skills/vnpy-export/SKILL.md":"# vnpy-export\n\nPath: `agent/src/skills/vnpy-export/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/vnpy-export/SKILL.md)","agent/src/skills/volatility/SKILL.md":"# volatility\n\nPath: `agent/src/skills/volatility/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/volatility/SKILL.md)","agent/src/skills/web-reader/SKILL.md":"# web-reader\n\nPath: `agent/src/skills/web-reader/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/web-reader/SKILL.md)","agent/src/skills/yfinance/SKILL.md":"# yfinance\n\nPath: `agent/src/skills/yfinance/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/SKILL.md)","agent/evals/harness/README.md":"# Harness Skill\n\nPath: `agent/evals/harness/README.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/README.md)","agent/src/skills/strategy-dev-manager/examples.md":"# Strategy-dev-manager Skill\n\nPath: `agent/src/skills/strategy-dev-manager/examples.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/examples.md)","agent/src/skills/strategy-generate/examples.md":"# Strategy-generate Skill\n\nPath: `agent/src/skills/strategy-generate/examples.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-generate/examples.md)","agent/src/tools/paper_trade/README.md":"# paper_trade Documentation\n\nPath: `agent/src/tools/paper_trade/README.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/tools/paper_trade/README.md)","agent/src/tools/paper_trade/paper_trade_step-v1.1.json":"# Paper_trade Skill\n\nPath: `agent/src/tools/paper_trade/paper_trade_step-v1.1.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/tools/paper_trade/paper_trade_step-v1.1.json)","agent/evals/harness/cases/identity/explicit_a_share.json":"# Identity Skill\n\nPath: `agent/evals/harness/cases/identity/explicit_a_share.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/explicit_a_share.json)","agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md":"# References Skill\n\nPath: `agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md)","agent/src/skills/elliott-wave/references/波浪结构.md":"# References Skill\n\nPath: `agent/src/skills/elliott-wave/references/波浪结构.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/references/波浪结构.md)","agent/src/skills/factor-research/references/combination.md":"# References Skill\n\nPath: `agent/src/skills/factor-research/references/combination.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/references/combination.md)","agent/src/skills/factor-research/references/neutralization.md":"# References Skill\n\nPath: `agent/src/skills/factor-research/references/neutralization.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/references/neutralization.md)","agent/src/skills/harmonic/references/PRZ交易.md":"# References Skill\n\nPath: `agent/src/skills/harmonic/references/PRZ交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/references/PRZ交易.md)","agent/src/skills/harmonic/references/XABCD形态.md":"# References Skill\n\nPath: `agent/src/skills/harmonic/references/XABCD形态.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/references/XABCD形态.md)","agent/src/skills/ichimoku/references/五线计算.md":"# References Skill\n\nPath: `agent/src/skills/ichimoku/references/五线计算.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/references/五线计算.md)","agent/src/skills/ichimoku/references/信号系统.md":"# References Skill\n\nPath: `agent/src/skills/ichimoku/references/信号系统.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/references/信号系统.md)","agent/src/skills/investor-lenses/references/bogle-cost-drag.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/bogle-cost-drag.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/bogle-cost-drag.md)","agent/src/skills/investor-lenses/references/buffett-quality-franchise.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/buffett-quality-franchise.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/buffett-quality-franchise.md)","agent/src/skills/investor-lenses/references/chanos-forensic-short.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/chanos-forensic-short.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/chanos-forensic-short.md)","agent/src/skills/investor-lenses/references/dalio-debt-cycle.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/dalio-debt-cycle.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/dalio-debt-cycle.md)","agent/src/skills/investor-lenses/references/duan-right-business.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/duan-right-business.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/duan-right-business.md)","agent/src/skills/investor-lenses/references/fengliu-weak-system.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/fengliu-weak-system.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/fengliu-weak-system.md)","agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md)","agent/src/skills/investor-lenses/references/graham-deep-value.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/graham-deep-value.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/graham-deep-value.md)","agent/src/skills/investor-lenses/references/lynch-garp.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/lynch-garp.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/lynch-garp.md)","agent/src/skills/investor-lenses/references/marks-cycle-position.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/marks-cycle-position.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/marks-cycle-position.md)","agent/src/skills/investor-lenses/references/munger-inversion.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/munger-inversion.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/munger-inversion.md)","agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md)","agent/src/skills/qveris/references/coverage.md":"# References Skill\n\nPath: `agent/src/skills/qveris/references/coverage.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/references/coverage.md)","agent/src/skills/qveris/references/rest-api.md":"# References Skill\n\nPath: `agent/src/skills/qveris/references/rest-api.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/references/rest-api.md)","agent/src/skills/sec-edgar/references/endpoints_and_limits.md":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/endpoints_and_limits.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/endpoints_and_limits.md)","agent/src/skills/sec-edgar/references/forms_and_concepts.md":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/forms_and_concepts.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/forms_and_concepts.md)","agent/src/skills/sec-edgar/references/get_sec_filings_tool.md":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/get_sec_filings_tool.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/get_sec_filings_tool.md)","agent/src/skills/sec-edgar/references/sec_edgar_client.md":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/sec_edgar_client.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/sec_edgar_client.md)","agent/src/skills/smc/references/结构突破.md":"# References Skill\n\nPath: `agent/src/skills/smc/references/结构突破.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/references/结构突破.md)","agent/src/skills/smc/references/订单块与缺口.md":"# References Skill\n\nPath: `agent/src/skills/smc/references/订单块与缺口.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/references/订单块与缺口.md)","agent/src/skills/strategy-dev-manager/references/decay_thresholds.md":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/decay_thresholds.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/decay_thresholds.md)","agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md)","agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md)","agent/src/skills/strategy-dev-manager/references/strategy_metrics.md":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/strategy_metrics.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/strategy_metrics.md)","agent/src/skills/strategy-dev-manager/templates/decay_report.md":"# Templates Skill\n\nPath: `agent/src/skills/strategy-dev-manager/templates/decay_report.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/templates/decay_report.md)","agent/src/skills/yfinance/references/tool_get_options_chain.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/tool_get_options_chain.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/tool_get_options_chain.md)","agent/src/skills/yfinance/references/tool_get_stock_profile.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/tool_get_stock_profile.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/tool_get_stock_profile.md)","agent/src/skills/yfinance/references/yahoo_client_get_chart.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_chart.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_chart.md)","agent/src/skills/yfinance/references/yahoo_client_get_options.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_options.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_options.md)","agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md)","agent/src/skills/yfinance/references/yahoo_client_search.md":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_search.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_search.md)","agent/src/skills/chanlun/references/买卖点/一买一卖.md":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/一买一卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/一买一卖.md)","agent/src/skills/chanlun/references/买卖点/三买三卖.md":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/三买三卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/三买三卖.md)","agent/src/skills/chanlun/references/买卖点/二买二卖.md":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/二买二卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/二买二卖.md)","agent/src/skills/chanlun/references/核心概念/中枢.md":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/中枢.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/中枢.md)","agent/src/skills/chanlun/references/核心概念/分型.md":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/分型.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/分型.md)","agent/src/skills/chanlun/references/核心概念/笔.md":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/笔.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/笔.md)","agent/src/skills/eastmoney/references/参考数据/大宗交易.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/大宗交易.md)","agent/src/skills/eastmoney/references/参考数据/股东户数.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/股东户数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/股东户数.md)","agent/src/skills/eastmoney/references/参考数据/融资融券.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/融资融券.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/融资融券.md)","agent/src/skills/eastmoney/references/参考数据/限售解禁.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/限售解禁.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/限售解禁.md)","agent/src/skills/eastmoney/references/板块/行业概念板块.md":"# 板块 Skill\n\nPath: `agent/src/skills/eastmoney/references/板块/行业概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/板块/行业概念板块.md)","agent/src/skills/eastmoney/references/研报舆情/券商研报.md":"# 研报舆情 Skill\n\nPath: `agent/src/skills/eastmoney/references/研报舆情/券商研报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/研报舆情/券商研报.md)","agent/src/skills/eastmoney/references/研报舆情/财经新闻.md":"# 研报舆情 Skill\n\nPath: `agent/src/skills/eastmoney/references/研报舆情/财经新闻.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/研报舆情/财经新闻.md)","agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md":"# 财务报表 Skill\n\nPath: `agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md)","agent/src/skills/eastmoney/references/资金面/资金流向.md":"# 资金面 Skill\n\nPath: `agent/src/skills/eastmoney/references/资金面/资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/资金面/资金流向.md)","agent/src/skills/eastmoney/references/选股检索/代码搜索.md":"# 选股检索 Skill\n\nPath: `agent/src/skills/eastmoney/references/选股检索/代码搜索.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/选股检索/代码搜索.md)","agent/src/skills/eastmoney/references/选股检索/全市场选股.md":"# 选股检索 Skill\n\nPath: `agent/src/skills/eastmoney/references/选股检索/全市场选股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/选股检索/全市场选股.md)","agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md":"# 龙虎榜 Skill\n\nPath: `agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md)","agent/src/skills/okx-market/references/合约行情/历史资金费率.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/历史资金费率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/历史资金费率.md)","agent/src/skills/okx-market/references/合约行情/持仓量.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/持仓量.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/持仓量.md)","agent/src/skills/okx-market/references/合约行情/标记价格.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/标记价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/标记价格.md)","agent/src/skills/okx-market/references/合约行情/资金费率.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/资金费率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/资金费率.md)","agent/src/skills/okx-market/references/合约行情/限价.md":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/限价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/限价.md)","agent/src/skills/okx-market/references/指数行情/指数K线.md":"# 指数行情 Skill\n\nPath: `agent/src/skills/okx-market/references/指数行情/指数K线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/指数行情/指数K线.md)","agent/src/skills/okx-market/references/指数行情/指数行情.md":"# 指数行情 Skill\n\nPath: `agent/src/skills/okx-market/references/指数行情/指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/指数行情/指数行情.md)","agent/src/skills/okx-market/references/现货行情/K线数据.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/K线数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/K线数据.md)","agent/src/skills/okx-market/references/现货行情/交易产品列表.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/交易产品列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/交易产品列表.md)","agent/src/skills/okx-market/references/现货行情/单个行情.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/单个行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/单个行情.md)","agent/src/skills/okx-market/references/现货行情/批量行情.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/批量行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/批量行情.md)","agent/src/skills/okx-market/references/现货行情/最近成交.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/最近成交.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/最近成交.md)","agent/src/skills/okx-market/references/现货行情/深度数据.md":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/深度数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/深度数据.md)","agent/src/skills/tushare/references/ETF专题/ETF份额规模.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF份额规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF份额规模.md)","agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md)","agent/src/skills/tushare/references/ETF专题/ETF基准指数.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF基准指数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF基准指数.md)","agent/src/skills/tushare/references/ETF专题/ETF基本信息.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF基本信息.md)","agent/src/skills/tushare/references/ETF专题/ETF复权因子.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF复权因子.md)","agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md)","agent/src/skills/tushare/references/ETF专题/ETF实时日线.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF实时日线.md)","agent/src/skills/tushare/references/ETF专题/ETF日线行情.md":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF日线行情.md)","agent/src/skills/tushare/references/债券专题/债券回购日行情.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/债券回购日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/债券回购日行情.md)","agent/src/skills/tushare/references/债券专题/全球财经事件.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/全球财经事件.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/全球财经事件.md)","agent/src/skills/tushare/references/债券专题/可转债发行.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债发行.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债发行.md)","agent/src/skills/tushare/references/债券专题/可转债基础信息.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债基础信息.md)","agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md)","agent/src/skills/tushare/references/债券专题/可转债票面利率.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债票面利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债票面利率.md)","agent/src/skills/tushare/references/债券专题/可转债行情.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债行情.md)","agent/src/skills/tushare/references/债券专题/可转债赎回信息.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债赎回信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债赎回信息.md)","agent/src/skills/tushare/references/债券专题/可转债转股价变动.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债转股价变动.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债转股价变动.md)","agent/src/skills/tushare/references/债券专题/可转债转股结果.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债转股结果.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债转股结果.md)","agent/src/skills/tushare/references/债券专题/国债收益率曲线.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/国债收益率曲线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/国债收益率曲线.md)","agent/src/skills/tushare/references/债券专题/大宗交易.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/大宗交易.md)","agent/src/skills/tushare/references/债券专题/大宗交易明细.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/大宗交易明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/大宗交易明细.md)","agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md)","agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md)","agent/src/skills/tushare/references/公募基金/基金净值.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金净值.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金净值.md)","agent/src/skills/tushare/references/公募基金/基金分红.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金分红.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金分红.md)","agent/src/skills/tushare/references/公募基金/基金列表.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金列表.md)","agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md)","agent/src/skills/tushare/references/公募基金/基金持仓.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金持仓.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金持仓.md)","agent/src/skills/tushare/references/公募基金/基金管理人.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金管理人.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金管理人.md)","agent/src/skills/tushare/references/公募基金/基金经理.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金经理.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金经理.md)","agent/src/skills/tushare/references/公募基金/基金规模.md":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金规模.md)","agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md":"# 外汇数据 Skill\n\nPath: `agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md)","agent/src/skills/tushare/references/外汇数据/外汇日线行情.md":"# 外汇数据 Skill\n\nPath: `agent/src/skills/tushare/references/外汇数据/外汇日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/外汇数据/外汇日线行情.md)","agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md)","agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md)","agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md)","agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md)","agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md)","agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md)","agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md)","agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md)","agent/src/skills/tushare/references/指数专题/中信行业成分.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/中信行业成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/中信行业成分.md)","agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md)","agent/src/skills/tushare/references/指数专题/国际主要指数.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/国际主要指数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/国际主要指数.md)","agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md)","agent/src/skills/tushare/references/指数专题/指数历史分钟.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数历史分钟.md)","agent/src/skills/tushare/references/指数专题/指数周线行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数周线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数周线行情.md)","agent/src/skills/tushare/references/指数专题/指数基本信息.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数基本信息.md)","agent/src/skills/tushare/references/指数专题/指数实时分钟.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数实时分钟.md)","agent/src/skills/tushare/references/指数专题/指数实时日线.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数实时日线.md)","agent/src/skills/tushare/references/指数专题/指数成分和权重.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数成分和权重.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数成分和权重.md)","agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md)","agent/src/skills/tushare/references/指数专题/指数日线行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数日线行情.md)","agent/src/skills/tushare/references/指数专题/指数月线行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数月线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数月线行情.md)","agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md)","agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md)","agent/src/skills/tushare/references/指数专题/申万实时行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万实时行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万实时行情.md)","agent/src/skills/tushare/references/指数专题/申万行业分类.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业分类.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业分类.md)","agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md)","agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md)","agent/src/skills/tushare/references/期权数据/期权分钟行情.md":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权分钟行情.md)","agent/src/skills/tushare/references/期权数据/期权合约信息.md":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权合约信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权合约信息.md)","agent/src/skills/tushare/references/期权数据/期权日线行情.md":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权日线行情.md)","agent/src/skills/tushare/references/期货数据/交易日历.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/交易日历.md)","agent/src/skills/tushare/references/期货数据/仓单日报.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/仓单日报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/仓单日报.md)","agent/src/skills/tushare/references/期货数据/南华期货指数行情.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/南华期货指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/南华期货指数行情.md)","agent/src/skills/tushare/references/期货数据/历史分钟行情.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/历史分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/历史分钟行情.md)","agent/src/skills/tushare/references/期货数据/合约信息.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/合约信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/合约信息.md)","agent/src/skills/tushare/references/期货数据/实时分钟行情.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/实时分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/实时分钟行情.md)","agent/src/skills/tushare/references/期货数据/日线行情.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/日线行情.md)","agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md)","agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md)","agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md)","agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md)","agent/src/skills/tushare/references/期货数据/每日持仓排名.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/每日持仓排名.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/每日持仓排名.md)","agent/src/skills/tushare/references/期货数据/每日结算参数.md":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/每日结算参数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/每日结算参数.md)","agent/src/skills/tushare/references/港股数据/港股交易日历.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股交易日历.md)","agent/src/skills/tushare/references/港股数据/港股分钟行情.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股分钟行情.md)","agent/src/skills/tushare/references/港股数据/港股利润表.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股利润表.md)","agent/src/skills/tushare/references/港股数据/港股基础信息.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股基础信息.md)","agent/src/skills/tushare/references/港股数据/港股复权因子.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股复权因子.md)","agent/src/skills/tushare/references/港股数据/港股复权行情.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股复权行情.md)","agent/src/skills/tushare/references/港股数据/港股实时日线.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股实时日线.md)","agent/src/skills/tushare/references/港股数据/港股日线行情.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股日线行情.md)","agent/src/skills/tushare/references/港股数据/港股现金流量表.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股现金流量表.md)","agent/src/skills/tushare/references/港股数据/港股财务指标数据.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股财务指标数据.md)","agent/src/skills/tushare/references/港股数据/港股资产负债表.md":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股资产负债表.md)","agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md":"# 现货数据 Skill\n\nPath: `agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md)","agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md":"# 现货数据 Skill\n\nPath: `agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md)","agent/src/skills/tushare/references/美股数据/美股交易日历.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股交易日历.md)","agent/src/skills/tushare/references/美股数据/美股利润表.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股利润表.md)","agent/src/skills/tushare/references/美股数据/美股基础信息.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股基础信息.md)","agent/src/skills/tushare/references/美股数据/美股复权因子.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股复权因子.md)","agent/src/skills/tushare/references/美股数据/美股复权行情.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股复权行情.md)","agent/src/skills/tushare/references/美股数据/美股日线行情.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股日线行情.md)","agent/src/skills/tushare/references/美股数据/美股现金流量表.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股现金流量表.md)","agent/src/skills/tushare/references/美股数据/美股财务指标数据.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股财务指标数据.md)","agent/src/skills/tushare/references/美股数据/美股资产负债表.md":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股资产负债表.md)","agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json":"# Identity Skill\n\nPath: `agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json)","agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md)","agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md)","agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md)","agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md)","agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md)","agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md)","agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md)","agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md)","agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md)","agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md)","agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md)","agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md)","agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md)","agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md)","agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md)","agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md)","agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md)","agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md)","agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md)","agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md)","agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md)","agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md)","agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md)","agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md)","agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md)","agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md)","agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md)","agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md)","agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md)","agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md)","agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md)","agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md)","agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md)","agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md)","agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md)","agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md)","agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md)","agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md)","agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md)","agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md)","agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md)","agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md)","agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md)","agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md)","agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md)","agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md)","agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md)","agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md)","agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md)","agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md)","agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md)","agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md)","agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md)","agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md)","agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md)","agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md)","agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md)","agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md)","agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md)","agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md)","agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md)","agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md)","agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md)","agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md)","agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md)","agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md)","agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md)","agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md)","agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md)","agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md)","agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md)","agent/src/skills/tushare/references/股票数据/财务数据/利润表.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/利润表.md)","agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md)","agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md)","agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md)","agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md)","agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md)","agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md)","agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md)","agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md)","agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md)","agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md)","agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md)","agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md)","agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md)","agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md)","agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md)","agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md)","agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md)","agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md":"# 基金销售行业数据 Skill\n\nPath: `agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md)","agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md":"# 基金销售行业数据 Skill\n\nPath: `agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md":"# 价格指数 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md)","agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md":"# 价格指数 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md":"# 国民经济 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md)","agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md":"# 景气度 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md)","agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md)","agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md":"# 社会融资 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md)","agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md":"# 货币供应量 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md)"},"items":[{"name":"explicit_a_share.json","path":"agent/evals/harness/cases/identity/explicit_a_share.json","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/explicit_a_share.json","title":"Identity Skill","category":"anthropic-skill","format":"json","content":"# Identity Skill\n\nPath: `agent/evals/harness/cases/identity/explicit_a_share.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/explicit_a_share.json)","tokens":300,"sizeBytes":228},{"name":"a_h_hengrui.v1.json","path":"agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json","title":"Identity Skill","category":"anthropic-skill","format":"json","content":"# Identity Skill\n\nPath: `agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json)","tokens":300,"sizeBytes":260},{"name":"README.md","path":"agent/evals/harness/README.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/README.md","title":"Harness Skill","category":"anthropic-skill","format":"markdown","content":"# Harness Skill\n\nPath: `agent/evals/harness/README.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/README.md)","tokens":300,"sizeBytes":173},{"name":"SKILL.md","path":"agent/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/SKILL.md","title":"vibe-trading","category":"anthropic-skill","format":"markdown","content":"---\nname: vibe-trading\nversion: 0.1.18\ndescription: Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).\ndependencies:\n python: \">=3.11\"\n pip:\n - vibe-trading-ai\nenv:\n - name: TUSHARE_TOKEN\n description: \"Tushare API token for China A-share data (optional — HK/US/Canada/crypto work without any key)\"\n required: false\n - name: OPENAI_API_KEY\n description: \"OpenAI-compatible API key — only needed for run_swarm (multi-agent teams). All other tools work without it.\"\n required: false\n - name: LANGCHAIN_MODEL_NAME\n description: \"LLM model name for run_swarm (e.g. deepseek/deepseek-v4-pro). Only needed if using run_swarm.\"\n required: false\nmcp:\n command: vibe-trading-mcp\n args: []\n---\n\n# Vibe-Trading\n\nProfessional finance research toolkit with AI-powered backtesting (11 engines), multi-agent teams, 90 specialized skills, the **Alpha Zoo** (478 pre-built quantitative alphas across qlib158 / alpha101 / gtja191 / academic / fundamental with one-line CLI benchmarking), and the Shadow Account loop — extract your implicit trading rules from a journal, backtest them across A股/港股/美股/crypto, then see where they would have served you better.\n\n## Setup\n\n```bash\npip install vibe-trading-ai\n```\n\n> **Package name vs commands:** The PyPI package is `vibe-trading-ai`. Once installed, you get:\n>\n> | Command | Purpose |\n> |---------|---------|\n> | `vibe-trading` | Interactive CLI / TUI |\n> | `vibe-trading serve` | Launch FastAPI web server |\n> | `vibe-trading-mcp` | Start MCP server (for Claude Desktop, OpenClaw, Cursor, etc.) |\n\nAdd to your agent's MCP config:\n\n```json\n{\n \"mcpServers\": {\n \"vibe-trading\": {\n \"command\": \"vibe-trading-mcp\"\n }\n }\n}\n```\n\n### API Key Requirements\n\nCore research MCP tools work with zero API keys for HK/US/Canada/crypto. After `pip install`, backtesting, market data, factor analysis, options pricing, chart patterns, web search, document reading, trade journal analysis, shadow-account extraction/backtest/report, the Alpha Zoo (478 pre-built alphas), and all 90 skills are ready to use. IBKR tools require a local TWS / IB Gateway session; `run_swarm` requires an LLM key.\n\n| Feature | Key needed | When |\n|---------|-----------|------|\n| HK/US/Canada equities & crypto | None | Always free (yfinance / stooq / yahoo + OKX) |\n| China A-share data | None | Free via akshare / baostock / tencent / sina / eastmoney fallback (`TUSHARE_TOKEN` optional for premium quality) |\n| Premium US fundamentals/quotes | `FINNHUB_API_KEY` / `ALPHAVANTAGE_API_KEY` / `TIINGO_API_KEY` / `FMP_API_KEY` | Only for optional-key providers (graceful fallback to free sources) |\n| Multi-agent swarm (`run_swarm`) | `OPENAI_API_KEY` + `LANGCHAIN_MODEL_NAME` | Swarm spawns internal LLM workers |\n\n## What You Can Do\n\n### Shadow Account — flagship loop\n\nFeed a CSV broker export (同花顺 / 东财 / 富途 / generic), and the agent will:\n1. `analyze_trade_journal` — profile your behavior (holding period, win rate, disposition effect, chasing, overtrading, anchoring).\n2. `extract_shadow_strategy` — distill 3-5 if-then rules that describe your profitable roundtrips.\n3. `run_shadow_backtest` — backtest those rules across A/HK/US/crypto and compute delta-PnL vs your realized trades.\n4. `render_shadow_report` — produce an HTML/PDF report (8 sections + charts) with today's matching signals.\n5. `scan_shadow_signals` — list today's symbols that match your shadow's entry cadence (research only).\n\n### Backtesting\nCreate and run quantitative strategies across 11 engines (ChinaA, GlobalEquity, IndiaEquity, KoreaEquity, VietnamEquity, BrazilEquity, Crypto, ChinaFutures, GlobalFutures, Forex, cross-market Composite) plus an options portfolio engine, with 29 market-data sources (auto-detect + ordered fallback; the hosted forex `tickerall` source is explicit-only):\n- **HK/US equities** via yfinance / stooq / yahoo (free, no API key); optionally via **Longbridge** historical OHLCV (`longbridge`, requires the optional SDK and `LONGBRIDGE_APP_KEY` / `LONGBRIDGE_APP_SECRET` / `LONGBRIDGE_ACCESS_TOKEN`). To force it for a run, set `\"source\": \"longbridge\"` in `config.json`.\n- **Canada equities (TSX/TSXV)** via yahoo / yfinance using Yahoo's canonical `.TO` (TSX, e.g. `TD.TO`) or `.V` (TSXV, e.g. `PNG.V`) suffixes — free, no API key. The GlobalEquity engine uses CAD identity, whole-share orders, configurable Canadian commission/slippage, and the TSX/TSXV price-increment grid.\n- **India equities (NSE/BSE)** via yahoo / yfinance using `.NS` (NSE, e.g. `RELIANCE.NS`) or `.BO` (BSE, e.g. `500325.BO`) — free, no API key. The `IndiaEquityEngine` models T+1 delivery, no overnight shorts (set `allow_short` for intraday), configurable circuit bands, 1-share lots, and the STT/stamp-duty/exchange/GST cost stack. Optionally back-fill from your live broker via the `india_broker` source (Shoonya/Dhan; requires broker login).\n- **Korea equities (KRX: KOSPI/KOSDAQ)** via pykrx using `.KS` (KOSPI, e.g. `005930.KS`) or `.KQ` (KOSDAQ, e.g. `247540.KQ`) — free, no API key (`pip install \"vibe-trading-ai[krx]\"`; yahoo/yfinance fallback needs no extra). pykrx serves **daily bars only** (an intraday request falls through to another source) and its adjusted series is Naver-backed rather than a verbatim KRX print. The `KoreaEquityEngine` models same-day round trips (no T+1), the ±30% daily price limit measured from the previous close and quantized to the KRX tick grid, tick-rounded fills, the 0.20% sell-side transaction tax (2026 rate), and 1-share lots. It is **long-only**: `allow_short` is refused, because KRX covered-short and uptick rules cannot be enforced on daily bars.\n- **Vietnam equities (HOSE)** via yahoo / yfinance using `.VN` (e.g. `VIC.VN`) — no API key. Yahoo officially lists HOSE but not HNX or UPCOM; those venues are unsupported and need the `local` source. yfinance is an unofficial Yahoo client, so availability is best-effort and subject to Yahoo's personal-use terms. The `VietnamEquityEngine` approximates the formal T+2 settlement cycle — shares bought on day T normally become sellable during the afternoon session on T+2, informally called T+1.5 — as a two-bar hold on daily data (`vn_settlement_bars` covers scenario testing and future rule changes). It applies HOSE's normal ±7% band around the reference price, rounding the ceiling down and the floor up to the 10/50/100-VND tick grid, and uses 100-share round lots; odd-lot trading is not modelled. Costs are configurable brokerage plus, for individual investors, 0.1% sell-side personal income tax on gross proceeds. It is **long-only**, because operational cash-equity short selling is not generally available in Vietnam.\n- **Cryptocurrency** via OKX or CCXT/100+ exchanges (free, no API key)\n- **China A-shares** via AKShare / baostock / tencent / sina / eastmoney (free, no API key) — `TUSHARE_TOKEN` optional for premium quality\n- **Futures, forex, macro** via AKShare (free, no API key)\n- **Forex / metals with no local terminal** via the hosted **TickerAll** MetaTrader 5 feed (`source=\"tickerall\"`, `TICKERALL_API_KEY` + `TICKERALL_ACCOUNT_ID`, read-only) — the same broker feed as the `mt5` loader but over a hosted API on any OS. **Explicit-only** (never an automatic fallback).\n- **HK & A-share equities** via Futu (broker login required, optional)\n- **Local CSV/parquet bars** via the `local` loader (offline, no network)\n- **Premium cross-market data** via QVeris (optional API key)\n- **Premium US data** via optional-key finnhub / alphavantage / tiingo / fmp (graceful fallback to free sources)\n\nFactors: the Alpha101 and QLib158 zoos are tagged for the `equity_in` and `equity_kr` universes, so they compute on NSE/BSE and KRX bars (the GTJA191 zoo stays China-only). Live/paper India trading uses the Shoonya / Dhan connectors (paper + read-only live; live order placement is structurally disabled because those brokers expose no paper/live switch). Live/paper Korea equity trading uses the **KIS** connector (한국투자증권; genuine 모의투자 paper sandbox on a separate host/port from 실전투자 live, plus read-only live — no mandate-gated live-trade profile yet). Live/paper Korea crypto trading uses the **Upbit** connector (KRW markets; paper + read-only live — no runtime paper/live discriminator, so live order placement is structurally disabled, same as Shoonya/Dhan). Read-only Korea/US equity access is also available via the **Toss Securities** connector (토스증권) — no verified sandbox, so order placement always refuses, same as Trading 212.\n\nExample workflow:\n1. Use `list_skills()` to discover strategy patterns\n2. Use `load_skill(\"strategy-generate\")` for the strategy creation guide\n3. Use `write_file()` to create `config.json` and `code/signal_engine.py`\n4. Use `backtest()` to run and get metrics (Sharpe, return, drawdown, etc.)\n\n### Multi-Agent Swarm Teams\n30 pre-built agent teams for complex research:\n- **Investment Committee**: bull/bear debate → risk review → PM decision\n- **Global Equities Desk**: A-share + HK/US + crypto → global strategist\n- **Crypto Trading Desk**: funding/basis + liquidation + flow → risk manager\n- **Earnings Research Desk**: fundamentals + revisions + options → earnings strategist\n- **Macro/Rates/FX Desk**: rates + FX + commodities → macro PM\n- **Quant Strategy Desk**: screening → factor research → backtest → risk audit\n- **Risk Committee**: drawdown, tail risk, regime analysis\n- And 23 more specialized teams\n\nUse `list_swarm_presets()` to see all teams, then `run_swarm()` to execute.\n\n### Alpha Zoo (478 pre-built alphas)\nOne-line cross-sectional IC / IR / alive-reversed-dead categorisation across five bundled zoos:\n- **qlib158** (154 alphas) — Microsoft Qlib's `Alpha158` feature handler, Apache-2.0 with pinned commit SHA.\n- **alpha101** (101 alphas) — Kakushadze (2015) \"101 Formulaic Alphas\" (arXiv:1601.00991), written from the paper appendix.\n- **gtja191** (191 alphas) — Guotai Junan 2014 \"191 Short-period Trading Alpha Factors\" research report.\n- **academic** (12 factors) — Fama-French 5 + Carhart momentum + Jegadeesh reversal + George-Hwang 52-week-high + Amihud illiquidity + Harvey-Siddique skew + Frazzini-Pedersen betting-against-beta (price-based proxies) + a correlation-rewiring stability score (from the in-repo correlation-regime skill).\n- **fundamental** (20 factors) — PIT-safe earnings yield, ROE, gross profitability and asset growth, plus ten SEC XBRL additions: book-to-market, FCF yield, accruals, ROA, CFO yield, operating margin, cash-to-assets, leverage, revenue growth and earnings growth, from daily fundamental panels; three CN A-share daily-valuation factors (PB, PS_TTM, dividend yield) fed by the ``val:*`` panel namespace from tushare daily_basic; and three industry-neutralized variants of those CN value factors, z-scored within Shenwan L1 peers against a PIT SW2021 industry tag from tushare index_member_all (membership is point-in-time; the SW2021 taxonomy itself is retroactive).\n\nEach alpha ships with `__alpha_meta__` (formula LaTeX + theme + universe + warmup + columns required), guarded by an AST purity gate + 300-row lookahead sentinel test. Use the `vibe-trading alpha {list,show,bench,compare,export-manifest}` CLI, the `/alpha/*` REST routes (browser at `/alpha-zoo`), or compose multi-factor signals via `ZooSignalEngine.from_zoo(...)`.\n\n### Finance Skills (90)\nComprehensive knowledge base covering:\n- Technical analysis (candlestick, Elliott wave, Ichimoku, SMC, harmonic, chanlun)\n- Quantitative methods (factor research, ML strategy, pair trading, multi-factor)\n- Risk management (VaR/CVaR, stress testing, hedging)\n- Options (Black-Scholes, Greeks, multi-leg strategies, payoff diagrams)\n- HK/US equities (SEC filings, earnings revisions, ETF flows, ADR/H-share arbitrage)\n- Crypto trading desk (funding rates, liquidation heatmaps, stablecoin flows, token unlocks, DeFi yields)\n- Behavioral finance, trade journal diagnostics, shadow account\n- Macro analysis, credit research, sector rotation, and more\n\nUse `load_skill(name)` to access full methodology docs with code templates.\n\n## Available MCP Tools (82)\n\n| Tool | Description | API Key |\n|------|-------------|---------|\n| `list_skills` | List all 90 finance skills | None |\n| `load_skill` | Load full skill documentation | None |\n| `start_research_goal` | Create an auditable research goal | None |\n| `get_research_goal` | Read the current research goal | None |\n| `add_goal_evidence` | Attach evidence to a research goal | None |\n| `update_research_goal_status` | Update goal lifecycle status | None |\n| `backtest` | Run vectorized backtest engine | None* |\n| `paper_trade_step` | Deterministic paper-trading matching engine; never places real orders | None* |\n| `factor_analysis` | IC/IR analysis + layered backtest | None* |\n| `alpha_zoo` | Browse bundled alpha metadata and registry health | None |\n| `alpha_bench` | Benchmark one alpha or a complete zoo | None* |\n| `analyze_options` | Black-Scholes price + Greeks | None |\n| `analyze_options_payoff` | Multi-leg expiry payoff + spot/IV scenarios | None |\n| `pattern_recognition` | Detect chart patterns (H&S, double top, etc.) | None |\n| `get_market_data` | Fetch OHLCV data (auto-detect + ordered fallback across 29 sources) | None* |\n| `get_fund_flow` | Capital fund-flow (main/retail net inflow) | None* |\n| `get_dragon_tiger` | Dragon-tiger list (龙虎榜) top buyer/seller seats | None* |\n| `get_northbound_flow` | Northbound (Stock Connect) net flow | None* |\n| `get_southbound_flow` | Southbound (Stock Connect) net buy into HK (Eastmoney/HKEX) | None |\n| `get_margin_trading` | Margin trading & short-selling balances | None* |\n| `get_block_trades` | Block-trade (大宗交易) records | None* |\n| `get_shareholder_count` | Shareholder-count history per symbol | None* |\n| `get_lockup_expiry` | Restricted-share lockup release schedule | None* |\n| `get_sector_info` | Sector / industry constituents & performance | None* |\n| `get_research_reports` | Sell-side analyst research reports | None* |\n| `get_stock_news` | Market & company news headlines | None* |\n| `get_sec_filings` | SEC EDGAR filings (10-K/10-Q/8-K, etc.) | None |\n| `get_financial_statements` | Income / balance / cash-flow statements | None* |\n| `get_options_chain` | Options chain (strikes, IV, OI, Greeks) | None* |\n| `get_stock_profile` | Valuation, analyst estimates & institutional holdings (US/HK) | None |\n| `screen_market` | Market screener with fundamental/technical filters | None* |\n| `search_symbol` | Symbol / ticker search across markets | None |\n| `get_macro_series` | FRED macroeconomic series | FRED_API_KEY |\n| `get_fund_nav` | Chinese off-exchange fund NAV history | GILDATA_TOKEN |\n| `get_cn_macro_series` | China macro/industry EDB series (NL query) | GILDATA_TOKEN |\n| `get_cn_announcements` | A-share/HK/fund announcement search (NL query) | GILDATA_TOKEN |\n| `search_broker_reports` | Broker research search over the Juyuan library (NL query) | GILDATA_TOKEN |\n| `iwencai_search` | A-share natural-language research search | IWENCAI_KEY |\n| `qveris_search` | Search QVeris premium data/tool marketplace (free discovery) | QVERIS_API_KEY + paid mode |\n| `qveris_inspect` | Inspect QVeris tool schemas before executing (free) | QVERIS_API_KEY + paid mode |\n| `qveris_execute` | Execute a QVeris capability; budget-bounded, may be billable | QVERIS_API_KEY + paid mode |\n| `web_search` | Search the web via DuckDuckGo | None |\n| `read_url` | Fetch web page as Markdown | None |\n| `read_document` | Extract text from PDF/DOCX/XLSX/PPTX/images | None |\n| `write_file` | Write files (config, strategy code) | None |\n| `read_file` | Read file contents | None |\n| `read_run_artifact` | Structured run-artifact reads (rows / downsample / meta) | None |\n| `list_strategies` | Browse discoverable strategies (Alpha Zoo + SDM store) | None |\n| `query_strategies` | Evidence-gated query: regime / Sharpe / quality / cost filters | None |\n| `get_strategy_evidence` | Per-regime evidence rows for one strategy | None |\n| `adapt_strategy` | Prepare an evidence-gated child strategy for generation and its own backtest |\n| `refresh_strategy_evidence` | Rebuild the disposable strategy-evidence cache from run artifacts | None |\n| `analyze_trade_journal` | Parse broker CSV → profile + behavior diagnostics | None |\n| `extract_shadow_strategy` | Distill 3-5 if-then rules from profitable roundtrips | None |\n| `run_shadow_backtest` | Multi-market backtest + delta-PnL attribution | None* |\n| `render_shadow_report` | HTML/PDF shadow report (8 sections + charts) | None |\n| `scan_shadow_signals` | Today's symbols matching the shadow's cadence | None |\n| `list_swarm_presets` | List multi-agent team presets | None |\n| `run_swarm` | Execute a multi-agent research team | LLM key |\n| `get_swarm_status` | Poll swarm run status without blocking | None |\n| `get_run_result` | Get final report and task summaries | None |\n| `list_runs` | List recent swarm runs with metadata | None |\n| `reap_stale_runs` | Finalize stale swarm runs | None |\n| `retry_run` | Re-run a failed/stale swarm run | LLM key |\n| `trading_connections` | List selectable connector profiles | None |\n| `trading_select_connection` | Select the default connector profile | None |\n| `trading_check` | Check connector readiness | Connector app/OAuth |\n| `trading_account` | Read account summary from selected connector | Connector app/OAuth |\n| `trading_positions` | Read positions from selected connector | Connector app/OAuth |\n| `trading_orders` | Read open orders from selected connector | Connector app/OAuth |\n| `trading_quote` | Read a quote snapshot from selected connector | Connector app/OAuth |\n| `trading_history` | Read historical bars from selected connector | Connector app/OAuth |\n| `get_institutional_holdings` | SEC 13F-HR holdings by manager/ticker + quarter-over-quarter position diffs | None |\n| `etf_holdings` | ETF look-through — SEC N-PORT (US) and full-book A-share fund reports | None |\n| `prediction_market` | Event-contract search/market/history as labelled implied probability | None |\n| `research_papers` | arXiv + OpenAlex search/read with source-anchored claim extraction | None |\n| `quantlib_call` | Pure-compute finance math — 308 functions across 24 quantlib modules | None |\n| `cashflow_performance` | XIRR / MOIC / DPI / TVPI / TWR / Modified Dietz over dated cash flows | None |\n| `orderbook_depth` | Crypto L2 ladder — spread bps, depth imbalance, impact cost | None |\n| `sentiment` | Local lexicon text scoring + crypto Fear & Greed Index | None |\n| `technical_indicators` | RSI / MACD / Bollinger / SMA / EMA through the existing loaders | None* |\n| `get_fundamentals` | PIT-safe SEC fundamentals panels (filed-date anchored) | None |\n\n*A-share symbols require `TUSHARE_TOKEN`. HK/US/Canada/crypto are free. Trading connector rows use the selected connector profile, e.g. IBKR local TWS/Gateway or Robinhood MCP OAuth.\n\n## Quick Start\n\n```bash\npip install vibe-trading-ai\n```\n\nThat's it — no API keys needed for HK/US/Canada/crypto markets. Start using `backtest`, `get_market_data`, `analyze_options`, `analyze_trade_journal`, `extract_shadow_strategy`, `web_search`, the **Alpha Zoo** (`vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025`), and all 90 skills immediately.\n\n## Loading Tools from External MCP Servers\n\nThe built-in agent can load tools from your own external MCP servers in addition to its local toolset.\n\n> **Note:** This is the *MCP client* path — the opposite of the MCP plugin listed above. The plugin above makes Vibe-Trading's tools available to your agents. This section lets Vibe-Trading's own agent call tools from *your* servers.\n\n### Setup\n\nCreate `~/.vibe-trading/agent.json`:\n\n```json\n{\n \"mcpServers\": {\n \"my-server\": {\n \"command\": \"uvx\",\n \"args\": [\"my-mcp-server\"],\n \"toolTimeout\": 30,\n \"enabledTools\": [\"*\"]\n }\n }\n}\n```\n\nOrdinary external MCP tools appear automatically in every `vibe-trading run` / `vibe-trading chat` call. They are injected after local tools under stable names: `mcp__`. Live-broker MCP servers are consumed through the connector-scoped `trading_*` tools instead of exposing raw `mcp__*` tools to the agent.\n\n### Official IBKR MCP read-only probe\n\nAdd Interactive Brokers' official MCP endpoint as a read-only external server:\n\n```json\n{\n \"mcpServers\": {\n \"ibkr\": {\n \"type\": \"streamableHttp\",\n \"url\": \"https://api.ibkr.com/v1/api/mcp-public\",\n \"auth\": {\n \"type\": \"oauth\",\n \"scopes\": [\"mcp.read\"],\n \"clientName\": \"Vibe-Trading\",\n \"cacheDir\": \"~/.vibe-trading/live/ibkr/oauth\"\n },\n \"enabledTools\": [\"*\"]\n }\n }\n}\n```\n\nAuthorize it with `vibe-trading connector authorize ibkr-live-official-mcp-readonly`. The wildcard is accepted\nonly for this `mcp.read` probe. Generic `trading_account` and `trading_positions`\ncalls stay disabled until IBKR publishes stable read tool names that Vibe-Trading\ncan map safely; `mcp.write` requires an explicit tool allowlist and live\norder-guard handling. If IBKR issues a pre-registered OAuth client, add\n`clientId` and `clientSecret` inside `auth`.\n\n### Official eToro Public API MCP (discovery + dev)\n\neToro ships a hosted MCP at `https://mcp.public-api.etoro.com` with live OpenAPI\nroute discovery (`get-all-routes`, `get-route-spec`) and optional execution\n(`execute-read`, `execute-write`). Use it for **API exploration and codegen** —\nproduction agent trading in Vibe-Trading goes through the built-in `etoro-*`\nconnector profiles and `trading_*` / `etoro_*` tools (mandate gate on live writes).\n\nAdd to `~/.vibe-trading/agent.json` (credentials on the connection, not in chat):\n\n```json\n{\n \"mcpServers\": {\n \"etoro-public-api\": {\n \"type\": \"streamableHttp\",\n \"url\": \"https://mcp.public-api.etoro.com\",\n \"headers\": {\n \"x-api-key\": \"YOUR_PUBLIC_API_KEY\",\n \"x-user-key\": \"YOUR_USER_KEY\"\n },\n \"enabledTools\": [\"get-all-routes\", \"get-route-spec\", \"execute-read\"]\n }\n }\n}\n```\n\nOmit `execute-write` unless you want the MCP to place trades directly (bypasses\nVibe-Trading's live mandate gate). Install skill:\n`https://mcp.public-api.etoro.com/skill`\n\n### Trading connector profiles\n\nThe public trading surface is connector-first. Choose a connector profile, then\npaper/live is just an attribute under that connector.\n\n```bash\npip install \"vibe-trading-ai[ibkr]\"\nvibe-trading connector list\nvibe-trading connector use ibkr-paper-local\nvibe-trading connector configure ibkr-paper-local --yes\nvibe-trading connector check\nvibe-trading connector account\nvibe-trading connector positions\nvibe-trading connector orders\nvibe-trading connector quote AAPL\nvibe-trading connector history AAPL --duration \"30 D\" --bar-size \"1 day\"\n```\n\nLive-runner model spending is independent of mandate trade limits. Operators\nconfigure it through Runtime / the chat runner panel, or\n`vibe-trading connector runner-settings --profile robinhood-live-mcp`\n(`--file settings.json` saves settings while stopped). Defaults are one model\ndecision every 15 minutes, 26 decisions and 1,000,000 reported tokens per UTC day,\nwith fresh conversation context per decision. A once-daily time and IANA timezone\nare optional. Dollar limits require operator-supplied input/output prices and are\nestimates, not provider billing guarantees. Usage is checked after each response;\nbudget exhaustion or missing usage activates the existing kill switch. The agent\nmust not edit this operator state or raise its own spending limits.\n\nDefault ports are TWS paper `7497`, IB Gateway paper `4002`, TWS live-readonly\n`7496`, and IB Gateway live-readonly `4001`.\n\n### Config fields\n\n| Field | Required | Default | Description |\n|-------|----------|---------|-------------|\n| `type` | stdio: no, HTTP: yes | inferred only for stdio | Transport type. Use `sse` or `streamableHttp` for URL-based servers. |\n| `command` | stdio: yes | — | Executable to launch |\n| `args` | no | `[]` | Command arguments |\n| `env` | no | `{}` | Extra env vars for the subprocess |\n| `url` | HTTP: yes | — | Remote SSE / streamable HTTP endpoint URL |\n| `headers` | no | `{}` | Extra HTTP headers for SSE / streamable HTTP servers |\n| `toolTimeout` | no | `30` | Seconds before a tool call is cancelled |\n| `enabledTools` | no | `[\"*\"]` | Allowlist of remote tool names. `[\"*\"]` enables all |\n\nFor URL-based transports, `type` is required. The agent no longer guesses between SSE and streamable HTTP from the URL suffix.\n\n### Per-session override (API)\n\n> **Security — disabled by default.** `mcpServers` defines subprocess `command`/`args`/`env` and is therefore restricted to operator-level trust. API callers **cannot** inject MCP server definitions through `POST /sessions` unless the server operator explicitly opts in.\n\nTo enable session-level MCP injection, set the environment variable on the server before starting the agent:\n\n```bash\nexport ALLOW_SESSION_MCP_SERVERS=1\n```\n\nWith the opt-in active, pass `mcpServers` inside `session.config` to extend or replace the global config for that session only:\n\n```json\n{\n \"config\": {\n \"mcpServers\": {\n \"research\": {\n \"command\": \"uvx\",\n \"args\": [\"research-mcp\"],\n \"enabledTools\": [\"search\"]\n }\n }\n }\n}\n```\n\nWithout `ALLOW_SESSION_MCP_SERVERS=1`, any `mcpServers` key in `session.config` is silently stripped before config loading. The global operator config on disk (`~/.vibe-trading/agent.json`) is always respected regardless of this flag.\n\n### v1 limits\n\n- **Transport:** stdio, SSE, and streamable HTTP.\n- **Execution:** serial only. MCP tools never enter the parallel readonly path.\n- **Surfaces:** tools only. Resources and prompts are not exposed.\n- **Swarm:** MCP tools are excluded from Swarm worker registries in v1.\n- **Hot reload:** not supported. Restart the process to pick up config changes.\n\n### Failure handling\n\n| Case | Behavior |\n|------|----------|\n| Missing config file | falls back to empty config — no MCP servers loaded |\n| Invalid config file | logs a warning and falls back to empty config |\n| Server fails to start | that server is skipped; local tools and other servers still load |\n| Tool call times out | returns a normalized error payload instead of raising |\n| Two server names collide after sanitization | deterministic hash suffix appended; operator warning emitted |\n\n\n\n## Examples\n\n**Backtest a MACD strategy on Apple:**\n> Backtest AAPL with MACD crossover strategy (fast=12, slow=26, signal=9) for 2024\n\n**Analyze my trade journal and build a Shadow Account:**\n> Call analyze_trade_journal on ~/Downloads/tonghuashun.csv, then extract_shadow_strategy with min_support=3, then run_shadow_backtest for the last year, then render_shadow_report.\n\n**Run an investment committee review:**\n> Use run_swarm with investment_committee preset to evaluate NVDA. Variables: target=NVDA.US, market=US\n\n**Factor analysis on CSI 300:**\n> Run factor_analysis on CSI 300 stocks using pe_ttm factor from 2023 to 2024\n\n**Options analysis:**\n> Use analyze_options: spot=100, strike=105, 90 days, vol=25%, rate=3%\n\n**Multi-leg options payoff:**\n> Use analyze_options_payoff for a 95/105 bull call spread at spot 100 with 30 days remaining: long one 95 call at premium 8, short one 105 call at premium 3, multiplier 100, commission rate 0.001.\n","frontmatter":{"name":"vibe-trading","version":"0.1.18","description":"Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).","dependencies":{"python":">=3.11","pip":["vibe-trading-ai"]},"env":[{"name":"TUSHARE_TOKEN","description":"Tushare API token for China A-share data (optional — HK/US/Canada/crypto work without any key)","required":false},{"name":"OPENAI_API_KEY","description":"OpenAI-compatible API key — only needed for run_swarm (multi-agent teams). All other tools work without it.","required":false},{"name":"LANGCHAIN_MODEL_NAME","description":"LLM model name for run_swarm (e.g. deepseek/deepseek-v4-pro). Only needed if using run_swarm.","required":false}],"mcp":{"command":"vibe-trading-mcp","args":[]}},"isInternal":false,"tokens":7216,"sizeBytes":28062},{"name":"SKILL.md","path":"agent/src/skills/adr-hshare/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/adr-hshare/SKILL.md","title":"adr-hshare","category":"anthropic-skill","format":"markdown","content":"---\nname: adr-hshare\ndescription: ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.\ncategory: flow\n---\n# ADR / H-Share / A-Share Cross-Listing Analysis\n\n## Overview\n\nMany Chinese companies are listed on multiple exchanges — A-shares in Shanghai/Shenzhen, H-shares in Hong Kong, and ADRs in the US. Pricing gaps between these listings create arbitrage opportunities and reveal market-specific sentiment differences. This skill provides frameworks for analyzing cross-listing premiums, identifying arbitrage signals, and assessing delisting risk for US-listed Chinese ADRs.\n\n## Core Concepts\n\n### 1. Cross-Listing Structures\n\n| Structure | Description | Examples |\n|-----------|-------------|---------|\n| A + H dual-listed | Same company listed on both A-share and HK exchange | PetroChina (601857.SH / 0857.HK), ICBC (601398.SH / 1398.HK) |\n| H + ADR dual-listed | HK-listed with US ADR | Alibaba (9988.HK / BABA), JD.com (9618.HK / JD) |\n| A + H + ADR triple-listed | All three markets | China Life (601628.SH / 2628.HK / LFC) |\n| HK primary + US secondary | Primary listing in HK, secondary ADR | Tencent (0700.HK / TCEHY OTC) |\n| US primary → HK secondary | Originally US, added HK listing | Alibaba (BABA → 9988.HK), Baidu (BIDU → 9888.HK) |\n\n### 2. AH Premium Analysis\n\n**AH Premium = (A-share price / H-share price in CNY terms - 1) × 100%**\n\n```python\ndef calculate_ah_premium(a_price_cny, h_price_hkd, usdcny, usdhkd):\n \"\"\"Calculate AH premium for a dual-listed stock.\"\"\"\n h_price_cny = h_price_hkd * (usdcny / usdhkd) # Convert HKD to CNY\n ah_premium = (a_price_cny / h_price_cny - 1) * 100\n return ah_premium\n\n# Example: PetroChina\n# A-share: 8.50 CNY, H-share: 6.20 HKD\n# USDCNY: 7.25, USDHKD: 7.82\n# H in CNY: 6.20 * (7.25/7.82) = 5.75 CNY\n# AH Premium: (8.50/5.75 - 1) * 100 = 47.8%\n```\n\n**AH Premium signal interpretation:**\n\n| Premium Level | Interpretation | Action |\n|--------------|----------------|--------|\n| >50% | Extreme A-share premium; A-share speculative bubble or H-share extreme undervaluation | Strong: buy H, sell/avoid A |\n| 30-50% | Elevated premium; normal for high-retail-participation names | Moderate: favor H if fundamentals same |\n| 10-30% | Normal range for most AH pairs | Neutral; no strong arbitrage signal |\n| 0-10% | Compressed premium; A-shares relatively cheap | Unusual; investigate catalyst |\n| <0% | H-share premium over A-share | Very rare; usually near-term event-driven |\n\n**Structural drivers of AH premium:**\n1. **Liquidity premium**: A-shares have much higher retail participation and turnover → liquidity premium\n2. **Access premium**: A-shares were historically hard for foreigners to access → scarcity premium\n3. **Currency expectations**: CNY depreciation expectations widen the premium\n4. **Regulatory arbitrage**: different trading rules (T+1 in A-shares vs T+0 in HK)\n5. **Investor composition**: A-share retail speculative premium vs HK institutional valuation discipline\n\n### 3. ADR Premium/Discount Analysis\n\n**ADR premium = (ADR price in USD / HK equivalent in USD - 1) × 100%**\n\n```python\ndef calculate_adr_premium(adr_price_usd, hk_price_hkd, adr_ratio, usdhkd):\n \"\"\"\n Calculate ADR premium over HK listing.\n adr_ratio: number of HK shares per 1 ADR (e.g., BABA: 1 ADR = 8 HK shares)\n \"\"\"\n hk_equivalent_usd = (hk_price_hkd * adr_ratio) / usdhkd\n premium = (adr_price_usd / hk_equivalent_usd - 1) * 100\n return premium\n\n# Example: Alibaba\n# BABA ADR: $85.00, 9988.HK: HKD 82.50\n# ADR ratio: 1 ADR = 8 HK shares\n# HK equivalent: (82.50 * 8) / 7.82 = $84.40\n# ADR premium: (85.00/84.40 - 1) * 100 = 0.71%\n```\n\n**Key ADR conversion ratios:**\n\n| Company | ADR Ticker | HK Ticker | ADR Ratio (HK:ADR) | ADR Exchange |\n|---------|-----------|-----------|---------------------|--------------|\n| Alibaba | BABA | 9988.HK | 8:1 | NYSE |\n| JD.com | JD | 9618.HK | 2:1 | NASDAQ |\n| Baidu | BIDU | 9888.HK | 8:1 | NASDAQ |\n| Bilibili | BILI | 9626.HK | 1:1 | NASDAQ |\n| NIO | NIO | 9866.HK | 1:1 | NYSE |\n| XPeng | XPEV | 9868.HK | 2:1 | NYSE |\n| Li Auto | LI | 2015.HK | 2:1 | NASDAQ |\n| NetEase | NTES | 9999.HK | 5:1 | NASDAQ |\n| Trip.com | TCOM | 9961.HK | 1:1 | NASDAQ |\n| Pinduoduo | PDD | N/A (US-only) | N/A | NASDAQ |\n\n**ADR premium drivers:**\n- US trading hours sentiment (earnings releases, macro data during US hours)\n- US-specific regulatory events (SEC, PCAOB audits)\n- Liquidity premium (ADR often more liquid for global funds)\n- Time zone gap: ADR closes at HK's open → overnight gap creates premium/discount\n\n### 4. Delisting Risk Assessment\n\n**HFCAA (Holding Foreign Companies Accountable Act) framework:**\n\nSince 2022, PCAOB gained access to audit workpapers of Chinese companies. Key risks:\n\n| Risk Level | Criteria | Impact |\n|------------|----------|--------|\n| Low | PCAOB inspection completed, no issues | ADR status stable |\n| Medium | PCAOB inspection completed, deficiencies noted | Monitor for resolution |\n| High | PCAOB access revoked or restricted | 3-year delisting countdown activated |\n| Critical | On SEC \"identified issuer\" list for 3 consecutive years | Forced delisting |\n\n**Delisting risk indicators:**\n```python\ndelisting_risk_factors = {\n \"pcaob_status\": \"inspected\", # inspected / pending / blocked\n \"sec_identified_years\": 0, # 0, 1, 2, or 3 (3 = delist)\n \"has_hk_listing\": True, # Backup listing reduces impact\n \"hk_listing_type\": \"primary\", # primary (can be in Connect) vs secondary\n \"vie_structure\": True, # Variable Interest Entity adds legal risk\n \"state_owned\": False, # SOE status adds geopolitical risk\n}\n\n# Companies with HK primary listing (BABA, JD, BIDU, NTES, etc.) have\n# a safety net if US delisting occurs → fungible conversion ADR → HK shares\n# Companies with US-only listing (PDD until HK listing) face higher risk\n```\n\n### 5. Cross-Listing Arbitrage Strategies\n\n**Strategy 1: AH Premium Mean-Reversion**\n```python\n# When AH premium for a specific stock diverges significantly from its historical average\nah_premium_current = 45 # current premium\nah_premium_mean_12m = 35 # 12-month average\nah_premium_std = 8 # standard deviation\n\nz_score = (ah_premium_current - ah_premium_mean_12m) / ah_premium_std\n\nif z_score > 2.0:\n signal = \"fade_premium\" # A-share overvalued vs H; buy H, avoid A\nelif z_score < -2.0:\n signal = \"buy_premium\" # A-share undervalued vs H; buy A, avoid H\nelse:\n signal = \"neutral\"\n```\n\n**Strategy 2: ADR-HK Intraday Arbitrage**\n- During overlapping trading hours (HK morning = US pre-market via ADR), price gaps can appear\n- Professional arbitrageurs use ADR↔HK fungible conversion to capture these gaps\n- For research purposes: tracking ADR premium trend indicates which market is leading sentiment\n\n**Strategy 3: Event-Driven Cross-Listing**\n- New HK listing announcement (US→HK): ADR typically dips 2-5% on dilution fear, then recovers\n- MSCI / FTSE index inclusion of HK listing: triggers passive fund buying in HK\n- Stock Connect inclusion (HK primary listing eligible): triggers mainland institutional buying\n\n## Data Access\n\n```python\nimport yfinance as yf\n\n# Fetch ADR and HK prices simultaneously\nbaba_adr = yf.download(\"BABA\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\nbaba_hk = yf.download(\"9988.HK\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\n\n# For A+H pairs\npetrochina_a = yf.download(\"601857.SS\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\npetrochina_h = yf.download(\"0857.HK\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\n\n# FX rates for premium calculation\ncny = yf.download(\"CNY=X\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False) # USD/CNY\nhkd = yf.download(\"HKD=X\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False) # USD/HKD\n\n# AH Premium Index (HSAHP)\n# Not directly on yfinance; use Hang Seng website or Tushare\n```\n\n## Output Format\n\n```\n## Cross-Listing Analysis — [Company Name]\n\n### Listing Structure\n- **A-share**: [code] @ [price CNY]\n- **H-share**: [code] @ [price HKD]\n- **ADR**: [ticker] @ [price USD] (ratio: X HK shares = 1 ADR)\n\n### Premium/Discount\n- **AH Premium**: [X%] (12m avg: X%, z-score: X.X)\n- **ADR-HK Premium**: [X%] (5d avg: X%)\n- **Direction**: [AH premium widening / narrowing / stable]\n\n### Valuation Comparison\n| Metric | A-share | H-share | ADR |\n|--------|---------|---------|-----|\n| PE (TTM) | XX.X | XX.X | XX.X |\n| PB | X.X | X.X | X.X |\n| Dividend yield | X.X% | X.X% | X.X% |\n\n### Delisting Risk (ADR)\n- **PCAOB status**: [inspected / pending]\n- **SEC identified years**: [0/1/2/3]\n- **HK backup**: [yes-primary / yes-secondary / no]\n- **Risk level**: [low / medium / high / critical]\n\n### Arbitrage Signal\n- **AH premium z-score**: [X.X] → [fade premium / neutral / buy premium]\n- **Best market to buy**: [A / H / ADR] — rationale\n- **Catalyst**: [index inclusion, Connect eligibility, earnings]\n\n### Investment Implication\n- **Preferred listing**: [H-share / ADR / A-share] for new position\n- **Risk**: [delisting, FX, regulatory, liquidity]\n```\n\n## Notes\n\n- AH premium arbitrage is not freely executable: A-shares and H-shares are NOT fungible (no direct conversion), so true arbitrage requires separate capital pools\n- ADR↔HK conversion IS possible for most dual-listed names (via depositary bank), but takes 2-3 business days and involves fees\n- Currency risk (CNY, HKD, USD) is a major driver of cross-listing premiums; always hedge or account for FX when comparing\n- VIE (Variable Interest Entity) structure adds a layer of legal risk for many Chinese ADRs; this is a structural risk, not a trading signal\n- Stock Connect eligibility requirements mean not all HK-listed Chinese companies are accessible to mainland investors\n- This framework is for research purposes only and does not constitute investment advice\n","frontmatter":{"name":"adr-hshare","description":"ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.","category":"flow"},"isInternal":false,"tokens":2895,"sizeBytes":9985},{"name":"SKILL.md","path":"agent/src/skills/akshare/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/akshare/SKILL.md","title":"akshare","category":"anthropic-skill","format":"markdown","content":"---\nname: akshare\ncategory: data-source\ndescription: AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance.\n---\n\n## Overview\n\nAKShare is a completely free, open-source Python financial data library. No registration or API key required. It aggregates data from public sources (Sina, East Money, etc.) covering Chinese and global markets.\n\n- GitHub: https://github.com/akfamily/akshare (18k+ stars)\n- Install: `pip install akshare`\n\n## Quick Start\n\n```python\nimport akshare as ak\n\n# A-share daily OHLCV (前复权)\ndf = ak.stock_zh_a_hist(symbol=\"000001\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n\n# US stock daily\ndf = ak.stock_us_hist(symbol=\"105.AAPL\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n\n# HK stock daily\ndf = ak.stock_hk_hist(symbol=\"00700\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n```\n\n## Top 10 High-Frequency Interfaces\n\n### A-shares\n\n| Function | Description | Key Params |\n|----------|-------------|------------|\n| `stock_zh_a_hist()` | A-share OHLCV | symbol, period, start_date, end_date, adjust |\n| `stock_zh_a_spot_em()` | Real-time A-share quotes | (none) |\n| `stock_individual_info_em()` | Stock basic info | symbol |\n| `stock_zh_a_hist_min_em()` | Intraday bars | symbol, period(1/5/15/30/60) |\n\n### US / HK\n\n| Function | Description | Key Params |\n|----------|-------------|------------|\n| `stock_us_hist()` | US stock OHLCV | symbol (e.g. \"105.AAPL\"), period, start_date, end_date |\n| `stock_hk_hist()` | HK stock OHLCV | symbol (e.g. \"00700\"), period, start_date, end_date |\n\n### Macro / Forex / Futures\n\n| Function | Description |\n|----------|-------------|\n| `macro_china_gdp()` | China GDP data |\n| `macro_china_cpi()` | China CPI data |\n| `futures_main_sina()` | Futures main contract quotes |\n| `currency_boc_sina()` | BOC forex rates |\n\n## Column Names\n\nAKShare returns Chinese column names by default:\n\n| Chinese | English | Description |\n|---------|---------|-------------|\n| 日期 | date | Trade date |\n| 开盘 | open | Open price |\n| 最高 | high | High price |\n| 最低 | low | Low price |\n| 收盘 | close | Close price |\n| 成交量 | volume | Volume |\n| 成交额 | amount | Turnover |\n| 涨跌幅 | pct_change | % change |\n| 换手率 | turnover_rate | Turnover rate |\n\n## Date Format\n\n- Input: `YYYYMMDD` string (e.g. `\"20240101\"`)\n- Output: `日期` column as string, convert with `pd.to_datetime()`\n\n## Symbol Format\n\n- A-shares: pure digits `\"000001\"` (no .SZ suffix)\n- US stocks: `\"105.AAPL\"` (NASDAQ prefix 105), `\"106.BABA\"` (NYSE prefix 106)\n- HK stocks: `\"00700\"` (5-digit zero-padded)\n\n## Built-in Loader\n\nThe project has a built-in AKShare DataLoader at `backtest/loaders/akshare_loader.py`. When backtesting, the runner automatically falls back to AKShare when tushare/yfinance are unavailable.\n\n## Reference Docs\n\nFor less common interfaces, see the `references/` subdirectory or the official docs at https://akshare.akfamily.xyz/\n","frontmatter":{"name":"akshare","category":"data-source","description":"AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance."},"isInternal":false,"tokens":856,"sizeBytes":3159},{"name":"SKILL.md","path":"agent/src/skills/alpha-zoo/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/alpha-zoo/SKILL.md","title":"alpha-zoo","category":"anthropic-skill","format":"markdown","content":"---\nname: alpha-zoo\ndescription: Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks \"which alphas exist\", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.\ncategory: research\n---\n# Alpha Zoo\n\n## Purpose\n\nWhen the user asks about prebuilt cross-sectional alphas — Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart — or wants to bench a whole zoo on an investable universe (CSI 300, S&P 500, BTC-USDT, ...), this skill orients you. The zoo is the curated library; the bench is the evaluator.\n\n## Tools Available\n\n| Tool | When to use |\n|------|------|\n| `alpha_zoo` | Browse the library. `action=list_alphas` to enumerate (filterable by zoo / theme / universe), `action=get_alpha` for one alpha's metadata, `action=health` for registry load status. |\n| `alpha_bench` | Run IC / IR on one alpha or a whole zoo over a universe + period. Emits an HTML report. |\n| `factor_analysis` | Ad-hoc factor evaluation from a user-supplied factor CSV + return CSV. Use this when the user has their own factor (not in the zoo). |\n\n## Decision Tree\n\n- \"list all momentum alphas\" → `alpha_zoo` with `action=list_alphas, theme=momentum`.\n- \"show me gtja191_alpha_001\" → `alpha_zoo` with `action=get_alpha, alpha_id=gtja191_alpha_001`.\n- \"bench all of GTJA 191 on CSI 300 from 2020 to 2024\" → `alpha_bench` with `zoo=gtja191, universe=csi300, period=2020-2024`.\n- \"is the registry healthy\" → `alpha_zoo` with `action=health` — surfaces `loaded`, `failed`, and per-error reasons.\n- User uploads `my_factor.csv` → `factor_analysis` (zoo tools are for prebuilt alphas only).\n\n## Zoo Inventory\n\n| Zoo | Description | Approx. count |\n|------|------|------|\n| `kakushadze101` | Formulaic alphas from Kakushadze's 2015 paper. Mix of momentum, reversal, volume, and microstructure. | ~101 |\n| `gtja191` | Guotai Junan 191 alphas — A-share focused cross-sectional factors. | ~191 |\n| `qlib158` | Microsoft Qlib's 158 alpha factors — features tuned for ML pipelines. | ~158 |\n| `classical` | Fama-French 3/5-factor + Carhart momentum. | <10 |\n\nCounts are nominal; check `alpha_zoo action=health` for the live count currently loaded.\n\n## Constraints\n\n- **No per-stock per-date factor values are surfaced to the agent.** IC results are aggregate stats (mean / std / IR / positive-ratio); the HTML report shows top-N by IR plus formulas, never the underlying panel.\n- **Lookahead is banned in the operator set.** `delta(df, d)` requires `d >= 1`; the negative-shift `Ref(df, -n)` form does not exist. See `docs/alpha-zoo/spec.md` for the full operator catalogue.\n- **Universe loaders may not be wired for every market yet.** When `alpha_bench` returns `universe loader for X not yet implemented`, that's the W2 scaffold — the universe is recognised but the data pull lands in W4.\n- **Do not expose absolute filesystem paths in agent output.** The bench tool writes to `~/.vibe-trading/reports/` by default; refer to it by that shorthand, not by the resolved absolute path.\n- **`alpha_zoo` is read-only.** `alpha_bench` writes a single HTML file per run — no scratch state elsewhere.\n\n## Common Pitfalls\n\n- Filter mismatch on `list_alphas`: theme / universe must match the alpha's declared metadata exactly (e.g. `equity_cn`, not `cn` or `china`).\n- Calling `alpha_bench` with both `alpha_id` and `zoo` set — they are mutually exclusive; pick one.\n- Empty registry (`loaded=0`) means no zoo modules are populated yet; treat it as \"zoos pending W3 porting\" rather than a bug.\n\n## Reference\n\n- Operator catalogue: `docs/alpha-zoo/spec.md`\n- Registry contract: `src/factors/registry.py` (frozen; do not modify)\n- IC / layered NAV math: `src/factors/factor_analysis_core.py`\n","frontmatter":{"name":"alpha-zoo","description":"Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks \"which alphas exist\", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.","category":"research"},"isInternal":false,"tokens":1036,"sizeBytes":3827},{"name":"SKILL.md","path":"agent/src/skills/ashare-pre-st-filter/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ashare-pre-st-filter/SKILL.md","title":"ashare-pre-st-filter","category":"anthropic-skill","format":"markdown","content":"---\nname: ashare-pre-st-filter\ndescription: A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报，预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示，并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股，不预测财务造假。\ncategory: risk-analysis\n---\n\n> **依赖**：本 skill 必须配合 [tushare skill](../tushare/SKILL.md) 使用，缺数据时回退到 [akshare skill](../akshare/SKILL.md)。\n\n# A 股 ST/*ST 风险预测\n\n## 适用范围\n\n- 仅 A 股**主板 / 创业板 / 科创板**。港股、美股、加密、商品、**北交所**（`8xxxxx.BJ` 监管规则与本 skill 阈值不一致）均不适用，本 skill 直接拒绝。\n- 仅做\"下一财年是否会被 ST/*ST\"的前瞻预测，不做财务造假预测。\n- 输出双轴结果：**触线风险等级** + **预测可信度**。\n\n## 触发条件\n\n用户提问形如：\n- \"分析 000729.SZ 的 ST 风险\"\n- \"600xxx 下个年报会不会被 ST\"\n- \"帮我看一下 300xxx 是否有退市风险\"\n\n收到此类请求时，按下面\"分析流程\"执行；其他场景不要主动启用本 skill。\n\n## 数据获取规范（tushare 优先 → akshare 兜底）\n\n**铁律**：\n\n1. **所有财务/基本面/分红/审计/ST 状态数据，必须先调用 [tushare skill](../tushare/SKILL.md)**，按下表指定的接口名、`ts_code`、`period` 拉取。\n2. 当 tushare 接口因积分不足、token 缺失或返回空时，**回退到 [akshare skill](../akshare/SKILL.md)** 对应函数。\n3. **akshare 兜底数据必须在最终输出\"备注\"中显式标注**：`数据源：akshare（非官方聚合，不保证完整性，建议核对原始公告）`。**不允许只用 akshare 给出\"高可信度\"预测**——akshare 兜底的红线项可信度强制降一档（高→中高，中高→中，中→低）。\n4. 监管处罚（E2）走本 skill 自带的 [scripts/fetch_sina_penalties.py](scripts/fetch_sina_penalties.py)，不经过 tushare/akshare。\n\n### 数据需求映射表\n\n| 数据需求 | tushare 接口（首选） | 抓取参数 / 时间窗口 | akshare 兜底 | akshare 注意事项 |\n|---------|---------------------|-------------------|-------------|----------------|\n| 当前 ST 状态 | `stock_st` | 最近一个交易日 | `stock_zh_a_st_em()` | 仅返回当前 ST/退市整理板列表，历史不可查 |\n| 历史改名 | `namechange` | `ts_code` 全历史 | `stock_zh_a_new_em()`（仅新股，无改名） | akshare 无完整改名接口，必要时跳过 |\n| 股票基础信息（板块判断） | `stock_basic` | `list_status='L'` | `stock_individual_info_em(symbol)` | 字段名不同，需自行映射 |\n| 业绩预告 | `forecast` | `ts_code` + 最近 2 个 period（`{Y}0630`/`{Y}0930`/`{Y}1231`） | `stock_yjyg_em(date='YYYYMMDD')` | 按报告期日期反查，需先按 period 拉全市场再筛 ts_code |\n| 业绩快报 | `express` | 同上，最近 2 个 period | `stock_yjbb_em(date='YYYYMMDD')` | 同上，全市场快照需筛 |\n| 利润表 | `income` | `ts_code` + 最近 5 个 period（过去 4 年年报 `{Y}1231` + 最新中报/三季报） | `stock_financial_report_sina(stock='sh600000', symbol='利润表')` | 单位不一致（万元 vs 元），需做归一；季报缺失常见 |\n| 资产负债表 | `balancesheet` | 同上 | `stock_financial_report_sina(stock='sh600000', symbol='资产负债表')` | 同上 |\n| 现金流量表 | `cashflow` | 同上 | `stock_financial_report_sina(stock='sh600000', symbol='现金流量表')` | 同上 |\n| 财务指标（扣非净利润等） | `fina_indicator` | `ts_code` + 最近 5 个 period | `stock_financial_analysis_indicator(symbol='600000')` | 字段中文，扣非字段名为\"扣除非经常性损益后的净利润\" |\n| 审计意见 | `fina_audit` | `ts_code` + 最近 2 个年报 period | **akshare 无对应接口** | 缺失时在输出中标注\"审计意见数据缺失\"，**不得跳过**——必须提示用户去官网查 |\n| 分红 | `dividend` | `ts_code` + 近 5 年 `div_proc='实施'` | `stock_history_dividend_detail(symbol='600000', indicator='分红')` | 字段中文，需把\"派息\"金额×总股本得到现金分红总额 |\n| 日线行情（1 元退市预警） | `daily` | `ts_code` + 最近 30 个交易日 | `stock_zh_a_hist(symbol='600000', period='daily', adjust='')` | 必须用**不复权**价格判断 1 元退市线 |\n| 每日指标（市值） | `daily_basic` | `ts_code` + 最近一个交易日 | `stock_zh_a_spot_em()` 全市场快照筛 `代码=600000` | 字段名为\"总市值\"（**单位：元**，东方财富 push2 原始口径，akshare 不做缩放），换算成亿元需 `/1e8`。⚠️ 不要与 tushare `daily_basic.total_mv`（万元）混淆——本列是 akshare 兜底口径 |\n| 财报披露日期 | `disclosure_date` | `ts_code` + 当前年度 | **akshare 无对应接口** | 缺失时按经验估算（4/30 年报、8/31 中报、10/31 三季报） |\n| 监管处罚 | — | — | **走本 skill 自带 sina 脚本** | 见 E2 章节 |\n\n### 时间窗口口径统一\n\n按数据类型分别处理，**禁止用单一 60 天经验值统一对齐**：\n\n- **财务报表（income / balancesheet / cashflow / fina_indicator）**：以 `disclosure_date` 实际公告日为准；该接口不可用时按法定截止日（年报 4/30、中报 8/31、三季报 10/31、一季报 4/30）做兜底。在公告日 +1 天起方可视该 period 为\"最新可得\"。\n- **\"过去 N 年年报\"**：一律 `period = {Y}1231`，不要用日历年起止。\n- **业绩预告 forecast / 业绩快报 express**：只取 `ann_date ≥ 当前年度 1 月 1 日` 的记录；按 `ann_date` 倒序取最新一条，老 period 的预告对预测无效。\n- **dividend**：按 `(end_date, ann_date, cash_div_tax)` 三元组**强制去重**——tushare 同一笔分红会出现 3-4 行（预案/股东大会通过/实施），直接累加会三倍误算。\n\n## 第一步：当前状态核查（M0）\n\n**判断公司当前是否已被风险警示，并据此调整分析方向（不一刀切结束）**：\n\n调用顺序：\n1. 优先调用 `stock_st`（tushare skill，3000 积分），按当前最近交易日查询；记录 `type` / `type_name`。\n2. 如积分不足，回退到 `namechange`（免费）：拉取 `ts_code` 全部历史改名记录，取按 `start_date` 排序的最新一条；判断 `name` 前缀。\n3. 兜底用 `stock_basic.name` 做名称确认。\n\n**分支处理**：\n\n| 当前状态 | 分析方向 | 是否继续 |\n|---------|---------|---------|\n| 正常股票 | 预测下一财年是否会被 ST/*ST | **是**（默认流程） |\n| 已 ST（普通） | 改为预测：是否会进一步转 *ST 或退市；R1-R4 阈值需更严格（亏损链门槛降一年） | **是** |\n| 已 *ST | 改为预测：是否会被强制退市；重点看 R1 营收 + R2 净资产 + E1 审计意见 | **是** |\n| 已退市整理期 / 已摘牌 | 直接结束 | **否** |\n\n## 第二步：板块阈值表（决定红线）\n\n**先用 `ts_code` 前缀判断板块，再套用对应阈值。不要混用。**\n\n| 板块 | 代码识别 | 营收红线 | 市值退市线 | 三年累计分红阈值 |\n|------|---------|---------|-----------|----------------|\n| 主板 | `60xxxx.SH` / `00xxxx.SZ`（除 30 / 688 开头外） | 净利润为负 且 营收 < 3 亿 | 市值 < 5 亿 | 累计 < 5000 万 且 < 年均净利润 30% |\n| 创业板 | `30xxxx.SZ` | 净利润为负 且 营收 < 1 亿 | 市值 < 3 亿 | 累计 < 3000 万 且 < 年均净利润 30%（研发豁免：研发投入占营收 ≥ 5% 或近三年累计 ≥ 6000 万） |\n| 科创板 | `688xxx.SH` | 净利润为负 且 营收 < 1 亿 | 市值 < 3 亿 | 累计 < 3000 万 且 < 年均净利润 30%（研发豁免：研发投入占营收 ≥ 5% 或近三年累计 ≥ 6000 万） |\n\n## 第三步：预测时点选择\n\n按\"最新可得证据\"原则确定基准时点，从下到上择优：\n\n| 基准时点 | 数据接口 | 可信度基线 | 适用季节 |\n|---------|---------|-----------|---------|\n| **业绩预告 forecast** | `forecast` | **高** | 1 月底 / 7 月中旬 / 10 月底高发期 |\n| **业绩快报 express** | `express` | **高** | 1-4 月年报前夕 |\n| **三季报** | `income/balancesheet/cashflow` (`period=YYYYMMDD`，9 月末) | **中高** | 10 月底披露后到次年年报前 |\n| **中报** | 同上，6 月末 period | **中** | 8 月底披露后到三季报前 |\n| **去年年报** | 同上，12 月末 period | **低** | 仅作为基准对照 |\n\n**证据优先级**（同一指标多个来源时，高的覆盖低的）：\n\n```\nforecast / express > 三季报 > 中报 > 去年年报 > 经验外推\n```\n\n输出时**必须在每个预测项明示采用了哪一级证据**。如果存在 forecast 给出的全年净利润区间，**禁止再用三季报机械年化覆盖**。\n\n## 第四步：四项可预测红线\n\n### R1 营收 + 净利润红线\n\n**目标**：预测全年营收、归母净利润和**扣非净利润**，按监管口径\"扣非前后孰低者\"判定。\n\n**口径关键**：监管原文是 `min(n_income, profit_dedt) < 0` 且 `revenue < 板块阈值` —— **必须同时预测扣非净利润**，不能只用归母。仅看归母会漏掉\"归母为正但扣非为负\"的造壳公司。\n\n**预测方法（按基准时点选用，至少一种）**：\n\n- **forecast 直采法**：若 `forecast` 给出 `net_profit_min` / `net_profit_max`，直接取区间作为全年归母预测；扣非用过去两年扣非率（`profit_dedt / n_income` 中位数）折算；可信度=高。营收红线仍需结合最新报表外推。\n- **express 直采法**：若 `express` 已披露，`revenue` / `n_income` 即为全年快报值；扣非同样按过去两年率折算（express 一般无扣非字段）；可信度=高。\n- **Q4 单季回补法**（三季报基准）：\n - `Q1Q3_revenue` = 三季报营业收入\n - `Q4_revenue_est` = 过去两年 Q4 营收占全年比例的中位数 × 当前年化预估值\n - `revenue_full_year_est = Q1Q3_revenue + Q4_revenue_est`\n - `n_income` / `profit_dedt` 同法处理\n - 可信度=中高\n- **下半年情景外推法**（中报基准，强季节性公司必须用）：\n - 取过去两年 H2 营收占全年比例均值，给出悲观/基准/乐观三情景\n - `revenue_full_year_est = H1_revenue / H1_share_avg`\n - 可信度=中\n- **同比延续法**（中报基准，弱季节性公司可用）：\n - `revenue_full_year_est = H1_revenue × (last_year_full_revenue / last_year_H1_revenue)`\n - 可信度=中\n\n**禁止**：H1 直接乘 2、Q1-Q3 直接乘 4/3。任何机械年化**强制降到\"低\"可信度**并在输出中注明。\n\n**触线判定**（口径：`worst_profit = min(n_income_est, profit_dedt_est)`）：\n- 高风险：`worst_profit < 0` 且 `revenue_est < 板块阈值`\n- 中风险：`worst_profit < 0` 但营收达标；或营收逼近阈值（< 阈值 × 1.2）\n- 低风险：上述均不满足\n\n> **系数说明**：R1 中风险的 1.2 是预测项预警 buffer（预测本身有误差，需窄 buffer）；R4 高风险的 1.5 是双年联动判定 buffer（两年都亏且营收接近阈值才是真退市信号，需宽 buffer）。两个系数不同是有意为之，**不要错误地统一**。\n\n### R2 年末净资产红线\n\n**目标**：判断年末归母股东权益是否可能转负。\n\n**方法**：\n1. 取最新一期 `balancesheet.total_hldr_eqy_exc_min_int`（最新中报或三季报），记为 `current_eq`。\n2. 计算 `eq_year_end_est = current_eq + 剩余季度净利润预测 − 当前年度已实施现金分红总额`。\n - 剩余季度净利润：中报基准用 R1 的 H2 预测，三季报基准用 R1 的 Q4 预测。\n - 已实施分红总额：见 R3 的去重 + 量纲公式 `cash_div_tax / 10 × total_share`。\n\n**触线判定**（统一相对阈值，单位：元）：\n- 高风险：`current_eq < 0` 或 `eq_year_end_est < 0`\n- 中风险：`0 ≤ eq_year_end_est < 1e8`（1 亿元）\n- 低风险：`eq_year_end_est ≥ 1e8`\n\n**注意**：商誉减值、长期股权投资减值在 Q3/Q4 集中确认，应在备注提示\"若计提大额减值，净资产可能进一步下行\"，但**不得在没有公告依据时擅自减值**。\n\n### R3 分红达标前瞻\n\n**目标**：判断\"近三年累计现金分红 < 年均净利润 30% 且 < 板块累计阈值\"是否会在下一次年度考核时同时满足（监管原文：两条件**同时**触发才警示）。\n\n**单笔分红量纲公式**（**必须用此公式，不要用 `cash_div_tax × base_share`**）：\n\n```\ncash_dividend_total = cash_div_tax / 10 × total_share\n```\n\n其中 `cash_div_tax` 来自 `dividend`（每 10 股派现金额，含税），`total_share` 来自 `daily_basic.total_share`（最新总股本，单位万股 → 需 ×1e4 换成股）或 `stock_basic`。tushare `dividend.base_share` 字段对很多公司为 None，**禁止直接使用**。\n\n**方法**：\n1. 用 `dividend` 拉取该 `ts_code` 近五年记录，按 `(end_date, ann_date, cash_div_tax)` 三元组**强制去重**——同一笔分红 tushare 会返回\"预案/股东大会通过/实施\"3-4 行。\n2. 折算系数（按 `div_proc` 状态）：`实施=1.0` / `股东大会通过=0.7` / `预案=0.5`；同一 `end_date` 取系数最高那条。\n3. 用 `income` 拉取近三年完整年度归母净利润（period=YYYY1231）。\n4. 计算考核窗口 = `[当前年-2, 当前年]`，即\"过去两年已落地分红 + 本年度预计分红\"。\n5. 本年度预计分红：\n - 若 `dividend` 已查到本年度记录：按上面折算系数计入。\n - 否则用\"过去两年平均分红率\"× R1 预测全年净利润（基准情景）做估算，可信度降一档。\n6. 与年均净利润比较：\n - 条件 A：`cumulative_div_3y < 30% × avg_n_income_3y`\n - 条件 B：`cumulative_div_3y < 板块累计阈值`\n7. 创业板 / 科创板若研发投入占比满足豁免条件，备注提示\"研发达标可能豁免\"。\n\n**触线判定**：\n- 高风险：A 和 B 同时满足 **且** 过去三年至少一年净利润为正（即\"盈利年份未达标\"——监管以盈利年为基准）。\n- 中风险：仅一个条件满足；或本年度分红尚未公告。\n- 低风险：累计分红已稳定达标；或过去三年累计净利润 ≤ 0（无分红基础，按 R1 联动）。\n\n**禁止**：本年度尚未公告分红时，不得断言\"必然不达标\"，只能给出概率。\n\n### R4 连续亏损 / 扣非亏损链\n\n**目标**：判断是否会触发\"连续两年扣非前后净利润孰低者为负\" → *ST。\n\n**口径关键**：监管原文是**连续两年**（不是三年），且 *ST 财务类强制退市的法定条件是 **\"亏损 + 营收<板块阈值\" 必须同时满足**——只看亏损链会系统性高估风险（实证：闻泰科技 2024 年亏损但营收超千亿，不会被 *ST）。\n\n**方法**：\n1. 取过去一年完整年报的 `n_income`、`fina_indicator.profit_dedt`，记 `worst_last = min(n_income, profit_dedt)`、`revenue_last`。\n2. 用 R1 已经预测好的 `worst_profit_est` 和 `revenue_full_year_est`（基准情景），对应当前年度。\n3. 检查两年的 `worst < 0` 组合，**并叠加营收联动条件**。\n\n**触线判定**（双条件，亏损链 + 营收联动）：\n- 高风险：`worst_last < 0` 且 `worst_profit_est < 0`，**且**（R1 当前年命中高风险 **或** `revenue_full_year_est < 板块阈值 × 1.5`）——即两年均亏 + 营收处于退市风险区间。\n- 中风险：(a) 连续两年均亏但营收远超阈值（≥ 阈值 × 1.5）——亏损链成立但不会触发财务类 *ST，仍可能因经营恶化转 ST；(b) 仅 `worst_profit_est < 0`（当前年预测亏损，去年盈利，未形成连续）；(c) 仅 `worst_last < 0` 但当前年预测为正且接近 0（< 板块阈值 × 0.5）。\n- 低风险：`worst_last ≥ 0` 且 `worst_profit_est ≥ 0`。\n\n**为什么要叠加营收**：A 股 *ST 财务类强制退市的法定条件是\"扣非前后净利润孰低 < 0 **且** 营收 < 板块阈值\"，二者必须同时满足。仅亏损不达营收阈值的公司（如大型周期股短期亏损）会被市场和监管视作\"周期性亏损\"而非\"持续经营存疑\"。\n\n## 第五步：三项事实/临界证据面（不参与预测可信度）\n\n### E1 审计意见（事实）\n\n调用 `fina_audit`，取最近 2 个完整年报：\n- 高风险：最新年报 `audit_result` 含\"无法表示意见\"或\"否定意见\"；**或单次出现\"保留意见\"**（实证支持：上交所近年规范类 *ST 案例多为单次保留即触发）。\n- 中风险：连续两年同一负面结论但本期已修正；或带强调事项段的无保留意见涉及持续经营。\n- 低风险：标准无保留意见。\n\n**禁止预测下一份审计意见**——审计是事后行为。\n\n**akshare 兜底缺失时的人工核查路径**（`fina_audit` 无 akshare 替代接口，必须给出可点击 URL）：\n\n| 来源 | URL 模板 | 查询方式 |\n|------|---------|---------|\n| 巨潮资讯网（公告原文，权威） | `http://www.cninfo.com.cn/new/disclosure/stock?orgId=&stockCode={code6}` | 进入页面后筛选\"定期报告 → 年度报告\"，下载 PDF 在第十节\"财务报告 → 审计报告\"查 `审计意见类型` |\n| 巨潮高级搜索（按关键词） | `http://www.cninfo.com.cn/new/fulltextSearch/full?searchkey={code6}+审计报告&sdate=&edate=&isfulltext=false&sortName=time&sortType=desc` | 直接命中含\"审计报告\"标题的公告 |\n| 上交所信息披露 | `http://www.sse.com.cn/disclosure/listedinfo/regular/?stockCode={code6}` | 仅限 60xxxx / 688xxx |\n| 深交所信息披露 | `http://www.szse.cn/disclosure/listed/notice/index.html?stock={code6}` | 仅限 00xxxx / 30xxxx |\n\n其中 `{code6}` 为 6 位股票代码（去掉 `.SH` / `.SZ` 后缀）。**强制要求**：当 `fina_audit` 返回空时，输出报告的\"备注\"必须列出上面巨潮的两条 URL 之一，便于用户人工核查；不得只写一句\"审计意见数据缺失\"了事。\n\n### E2 监管处罚（事实，双窗口：上一财年 + 过去 12 个月）\n\n**这是本 skill 的关键证据面。**\n\n**窗口规则（双窗口策略，单次抓取在内存中切片）**：\n\n- 窗口 A — `[当前年-1 的 1 月 1 日, 当前年-1 的 12 月 31 日]`（上一个完整财年）：用于 reason_normalized 单条评级。\n- 窗口 B — `[分析日 - 365 天, 分析日]`（过去 12 个月滚动）：用于**频次增强规则**。\n- 若分析时点已经在 5 月之后且最新年报已披露完毕，窗口 A 可改为 `[最新年报 end_date - 365 天, 最新年报 end_date]`。\n\n**调用方式（推荐：单次拉全量 + 内存切片）**：\n\n```bash\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --no-filter\n```\n\n返回页面全量记录后，由 LLM 在内存中按窗口 A、窗口 B 切两份。也可以用 `--start-date / --end-date` 单独抓取，但同一次分析**避免发起两次 HTTP**。\n\n输出 JSON 列表，字段：`ann_date / event_type / title / reason / reason_normalized / content / issuer / issuer_normalized / subject_normalized / target_relevance / e2_countable / source_url`。\n\n**相关性铁律**：调用脚本时必须传入 `--stock-name {股票简称}`（来自 `stock_basic.name`），必要时用 `--alias` 补充曾用名/简称。新浪页面可能收录“某个人证券账户交易列表里出现目标股票”的记录，这类记录只说明目标股票被交易过，**不是上市公司/股东/董监高处罚**。脚本会将其标为 `target_relevance=security_mention_only` 且 `e2_countable=false`，E2 频次统计必须排除。\n\n**`subject_normalized` 字段**（用于频次权重折算）：\n- `company` — 处罚主体是公司本身（默认；不含个人身份关键词时归此类）\n- `shareholder` — 控股股东 / 实控人 / 5% 以上大股东\n- `officer` — 董事 / 监事 / 高管（董事长、总经理、董秘、财务总监等）\n\n**风险等级判定**：\n\n| 命中 `reason_normalized` | 等级贡献 |\n|---|---|\n| 财务造假 / 虚假陈述 / 信息披露违规 | 高（强烈预警） |\n| 违规担保 / 占用资金 | 高（直接触及 ST 红线） |\n| 内幕交易 / 市场操纵 / 违规减持 | 中 |\n| 其他/unknown | 低（仅作背景） |\n\n**频次增强规则（治理质量预警，窗口 B）**：\n\n监管函本身反映公司治理与内控质量，密集出现是\"规范类 ST\"（保留意见、内控非标、信披重大缺陷）的强领先指标。但**处罚主体不同，治理含义不同**：股东减持违规、董监高短线交易反映的是个人合规问题，不应等同于\"公司治理失序\"。\n\n**主体加权计数**（按 `subject_normalized` 折算后再查表）：\n\n```\n加权条数 = Σ 单条权重\n 其中：subject_normalized == \"company\" → 权重 1.0\n subject_normalized == \"officer\" → 权重 0.5\n subject_normalized == \"shareholder\" → 权重 0.5\n```\n\n| 过去 12 个月监管函/问询/警示函**加权条数** | 频次等级 |\n|---|---|\n| ≥ 5.0 | **极高** |\n| 3.0 - 4.5 | 高 |\n| 2.0 - 2.5 | 中 |\n| < 2.0 | 低 |\n\n**判定来源**：仅当 `e2_countable=true` 时，脚本输出的 `event_type ∈ {警示, 问讯, 监管关注, 监管函}` 或 `issuer_normalized ∈ {上交所, 深交所, 北交所, 证监会, 地方证监局}` 才计入条数；条数按 `subject_normalized` 折算后再查表。`target_relevance=security_mention_only` 的记录必须排除。\n\n**输出要求**：必须同时给出\"原始条数\"和\"加权条数\"两个数字，便于用户审阅折算逻辑。例如：`窗口 B 命中 6 条（公司 2 条 + 股东 3 条 + 高管 1 条 → 加权 4.0 条），频次等级：高`。\n\n**E2 综合等级合成规则**（解决单条评级与频次叠加的问题）：\n\n```\nE2_单条等级 = 窗口 A 内所有 reason_normalized 单条等级的最大值\nE2_频次等级 = 窗口 B 频次表查得\nE2_综合等级 = max(E2_单条等级, E2_频次等级)\n```\n\n**叠加加成**：若同时满足 `E2_频次等级 ≥ 高` 且 `E2_单条等级 ≥ 高`（含财务造假/虚假陈述/违规担保/占用资金）→ **直升极高**。\n\n**为什么要这条规则**：纯量化红线只能捕捉\"财务类 ST\"，无法预警\"规范类 ST\"。频次规则把\"治理失序\"作为前瞻信号纳入。\n\n**注意**：处罚证据**只抬升风险等级，不影响预测可信度**——这是事实证据，不是预测项。\n\n### E3 交易类临界预警（事实）\n\n调用 `daily`（近 30 个交易日，**必须 `adjust=''` 不复权**）和 `daily_basic`（最新一日，单位：万元）：\n\n**1 元退市预警**（用 `daily.close`，**不复权**）：\n- 高风险：近 20 个交易日中收盘价 < 1 元的天数 ≥ 10\n- 中风险：≥ 1 但 < 10\n- 低风险：0 天\n\n**市值退市预警**（用 `daily_basic.total_mv`，单位万元 → ×1e4 换算成元 → /1e8 换算成亿元）：\n\n| 板块 | 高风险阈值 | 中风险阈值 |\n|---|---|---|\n| 主板 | < 5 亿 | < 5 × 1.5 = 7.5 亿 |\n| 创业板 / 科创板 | < 3 亿 | < 3 × 1.5 = 4.5 亿 |\n\n## 第六步：双轴输出模板\n\n每次分析必须按下面模板输出。**风险等级 + 可信度两个维度都要给**。\n\n```markdown\n## ST 风险预测：{name}（{ts_code}）\n\n### 当前状态\n- 是否已 ST/*ST：{是 / 否}\n- 板块：{主板 / 创业板 / 科创板}\n- 基准预测时点：{forecast / express / 三季报（period） / 中报（period）}\n\n### 量化红线预测（双轴）\n\n| 红线 | 基准情景结论 | 风险等级 | 可信度 | 主要证据 |\n|------|-------------|---------|-------|---------|\n| R1 营收+净利润 | 全年营收预测 X 亿，净利润 Y 亿 | 高/中/低 | 高/中高/中/低 | forecast/三季报/... |\n| R2 年末净资产 | 当前 X 亿，年末预计 Y 亿 | 高/中/低 | 高/中高/中/低 | balancesheet + R1 |\n| R3 分红达标 | 三年累计预计 X，对应阈值 Y | 高/中/低 | 中/低 | dividend + income |\n| R4 连续亏损链 | 过去两年 + 当前年是否均亏 | 高/中/低 | 同 R1 | income + fina_indicator |\n\n### 事实证据面（不参与预测可信度）\n\n| 证据 | 结论 | 影响 |\n|------|------|------|\n| E1 审计意见 | 最新结论 | 抬升/无影响 |\n| E2 监管处罚（窗口 A 上一财年 / 窗口 B 过去 12 个月） | A 命中 Na 条（最高单条等级：…），B 原始 Nb_raw 条 / 加权 Nb_w 条（公司 a + 股东 b + 高管 c → a×1.0+b×0.5+c×0.5），频次等级：…，E2_综合等级：… | 抬升/无影响 |\n| E3 交易类临界 | 收盘价/市值情况 | 抬升/无影响 |\n\n### 综合结论\n- **下一财年被 ST/*ST 风险等级**：{极高 / 高 / 中 / 低}\n- **预测综合可信度**：{高 / 中高 / 中 / 低}\n- **关键风险点**（最多 3 条）：...\n- **缓解信号**（如有）：...\n\n### 备注\n- 数据时点：{所用最新报告期}\n- 已忽略的不可预测项：财务造假、未来审计意见\n- 季节性提醒（如适用）：...\n```\n\n## 禁止事项\n\n1. 不预测财务造假（事前不可知）。\n2. 不预测下一份审计意见（事后才出）。\n3. 不在没有公告依据时擅自计提减值。\n4. 不混用板块阈值（主板 vs 创业板/科创板的营收/市值/分红阈值不同）。\n5. 不把机械年化（H1×2、Q1Q3×4/3）伪装成\"高可信度\"。\n6. 不在港股 / 美股 / 加密 / **北交所**（`8xxxxx.BJ`，监管规则与本 skill 阈值不一致）上启用本 skill。\n7. 不把\"违规处罚记录\"计入预测可信度（仅抬升风险等级）。\n8. 不在 R3 分红计算中使用 `cash_div_tax × base_share`（base_share 经常为 None，会得 0）；必须用 `cash_div_tax / 10 × total_share`，且按 `(end_date, ann_date, cash_div_tax)` 三元组去重。\n9. 不在 R1 触线判定中只用 `n_income`；必须用 `min(n_income, profit_dedt)`，否则会漏掉造壳公司。\n10. 不把 R4 写成\"连续三年\"；监管原文是\"连续两年\"。**且 R4 高风险必须叠加营收联动条件**——仅\"连续两年亏损\"不构成 *ST 财务类强制退市，必须同时满足\"营收 < 板块阈值 × 1.5\"或 R1 已命中高风险，否则只能给中风险。\n11. 不把 E2 监管处罚\"按主体一刀切\"计数——必须用 `subject_normalized` 加权（公司 ×1.0 / 股东 ×0.5 / 董监高 ×0.5），否则股东动荡的公司会被系统性高估。\n\n## 依赖的 tushare 接口清单\n\n| 用途 | 接口 | 备注 |\n|------|------|------|\n| 当前 ST 状态 | `stock_st` | 3000 积分；不可用时回退 `namechange` |\n| 历史改名 | `namechange` | 免费 |\n| 板块判断 | `stock_basic` | 免费 |\n| 利润表 | `income` | 2000 积分，多 period 拉取 |\n| 资产负债表 | `balancesheet` | 2000 积分 |\n| 财务指标（扣非等） | `fina_indicator` | 2000 积分 |\n| 业绩预告 | `forecast` | 2000 积分 |\n| 业绩快报 | `express` | 2000 积分 |\n| 审计意见 | `fina_audit` | 500 积分 |\n| 分红 | `dividend` | 2000 积分 |\n| 日线 | `daily` | 免费 |\n| 每日指标（市值） | `daily_basic` | 免费 |\n| 财报披露日期 | `disclosure_date` | 500 积分 |\n\n## 自带脚本\n\n- [scripts/fetch_sina_penalties.py](scripts/fetch_sina_penalties.py) — 抓取新浪财经 vGP_GetOutOfLine 处罚页，stdlib 实现，无外部依赖；reason / issuer 标准化。\n\n调用方式：\n\n```bash\n# 单次拉全量（推荐：本 skill 双窗口策略由 LLM 在内存中切片）\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --no-filter\n\n# 或指定单一窗口\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --start-date 2025-01-01 --end-date 2025-12-31\n```\n\n正常情况下输出 JSON 到 stdout；网络/解析失败或参数非法时返回 `{\"source\": \"unavailable\", \"error\": \"...\"}` 到 stdout 并以非零退出码结束（不抛 traceback），便于 LLM 兜底处理。\n\n## 端到端调用伪代码\n\n用于减少 LLM 调用顺序漂移，按下列骨架组织：\n\n```python\nimport os, json, subprocess\nimport tushare as ts\n\nts_code = '000729.SZ'\npro = ts.pro_api(os.environ['TUSHARE_TOKEN'])\n\n# === Step 0: M0 当前状态 ===\nbasic = pro.stock_basic(ts_code=ts_code, fields='ts_code,name,market').to_dict('records')[0]\nst_hit = pro.stock_st() # 失败则回退 namechange\nname_chg = pro.namechange(ts_code=ts_code).sort_values('start_date', ascending=False)\n\n# === Step 1: 板块判断 ===\nboard = '科创板' if ts_code.startswith('688') else '创业板' if ts_code.startswith('30') else '主板'\nthreshold_revenue = 1e8 if board != '主板' else 3e8\nthreshold_mv = 3e8 if board != '主板' else 5e8\n\n# === Step 2: 拉财务（最新 5 期 + 过去 4 年年报） ===\nincome = pro.income(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nbalance = pro.balancesheet(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nfina_ind = pro.fina_indicator(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nforecast = pro.forecast(ts_code=ts_code).sort_values('ann_date', ascending=False)\nexpress = pro.express(ts_code=ts_code).sort_values('ann_date', ascending=False)\n\n# === Step 3: R1-R4 计算（按各章节口径，注意 min(n_income, profit_dedt)） ===\n# ... 略\n\n# === Step 4: E1 审计 ===\naudit = pro.fina_audit(ts_code=ts_code).sort_values('end_date', ascending=False).head(2)\n\n# === Step 5: E2 监管处罚（双窗口 + 主体加权）===\nfrom datetime import date, timedelta\nresult = subprocess.run([\n 'python', 'agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py',\n '--ts-code', ts_code, '--stock-name', basic['name'], '--no-filter',\n], capture_output=True, text=True)\npenalties = json.loads(result.stdout).get('records', [])\n\n# 在内存中切双窗口（避免两次 HTTP）\nthis_year = date.today().year\nwin_a_start, win_a_end = f'{this_year-1}-01-01', f'{this_year-1}-12-31'\nwin_b_start = (date.today() - timedelta(days=365)).isoformat()\nwin_b_end = date.today().isoformat()\n\ndef _in(rec, s, e):\n d = rec.get('ann_date') or ''\n return bool(d) and s <= d <= e\n\nwin_a = [r for r in penalties if _in(r, win_a_start, win_a_end)]\nwin_b = [r for r in penalties if _in(r, win_b_start, win_b_end)]\n\n# 窗口 A：单条等级取最高\nREASON_LEVEL = {\n '财务造假': 3, '虚假陈述': 3, '信息披露违规': 3,\n '违规担保': 3, '占用资金': 3,\n '内幕交易': 2, '市场操纵': 2, '违规减持': 2,\n}\nlevels_a = [REASON_LEVEL.get(r['reason_normalized'], 1) for r in win_a]\ne2_single = max(levels_a) if levels_a else 0\n\n# 窗口 B：原始条数 + 主体加权条数\nFREQ_EVENT_TYPES = {'警示', '问讯', '监管关注', '监管函', '警示函'}\nFREQ_ISSUERS = {'上交所', '深交所', '北交所', '证监会', '地方证监局'}\nSUBJECT_WEIGHT = {'company': 1.0, 'shareholder': 0.5, 'officer': 0.5}\n\nfreq_pool = [r for r in win_b\n if r.get('e2_countable', True)\n and (r['event_type'] in FREQ_EVENT_TYPES\n or r['issuer_normalized'] in FREQ_ISSUERS)]\nnb_raw = len(freq_pool)\nnb_weighted = round(sum(SUBJECT_WEIGHT.get(r['subject_normalized'], 1.0)\n for r in freq_pool), 1)\n\n# 频次等级（基于加权条数）\nif nb_weighted >= 5.0: e2_freq = 4 # 极高\nelif nb_weighted >= 3.0: e2_freq = 3 # 高\nelif nb_weighted >= 2.0: e2_freq = 2 # 中\nelse: e2_freq = 1 # 低\n\ne2_overall = max(e2_single, e2_freq)\n# 叠加加成：频次高 + 单条高同时命中 → 直升极高\nif e2_freq >= 3 and e2_single >= 3:\n e2_overall = 4\n\n# === Step 6: E3 交易临界 ===\ndaily = pro.daily(ts_code=ts_code).sort_values('trade_date', ascending=False).head(30)\ndaily_basic = pro.daily_basic(ts_code=ts_code).sort_values('trade_date', ascending=False).head(1).iloc[0]\ntotal_mv_yi = daily_basic['total_mv'] * 1e4 / 1e8 # 万元 → 亿元\n\n# === Step 7: 双轴合成输出 ===\n# 风险等级 = max(R1, R2, R3, R4, E1, E2_综合, E3)\n# 可信度 = 加权平均(R1, R2, R3, R4 各项可信度)，akshare 兜底项降一档\n```\n","frontmatter":{"name":"ashare-pre-st-filter","description":"A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报，预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示，并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股，不预测财务造假。","category":"risk-analysis"},"isInternal":false,"tokens":12672,"sizeBytes":32313},{"name":"SKILL.md","path":"agent/src/skills/asset-allocation/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/asset-allocation/SKILL.md","title":"asset-allocation","category":"anthropic-skill","format":"markdown","content":"---\nname: asset-allocation\ndescription: Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.\ncategory: asset-class\n---\n\n# Asset Allocation and Portfolio Optimization\n\n## Overview\n\nFrom asset allocation theory to practical implementation, this skill covers classical frameworks (MPT, BL, risk budgeting, all-weather) and the usage of the four optimizers built into this system. The output can be written directly into `config.json`.\n\n## Asset Allocation Theory\n\n### 1. Modern Portfolio Theory (MPT, Markowitz)\n\n**Core idea**: maximize expected return for a given level of risk (the efficient frontier).\n\n```\nOptimization problem:\nmin w'Σw (portfolio variance)\ns.t. w'μ = target_return\n Σw = 1\n w ≥ 0 (no shorting)\n```\n\n| Advantages | Disadvantages |\n|------|------|\n| Mathematically rigorous | Extremely sensitive to inputs (garbage in, garbage out) |\n| Efficient frontier is visualizable | Concentrated-allocation problem (often produces extreme weights) |\n| Foundational framework | Assumes normality and ignores fat tails |\n\n**Practical advice**: do not use raw MPT directly. Add constraints (upper/lower bounds, sector limits) or use a regularized version.\n\n### 2. Black-Litterman Model\n\n**Core idea**: start from market equilibrium and incorporate investor views.\n\n```\nSteps:\n1. Reverse-imply market equilibrium returns: π = δΣw_mkt\n2. Build the view matrices: P (selection matrix), Q (view returns), Ω (view uncertainty)\n3. Blend the posterior: μ_BL = [(τΣ)^-1 + P'Ω^-1 P]^-1 [(τΣ)^-1 π + P'Ω^-1 Q]\n4. Run Markowitz optimization using posterior μ_BL\n```\n\n**Example views**:\n- Absolute view: \"China A-shares will return 10% over the next year\" → `P=[1,0,0], Q=[0.10]`\n- Relative view: \"China A-shares will outperform US equities by 5%\" → `P=[1,-1,0], Q=[0.05]`\n\n**Parameter guidance**:\n- `τ` (uncertainty scaling): `0.025-0.05`\n- `Ω`: set according to view confidence, where higher confidence = smaller variance\n\n### 3. Risk Budgeting\n\n**Core idea**: allocate by risk contribution rather than by capital share.\n\n```\nRisk contribution: RC_i = w_i × (Σw)_i / σ_p\nTarget: RC_i / σ_p = budget_i (for all i)\n```\n\n| Strategy | Risk Budget | Best Use Case |\n|------|---------|---------|\n| Equal risk contribution | Each asset 1/N | When you do not know which asset is best |\n| Equity-tilted risk budget | Stocks 60%, bonds 30%, commodities 10% | When you want equities to contribute more risk |\n| Dynamic risk budget | Adjust dynamically by signal strength | When you have market-timing ability |\n\n### 4. All-Weather Strategy\n\n**Bridgewater framework**: allocate risk equally across economic environments.\n\n```\nEconomic environment Asset allocation\n───────── ─────────\nGrowth rising Equities + commodities + corporate bonds\nGrowth falling Government bonds + inflation-protected bonds\nInflation rising Commodities + inflation-protected bonds + EM debt\nInflation falling Equities + government bonds\n\nSimplified allocation example for China-focused portfolios:\n- 30% CSI 300 / CSI 500\n- 40% government bonds / credit bonds\n- 15% gold\n- 15% commodities / REITs\n```\n\n## Guide to the 5 Optimizers\n\n### Overview of the Built-In Optimizers\n\nConfigure them in `config.json` through `optimizer` and `optimizer_params`:\n\n| optimizer | Display Name | Core Idea | Best Use Case |\n|-----------|--------|---------|---------|\n| `equal_volatility` | Equal Volatility | Allocate weights by inverse volatility | Simple and effective baseline |\n| `risk_parity` | Risk Parity | Equalize risk contribution while accounting for correlation | Long-term robust allocation |\n| `mean_variance` | Mean-Variance | Maximize Sharpe ratio or minimize variance | When return forecasts are available |\n| `max_diversification` | Maximum Diversification | Maximize the diversification ratio | When pursuing a low-correlation portfolio |\n| `turnover_aware` | Turnover-Aware | Mean-variance utility with an L1 penalty on weight changes vs the previous rebalance | When trading costs matter; tune `turnover_penalty` to your data frequency |\n\n### 1. `equal_volatility`\n\n```json\n{\n \"optimizer\": \"equal_volatility\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: `w_i = (1/σ_i) / Σ(1/σ_j)`\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Volatility calculation window (trading days) |\n\n**Advantages**: simple and fast, no return forecast required, no correlation matrix required. \n**Disadvantages**: ignores cross-asset correlation.\n\n### 2. `risk_parity`\n\n```json\n{\n \"optimizer\": \"risk_parity\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: solve for weights such that each asset contributes the same amount of risk.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Covariance-matrix estimation window |\n\n**Advantages**: accounts for correlation, spreads risk more evenly, and is robust over long horizons. \n**Disadvantages**: requires iterative solving and is sensitive to covariance estimates.\n\n### 3. `mean_variance`\n\n```json\n{\n \"optimizer\": \"mean_variance\",\n \"optimizer_params\": {\n \"lookback\": 60,\n \"risk_free\": 0.0\n }\n}\n```\n\n**Principle**: Markowitz optimization that maximizes the Sharpe ratio.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Window for estimating means and covariances |\n| risk_free | 0.0 | Risk-free rate (annualized) |\n\n**Advantages**: theoretically optimal (if inputs are accurate). \n**Disadvantages**: extremely sensitive to inputs, prone to extreme weights, and often performs poorly out of sample. \n**Recommendation**: do not make `lookback` too short (`<30` easily overfits), and add upper/lower weight constraints.\n\n### 4. `max_diversification`\n\n```json\n{\n \"optimizer\": \"max_diversification\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: maximize `DR = (w'σ) / σ_p` (the diversification ratio).\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Calculation window |\n\n**Advantages**: does not require return forecasts and seeks true diversification. \n**Disadvantages**: effectiveness is limited in highly correlated environments.\n\n### 5. `turnover_aware`\n\n```json\n{\n \"optimizer\": \"turnover_aware\",\n \"optimizer_params\": {\n \"lookback\": 60,\n \"risk_aversion\": 1.0,\n \"turnover_penalty\": 0.5\n }\n}\n```\n\n**Principle**: minimize `-w'μ + λ·w'Σw + γ·||w - w_prev||₁` subject to long-only, fully-invested weights — mean-variance utility with an L1 penalty on weight changes versus the previous rebalance, so the optimizer only trades when the expected improvement outweighs the (implicit) cost.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Calculation window |\n| risk_aversion | 1.0 | Weight on the variance term (λ) |\n| turnover_penalty | 0.0 | Weight on the L1 turnover term (γ); 0 reduces to the mean-variance baseline |\n\n**Advantages**: dampens rebalancing churn, which usually dominates realized costs; the first rebalance is unpenalized so the cold start is undistorted. \n**Disadvantages**: `turnover_penalty` is scale-sensitive to the return frequency of the input window — for daily returns even γ ≈ 0.5 strongly prefers holding still, so tune it per data frequency.\n\n### Optimizer Selection Decision Tree\n\n```\nDo you have return forecasts?\n├── Yes → Do trading costs / churn matter?\n│ ├── Yes → turnover_aware (tune turnover_penalty to data frequency)\n│ └── No → mean_variance (remember to add constraints)\n└── No → Do you need to account for correlation?\n ├── Yes → risk_parity (recommended default)\n └── No → Are volatility differences across assets large?\n ├── Yes → equal_volatility\n └── No → max_diversification\n```\n\n## Rebalancing Strategy\n\n### Three Rebalancing Triggers\n\n| Method | Trigger Condition | Advantages | Disadvantages |\n|------|---------|------|------|\n| Periodic rebalancing | Fixed monthly / quarterly date | Simple, predictable trading cost | May miss or delay adjustments |\n| Threshold trigger | Deviation from target weight > X% | Trades only when needed | Frequent trading in high-volatility markets |\n| Volatility trigger | VIX / volatility breaks a threshold | Adapts to market regime | Parameter selection is difficult |\n\n### Suggested Rebalancing Frequency\n\n| Asset Class | Suggested Frequency | Threshold |\n|---------|---------|------|\n| Equity portfolio | Monthly | ±5% |\n| Stock-bond mix | Quarterly | ±10% |\n| Global macro | Quarterly / semiannual | ±10% |\n| Cryptocurrency | Weekly / biweekly | ±15% (high volatility) |\n\n### Rebalancing in Backtests\n\nImplement rebalancing logic in `signal_engine.py`:\n\n```python\n# Periodic rebalancing example (every 20 trading days)\nif bar_count % rebalance_freq == 0:\n # Recompute weights\n new_weights = calculate_target_weights(data_map)\n for code, weight in new_weights.items():\n signals[code].iloc[i] = weight\n```\n\n## Cross-Asset Correlation Analysis\n\n### Typical Correlation Matrix (China-Focused Portfolio Example)\n\n| | CSI 300 | CSI 500 | Government Bonds | Gold | BTC |\n|--|--------|--------|------|------|-----|\n| CSI 300 | 1.00 | 0.85 | -0.15 | 0.05 | 0.10 |\n| CSI 500 | 0.85 | 1.00 | -0.10 | 0.03 | 0.12 |\n| Government Bonds | -0.15 | -0.10 | 1.00 | 0.20 | -0.05 |\n| Gold | 0.05 | 0.03 | 0.20 | 1.00 | 0.15 |\n| BTC | 0.10 | 0.12 | -0.05 | 0.15 | 1.00 |\n\n**Key patterns**:\n- Negative stock-bond correlation is the foundation of allocation (but it does not always hold; in 2022 both stocks and bonds sold off)\n- Gold has low correlation with equities and serves as a hedge\n- BTC's correlation with traditional assets is unstable and tends to become positive in crises\n- Large-cap versus small-cap China A-shares have high correlation (`0.85`), so diversification benefits are limited\n\n## Output Format\n\n```markdown\n## Asset Allocation Recommendation\n\n### Allocation Plan\n| Asset | Weight | Risk Contribution | Expected Return (Annualized) |\n|------|------|---------|--------------|\n| CSI 300 | 30% | 45% | 8% |\n| Government Bond ETF | 40% | 15% | 3% |\n| Gold | 15% | 20% | 5% |\n| BTC | 15% | 20% | 15% |\n\n### Optimizer Configuration\n```json\n{\n \"optimizer\": \"risk_parity\",\n \"optimizer_params\": {\"lookback\": 60}\n}\n```\n\n### Expected Risk / Return\n| Metric | Value |\n|------|-----|\n| Expected annualized return | 7.2% |\n| Expected annualized volatility | 8.5% |\n| Expected Sharpe | 0.85 |\n| Expected maximum drawdown | -12% |\n\n### Rebalancing Rules\n- Frequency: quarterly (first trading day of March / June / September / December)\n- Threshold: trigger when any asset deviates from target by ±10%\n- Cost: estimated annual trading cost 0.15%\n```\n\n## Notes\n\n1. **The optimizer needs enough instruments**: at least 3 instruments are needed for meaningful optimization; with 2 instruments, `equal_volatility` is usually enough\n2. **`lookback` window**: too short (`<20`) is noisy, too long (`>120`) reacts slowly, and 60 is a reasonable default\n3. **`mean_variance` trap**: it is the easiest to overfit, and out-of-sample Sharpe is often cut by half or more\n4. **Rebalancing cost**: frequent rebalancing eats into returns; for China A-share portfolios, stamp duty of 0.05% plus commissions is material\n5. **Cross-market allocation**: use `\"source\": \"auto\"` in `config.json`, and let `codes` mix instruments from different markets\n6. **Leverage constraint**: the sum of weights must be ≤ 1.0, and leverage is not allowed unless explicitly specified\n7. **Survivorship bias**: historical correlations may be distorted by delistings and new listings\n","frontmatter":{"name":"asset-allocation","description":"Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.","category":"asset-class"},"isInternal":false,"tokens":3014,"sizeBytes":11867},{"name":"SKILL.md","path":"agent/src/skills/backtest-diagnose/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/backtest-diagnose/SKILL.md","title":"backtest-diagnose","category":"anthropic-skill","format":"markdown","content":"---\nname: backtest-diagnose\ndescription: Diagnose failed or underperforming backtests, locate the root cause, and fix the issue\ncategory: tool\n---\n\n# Backtest Diagnosis\n\n## Overview\n\nUse this skill when a user reports that a backtest failed, raised an error, or produced poor results.\n\n## Diagnostic Workflow\n\n1. **Read existing artifacts**: use `read_file` to inspect `artifacts/metrics.csv`, `equity.csv`, and `trades.csv`\n2. **Read the code**: use `read_file` to inspect `code/signal_engine.py` and `config.json`\n3. **Classify the issue**: determine the root cause using the error taxonomy below\n4. **Apply the fix**: use `edit_file` to modify the code, then rerun the backtest\n5. **Verify the fix**: use `read_file` to inspect the new `metrics.csv`\n\n## Error Taxonomy\n\n### Runtime Errors (`exit_code != 0`)\n\n| Error Type | Common Cause | Fix |\n|---------|---------|---------|\n| ImportError | Missing dependency | `bash(\"pip install xxx\")` |\n| KeyError | DataFrame column-name mismatch | Check the actual column names in `data_map` |\n| IndexError | Empty data or insufficient length | Add length checks |\n| TypeError | Incorrect signal type | Ensure the return value is `pd.Series` |\n\n### Logic Bugs (Backtest Succeeds but Results Are Abnormal)\n\n1. **Zero trades** (`trade_count=0`): signal-logic bug. Conditions are too strict, so the signal stays at 0. Check whether entry and exit logic is reasonable, and inspect the signal series to confirm it is not all zeros.\n2. **Late trades** (first trade occurs more than 2 years after the backtest start): data-filtering bug. The lookback window may be too long, or the initial data segment may have been dropped. Shorten the window or check whether `dropna` is too aggressive.\n3. **Capital utilization < 50%** (mostly in cash): position-sizing bug. Signal triggers may be too sparse, or the position-sizing logic may be wrong.\n4. **Open position at the end** (a position still exists when the backtest ends): exit-timing bug. Forced liquidation may be missing, or exit logic does not cover the final segment.\n\n### Data Errors\n\n| Symptom | Root Cause | Fix |\n|------|------|---------|\n| No data fetched | Invalid API token or code issue | Check `config.json` |\n| Too little data | Date range too narrow | Expand the date range |\n\n### Data-Source Error Ignore List\n\nIf you encounter the following keywords, **do not modify the code**. The problem is on the data-provider side:\n- a provider-side \"no data available\" response\n- `rate limit`\n- `API limit`\n- `daily limit`\n- `Information` (common in Tushare API responses)\n\nThese issues require the user to check the API token, switch data sources, or wait for the quota to reset.\n\n## Hard-Gate Checklist\n\n1. `artifacts/metrics.csv` exists and is non-empty\n2. `artifacts/equity.csv` exists and is non-empty\n3. `trade_count > 0` (`0` trades means a signal bug)\n4. The equity series contains no `NaN`\n5. `exit_code == 0`\n\n## Evidence hookup\n\nThis Hard-Gate Checklist is also the evidence ingestion gate for Strategy Discovery: a run failing any gate produces **no evidence rows** — never partial rows — and is skipped with a stable machine-readable token (`hard-gate:exit-nonzero`, `hard-gate:metrics-missing`, `hard-gate:zero-trades`, `hard-gate:equity-empty`, `hard-gate:equity-nan`). Diagnose and fix the failing gate as usual, rerun the backtest, then repopulate the evidence cache with `refresh_strategy_evidence` (agent tool / MCP tool, or `vibe-trading strategy-evidence refresh --manifest `) so the fixed run becomes queryable evidence. See the `strategy-discovery` skill for the manifest format and the full gate list.\n\n## Fixing Principles\n\n- Use **edit_file** to make precise code fixes instead of rewriting the entire file with `write_file`, unless the structure is fundamentally broken\n- Fix the bug only, do not change strategy logic unless the user explicitly asks\n- Fix one issue at a time, and rerun the backtest immediately after each fix\n- Limit yourself to at most 3 repair iterations\n\n## Post-Fix Validation Rules\n\nAfter modifying `signal_engine.py`, you must confirm:\n1. **AST syntax passes**: `bash(\"python -c \\\"import ast; ast.parse(open('code/signal_engine.py').read()); print('OK')\\\"\")`\n2. **Contains `class SignalEngine`**: the file must define `class SignalEngine`\n3. **Contains `def generate`**: the class must contain a `def generate` method\n4. **Rerun the backtest**: after the fix, rerun the backtest and verify the results\n\n## `action_items` Writing Rules\n\nAfter diagnosis, output actionable improvement suggestions:\n- Format: `\"Change X from A to B\"` or `\"Add X logic in signal_engine.py\"`\n- Be specific about parameter values, filenames, and function names\n- Provide at least 2 items\n- Examples:\n - `\"Change RSI threshold from 30 to 25 in signal_engine.py line 42\"`\n - `\"Add signals = signals.fillna(0) after signal calculation to prevent NaN propagation\"`\n - `\"Add a volume filter: skip buy signals when volume is below the 20-day average\"`\n","frontmatter":{"name":"backtest-diagnose","description":"Diagnose failed or underperforming backtests, locate the root cause, and fix the issue","category":"tool"},"isInternal":false,"tokens":1205,"sizeBytes":4981},{"name":"SKILL.md","path":"agent/src/skills/behavioral-finance/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/behavioral-finance/SKILL.md","title":"behavioral-finance","category":"anthropic-skill","format":"markdown","content":"---\nname: behavioral-finance\ndescription: \"Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.\"\ncategory: analysis\n---\n\n# Behavioral Finance Applications\n\n## Overview\n\nTranslate behavioral-finance theory into quantifiable trading signals and risk-control rules. Core assumption: market participants systematically deviate from rational decision-making, and these biases can be predicted and exploited.\n\nApplicable scenarios:\n- Behavioral interpretation and parameter optimization for momentum / reversal strategies\n- Contrarian signals when market sentiment becomes extreme\n- Debiasing mechanisms in portfolio construction\n- Capturing behavior patterns specific to retail-driven China A-share markets\n\n## Core Concepts\n\n### Overreaction and Underreaction\n\n**Underreaction** → momentum effect:\n```\nMechanism: anchoring bias + conservatism\n Investors anchor on old information and update insufficiently to new information\n After an earnings beat, the stock price digests it gradually rather than all at once\nChina A-share evidence:\n - Earnings-guidance beats still produce 3-5% excess return over the following 20 days\n - After analyst rating upgrades, momentum often persists for 1-3 months\nQuant signal:\n SUE (standardized unexpected earnings) > 2σ -> buy and hold for 60 days\n Top 10% 20-day return -> continue holding for 20 days (China A-share momentum cycles are shorter)\n```\n\n**Overreaction** → reversal effect:\n```\nMechanism: representativeness heuristic + availability bias\n Investors extrapolate recent trends too aggressively and ignore mean reversion\n Panic / euphoria drives reactions beyond what fundamentals support\nChina A-share evidence:\n - Rebounds after consecutive limit-downs (after 3 limit-downs, the average 20-day rebound is 8%)\n - Big annual losers often earn 5-10% excess return the next year\nQuant signal:\n Bottom 10% of 250-day return -> buy and hold for 250 days\n RSI(5) < 10 -> short-term rebound signal (5-10 days)\n```\n\n**Key distinction**:\n| Dimension | Underreaction (Momentum) | Overreaction (Reversal) |\n|------|------------------|------------------|\n| Time scale | 1-12 months | <1 week or >12 months |\n| Information type | Clear events (earnings / announcements) | Ambiguous information (sentiment / trend) |\n| Best China A-share window | 20-60 days | 5-10 days (short term) / 1 year (long term) |\n\n### Cognitive Bias Checklist\n\n**Individual decision biases**:\n| Bias | Manifestation | Quant Detection | Debiasing Strategy |\n|------|------|----------|------------|\n| Loss aversion | Hold losing stocks, sell winners too early | Holding period: losing positions > winning positions by 2-3x | Pre-set stop-loss line and execute mechanically |\n| Overconfidence | Overtrading, concentrated positions | Monthly turnover > 100%, single-stock weight > 30% | Limit the number of trades per month |\n| Anchoring effect | Anchoring to entry price / historical highs | Abnormal volume expansion near the entry price | Use relative valuation instead of absolute price |\n| Confirmation bias | Focus only on information that supports the existing view | Single-source information, ignoring bearish news | Force reading the opposing view |\n| Recency bias | Overweight recent events | Recent gains/losses have too much influence on position size | Lengthen the evaluation window (≥60 days) |\n| Framing effect | Same information framed differently leads to different decisions | Decision differences between return format and absolute-PnL format | Evaluate consistently in return space |\n\n**Group behavior biases**:\n| Bias | Manifestation | China A-share Characteristics | Quant Indicator |\n|------|------|---------|----------|\n| Herding | Chasing rallies and panic-selling together | Extremely fast sector rotation (3-5 days) | Intra-sector stock correlation > 0.8 |\n| Information cascades | Ignoring private information and following public signals | Sector follow-through after a leader stock hits limit-up | Sector return on the day after leader-stock limit-up |\n| Attention effect | Buying stocks that attract attention | Explosive turnover in limit-up / news-driven stocks | Abnormal turnover > 3x average |\n\n### Investor Sentiment Cycle\n\n```\nFear -> Caution -> Optimism -> Excitement -> Euphoria -> Denial -> Panic -> Fear\n | | | | | | |\n Bottom Recovery Mid-uptrend Pre-top Top Early selloff Pre-bottom\n\nQuant sentiment indicators:\n 1. Closed-end fund discount: discount > 15% -> extreme fear -> buy signal\n 2. Margin-financing growth: monthly growth > 20% -> euphoria -> reduce position\n 3. New account openings: weekly openings > 2x average -> overheated market\n 4. Turnover ratio: All-A daily turnover > 3% -> euphoric; < 0.5% -> deeply depressed\n 5. Number of limit-up stocks: > 100 -> euphoric; < 10 -> weak\n```\n\n## Analysis Framework\n\n### 1. Disposition-Effect Signal\n\n**Principle**: investors tend to sell winners and hold losers. Once winning positions are largely cleared, selling pressure eases; when trapped holders are deeply underwater, selling pressure can also ease.\n\n```\nChina A-share application:\n Compute the profit ratio in the chip-distribution structure:\n - Profit ratio > 90% and shrinking volume -> winners are reluctant to sell -> may continue rising\n - Profit ratio > 90% and expanding volume -> winners are exiting -> topping signal\n - Profit ratio < 10% and shrinking volume -> low willingness to cut losses -> bottom stabilization\n - Profit ratio < 10% and expanding volume -> panic selling -> short-term oversold\n\nQuant implementation:\n capital_gain_overhang = (current_price - avg_cost) / avg_cost\n where avg_cost is approximated by 60-day VWAP\n CGO > 0.2 -> strong unrealized gains, watch for disposition-effect selling pressure\n CGO < -0.3 -> deeply trapped holders, selling pressure may actually ease\n```\n\n### 2. Composite Sentiment Indicator\n\n```python\n# Multi-dimensional sentiment score (0-100, 50 = neutral)\nsentiment_components = {\n 'turnover_ratio': normalize(all_a_turnover, historical_percentile), # weight 25%\n 'margin_growth': normalize(monthly_margin_growth, historical_percentile), # weight 25%\n 'new_high_ratio': normalize(new_high_ratio, historical_percentile), # weight 20%\n 'limit_up_count': normalize(limit_up_count, historical_percentile), # weight 15%\n 'fund_discount': normalize(closed_end_fund_discount, historical_percentile), # weight 15% (inverse)\n}\n\nsentiment_score = weighted_sum(components)\n# > 80: extreme greed -> cut exposure below 60%\n# 60-80: optimistic -> maintain normal exposure\n# 40-60: neutral -> keep exposure unchanged\n# 20-40: pessimistic -> add gradually\n# < 20: extreme fear -> increase exposure above 80%\n```\n\n### 3. Behavioral Optimization of Momentum Strategies\n\nTraditional momentum (sorting by past 12-month returns) is unstable in China A-shares. A behavioral-finance perspective suggests the following optimizations:\n\n```\nOptimization 1: Separate sentiment momentum from fundamental momentum\n Sentiment momentum = part of recent price rise with no fundamental support -> short-term reversal\n Fundamental momentum = price rise consistent with earnings revisions -> can persist\n Trade: buy stocks with \"strong fundamental momentum + weak sentiment momentum\"\n\nOptimization 2: Attention-weighted momentum\n High-attention retail names reverse faster\n Indicator: if abnormal turnover > 3x average, cut momentum holding period by 50%\n Example: if a normal momentum basket holds for 60 days, high-attention names hold only 30 days\n\nOptimization 3: Combine cross-sectional momentum and time-series momentum\n Cross-sectional: relative strength (top 20% in return ranking)\n Time-series: absolute trend (price > MA60)\n Both satisfied -> strong signal; only one satisfied -> half position\n```\n\n### 4. Contrarian Trading Signals\n\n```\nExtreme-fear buy conditions (at least 3 items):\n □ Shanghai Composite RSI(5) < 15\n □ All-A daily turnover < 0.5%\n □ Weekly margin-financing decline > 5%\n □ Limit-up count < 10 and limit-down count > 50\n □ Closed-end fund discount > 15%\n\nExtreme-greed sell conditions (at least 3 items):\n □ Shanghai Composite RSI(5) > 90\n □ All-A daily turnover > 3%\n □ Weekly margin-financing growth > 10%\n □ Limit-up count > 150\n □ Weekly increase in new account openings > 100%\n```\n\n## Output Format\n\nBehavioral-finance analysis report:\n```\n=== Market Sentiment Diagnosis ===\nDate: 2026-03-28\nSentiment score: 72/100 (optimistic bias)\nCurrent phase: transition from optimism to excitement\n\n=== Behavioral-Bias Signals ===\nOverreaction detection: 127 stocks rose > 15% in the past 5 days -> 65% probability of short-term reversal\nDisposition effect: winner-clearing ratio is low (35%) -> overhead selling pressure remains\nHerding effect: sector correlation 0.85 -> severe follow-the-leader behavior, divergence likely soon\n\n=== Strategy Recommendations ===\nMomentum strategy: shorten holding period from 60 days to 30 days (market attention is elevated)\nContrarian signal: not triggered (sentiment is not yet extreme)\nPosition suggestion: maintain 70% exposure, and prioritize names with \"strong fundamental momentum + weak sentiment momentum\"\n\n=== Debiasing Checklist ===\n□ Are you overconfident because of recent profits? -> check position concentration\n□ Are you anchored to your entry price? -> re-evaluate using current PE/PB\n□ Are you ignoring bearish information? -> force yourself to read bearish research reports\n```\n\n## Notes\n\n1. **High retail participation in China A-shares**: behavioral-bias signals are more pronounced than in US equities, but sector rotation is also faster, so momentum windows should be shorter\n2. **Lag in sentiment indicators**: margin-financing balance is released T+1, and new account openings are weekly, so they are not suitable for intraday trading\n3. **Structural changes**: after 2019, foreign capital and quant participation rose, so the effectiveness of traditional behavioral-finance signals may have weakened\n4. **Behavioral factors correlate with traditional factors**: disposition-effect factors correlate about 0.3-0.5 with momentum, so control collinearity\n5. **Overfitting risk**: behavioral stories are easy to explain after the fact, so out-of-sample validation is mandatory\n6. **Extreme sentiment is rare**: extreme fear / greed appears only 2-3 times per year, so strategy capacity is limited\n\n## Dependencies\n\n```bash\npip install pandas numpy scipy\n```\n","frontmatter":{"name":"behavioral-finance","description":"Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.","category":"analysis"},"isInternal":false,"tokens":2399,"sizeBytes":10651},{"name":"SKILL.md","path":"agent/src/skills/bottleneck-hunter/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/bottleneck-hunter/SKILL.md","title":"bottleneck-hunter","category":"anthropic-skill","format":"markdown","content":"---\nname: bottleneck-hunter\ncategory: strategy\ndescription: \"Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.\"\n---\n\n# Supply-Chain Bottleneck Hunter\n\nDecompose a super-trend (user-specified, e.g. \"AI infrastructure\", \"energy transition\") into its physical supply chain and hunt for bottleneck-arbitrage opportunities.\n\n## Core Idea\n\nDon't ask \"which AI stock to buy\" — ask \"if this trend keeps expanding, which link runs out first?\"\n\nTraditional research chases leaders and known tracks. This skill inverts: **start from the choke points of the physical supply chain and find companies nobody notices but that the whole industry must wait on when they run short.**\n\nThe edge: Layer-1 bottlenecks (GPU, HBM, power) are already priced in. The real alpha is in **Layer 2 and Layer 3** — optical modules, lasers, InP substrates, SOI wafers, epitaxy equipment, wafer-level test, IC substrates, specialty fiberglass.\n\n## Step 1: Super-Trend Confirmation\n\n### Trend Filter Criteria\n\n| Criterion | Requirement | How to verify |\n|-----------|-------------|---------------|\n| Durability | ≥3-5 years of certain growth | Search industry forecasts, capex plans |\n| Physicality | Needs real hardware/material/equipment build | Distinguish \"software upgrade\" from \"physical expansion\" |\n| Scale | Global capex >$50B/year | Search top players' capex guidance |\n| Acceleration | Demand growth > supply expansion | Compare demand growth vs capacity plans |\n\nUse `web_search` to verify. Reference super-trends: AI infrastructure, energy transition (nuclear/grid/storage), defense modernization, semiconductor reshoring, space economy.\n\n## Step 2: Physical Supply-Chain Decomposition\n\nDon't stop at concepts — decompose to physical entities.\n\n```\nLayer 0 (end): final product/service\nLayer 1 (core component): already closely watched → priced in, limited alpha\nLayer 2 (sub-component/material): low attention, alpha-rich\nLayer 3 (upstream equipment/raw material)\nLayer 4 (infrastructure): power, cooling, land, talent, certifications\n```\n\n### AI Infrastructure Example\n\n```\nLayer 0: AI model training/inference services\nLayer 1: GPU/accelerators, HBM, servers, data centers\nLayer 2 (focus zone):\n - Network interconnect: optical modules, fiber, switch ASICs, copper cables\n - Optical comms core: lasers (EML/VCSEL/CW), modulators, photodetectors\n - Semiconductor materials: InP substrates, GaAs substrates, SOI wafers, SiC substrates\n - Advanced packaging: CoWoS interposers, HBM TSV, ABF substrate film\n - PCB/substrate: high-frequency PCB, IC substrates, specialty fiberglass\n - Test: wafer-level test (probe cards), burn-in, ATE\n - Thermal/cooling: liquid cooling, CDU, immersion fluid\n - Power connection: busbars, UPS, distribution, transformers\nLayer 3: epitaxy equipment (MOCVD/MBE), lithography/etch, high-purity metals (In/Ga/Ge), specialty gases, sputtering targets, certifications (MSA/Telcordia)\nLayer 4: power (nuclear/gas/transmission), cooling water, data-center land/permits\n```\n\nFor other trends, use `web_search` with queries like `{trend} supply chain bottleneck`, `{trend} shortage critical component`, `{trend} capacity constraint`, `{trend} sole source supplier`.\n\n## Step 3: Bottleneck Identification — Finding \"Choke Points\"\n\nFor each Layer 2-3 link, evaluate 6 criteria:\n\n| # | Criterion | Question | Score |\n|---|-----------|----------|-------|\n| 1 | Supply concentration | ≤3 global suppliers? | 🔴 ≤2 / 🟡 3-5 / 🟢 >5 |\n| 2 | Expansion lead time | How long to add capacity? | 🔴 >2y / 🟡 1-2y / 🟢 <1y |\n| 3 | Substitutability | Can other tech/material replace it? | 🔴 irreplaceable / 🟡 partial / 🟢 easy |\n| 4 | Capacity utilization | Current utilization? | 🔴 >90% / 🟡 70-90% / 🟢 <70% |\n| 5 | Demand growth | Downstream demand growth? | 🔴 >50%/yr / 🟡 20-50% / 🟢 <20% |\n| 6 | Customer qualification cycle | How long for a new supplier to qualify? | 🔴 >1y / 🟡 6-12m / 🟢 <6m |\n\n**Bottleneck grade**: 🔴×≥4 → **S-grade** (single-point failure, highest priority); 🔴×3 → **A-grade** (severely constrained); 🔴×1-2 → **B-grade** (stressed but manageable); no 🔴 → not a bottleneck, skip.\n\n## Step 4: Company Screening — From Bottleneck to Tickers\n\nFor each S/A-grade bottleneck, use `web_search` / `screen_market` to find listed companies.\n\n### Initial Screen\n\n| Criterion | Requirement |\n|-----------|-------------|\n| Listing status | Listed (A/HK/US/JP/TW/EU) |\n| Bottleneck revenue share | >30% of revenue from the bottleneck link |\n| Market cap | Prefer <$10B (large caps already priced) |\n| Liquidity | Average daily turnover >$1M |\n\n### Valuation Gate (mandatory, never skip)\n\n**A real bottleneck ≠ an investment opportunity.** For every company, compute PE/PB/ROE/FCF yield with `financial_rigor` (`command=verify_valuation`), and run `financial_rigor` (`command=three_scenario`) for scenario valuation:\n\n- **Red light** (any one → signal strength capped at ★★, flag \"valuation stretched\"): market cap >20% of TAM; PS>30x with revenue growth <100%; market cap >10× 5-year optimistic revenue forecast; stock doubled within 60 days of a follow-on offering.\n- **Yellow light** (needs extra justification, else downgrade): loss-making + PS>15x; PS >5× a profitable peer; PE>80x (compute PEG).\n- **Green light** (bonus): PS<10x with revenue growing; PE<30x with a moat (flag \"margin of safety\").\n\n**Sanity check (mandatory)**: with `financial_rigor` (`command=three_scenario`), answer — \"buying at current market cap, if the most optimistic scenario fully plays out and I exit at 25× PE in 10 years, what's the annualized return?\" <10%/yr → flag \"no margin of safety at current price\".\n\n## Step 5: Cross-Validation — Don't Trust a Single Story\n\n### Positive checks\n\n| Check | Question |\n|-------|----------|\n| Customer validation | Have top customers signed/imported? (check announcements, customer filings) |\n| Revenue validation | Is the bottleneck already showing in revenue growth? (last 2-3 quarters) |\n| Price validation | Is the product raising price? (industry quotes, analyst reports) |\n| Capacity validation | Is capacity really tight? (lead times, customer complaints) |\n| Capital validation | Is there expansion capex? (company guidance) |\n\nUse `get_financial_statements` / `get_stock_news` / `web_search`.\n\n### Reverse checks (Munger inversion)\n\n- Why don't smart people buy this stock?\n- Can the bottleneck be bypassed? Alternative routes?\n- Can China / other players quickly replicate capacity?\n- If end-demand drops 50%, what happens to this company?\n- Has management diluted at highs before?\n- What growth assumption does the current valuation imply?\n\n## Step 6: Output — Bottleneck Opportunity Board\n\n### Ranking Table\n\n| Rank | Company | Ticker | Mkt Cap | Revenue | PS | PE | Bottleneck link | Grade | Share | Growth | Signal | Valuation |\n|------|---------|--------|---------|---------|-----|-----|-----------------|-------|-------|--------|--------|-----------|\n\n**Market cap, revenue, PS, PE are mandatory** — never skip with \"TBD\". If financials can't be obtained, signal strength ≤★★.\n\nSignal strength (valuation gate directly affects):\n- ★★★★★ multi-cross-validated + customers imported + revenue confirmed + valuation green\n- ★★★★ most checks pass + valuation green/yellow (with explanation)\n- ★★★ logic holds but parts unverified + valuation yellow acceptable\n- ★★ early signal, or logic holds but valuation red\n- ★ pure concept, unverified\n\nAfter drafting, run `report_audit` (`command=extract` → verify each point → `command=verdict`) as a quality gate to ensure no hallucinated numbers.\n\n### One-Pager Template\n\n```\n🎯 {Company} ({Ticker}) — {one-line bottleneck positioning}\n\nWhy it's a bottleneck: (2-3 sentences)\nWhy this company: (2-3 sentences)\n\nCatalyst timeline:\n- Near-term (1-3m): [earnings / capacity / customer win]\n- Mid-term (3-12m): [industry trend / expansion node]\n\nKey risks: 1. 2.\n\nKey data: market cap / revenue / PS / PE / growth / bottleneck revenue share\nMargin of safety: 10y 25× PE exit method, annualized return XX%. Conclusion: yes/no.\n\nCross-validation status: ✅ customer / ✅ revenue / ⚠️ valuation stretched / ❌ unverified\n\nConclusion: deep research / watchlist / skip\n```\n\nSave with `write_file` to the reports directory (e.g. `reports/bottleneck-map/{trend}-bottleneck-{YYYYMMDD}.md`).\n\n## Step 7: Inventory Update — Maintain the Bottleneck Map\n\nOn each run: ① re-check identified bottlenecks (new suppliers? capacity expanded? substitute breakthrough?); ② scan new bottlenecks (`web_search` last 7 days supply chain / shortage / bottleneck news); ③ update grades (upgrade/downgrade/relieve).\n\n## AI Research Bias Self-Check\n\n| Bias | Symptom | Counter |\n|------|---------|---------|\n| Leader-bias | Search dominated by large caps | Deliberately search small-cap suppliers, add \"small cap\" |\n| English-bias | Miss JP/KR/TW players | Must search JP/KR/TW market suppliers |\n| Narrative-bias | Drawn to \"AI concept\" labels | Look only at actual supply-chain position, not market labels |\n| Confirmation-bias | After finding a bottleneck, only seek positive evidence | Force Step 5 reverse checks |\n| Recency-bias | Rely on stale info | Prefer last 30 days of data |\n\n## Core Principles (highest priority)\n\n1. **Don't ask the AI to recommend stocks — ask it to decompose supply chains.** The question matters more than the answer.\n2. **Physical first** — only links that need real physical product/material/equipment.\n3. **Layer 2 and Layer 3** — don't chase already-priced leaders.\n4. **Cross-validate** — every conclusion needs ≥2 independent sources.\n5. **Be honest about uncertainty** — if data is missing, say so; don't fill with speculation.\n6. **Bottlenecks are temporary** — every bottleneck gets resolved; the key is timing the window.\n7. **Small cap ≠ good opportunity** — a small cap can also be a bad company; it must pass financial quality.\n8. **A real bottleneck ≠ an investment** — at PS>30x or still loss-making, the current price is not a buy. **Valuation is a hard gate that cannot be overridden by bottleneck purity, signal strength, or narrative appeal.** Better to miss a bottleneck stock that ran than buy a loss-making company at 100× sales.\n","frontmatter":{"name":"bottleneck-hunter","category":"strategy","description":"Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders."},"isInternal":false,"tokens":2698,"sizeBytes":10961},{"name":"SKILL.md","path":"agent/src/skills/candlestick/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/candlestick/SKILL.md","title":"candlestick","category":"anthropic-skill","format":"markdown","content":"---\nname: candlestick\ndescription: Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores.\ncategory: strategy\n---\n# Candlestick Pattern Recognition\n\n## Purpose\n\nIdentifies 15 classic candlestick patterns and generates trading signals:\n\n### Single-Candle Patterns (5)\n| Pattern | Signal | Description |\n|------|------|------|\n| Hammer | Bullish | Long lower shadow with a small body at the top |\n| Inverted Hammer | Bullish | Long upper shadow with a small body at the bottom |\n| Shooting Star | Bearish | Long upper shadow with a small body at the bottom (appears after an uptrend) |\n| Doji | Neutral | Open and close are nearly equal |\n| Spinning Top | Neutral | Small body with roughly equal upper and lower shadows |\n\n### Double-Candle Patterns (5)\n| Pattern | Signal |\n|------|------|\n| Bullish Engulfing | Bullish |\n| Bearish Engulfing | Bearish |\n| Bullish Harami | Bullish |\n| Bearish Harami | Bearish |\n| Piercing Line | Bullish |\n| Dark Cloud Cover | Bearish |\n\n### Triple-Candle Patterns (4)\n| Pattern | Signal |\n|------|------|\n| Morning Star | Bullish |\n| Evening Star | Bearish |\n| Three White Soldiers | Bullish |\n| Three Black Crows | Bearish |\n\n## Signal Logic\n\nBullish patterns score +1, bearish patterns score -1. Go long when the total score is > 0, go short when it is < 0, and stand aside when it equals 0.\n\n## Parameters\n\n| Parameter | Default | Description |\n|------|--------|------|\n| body_pct | 0.1 | Threshold for body-to-range ratio in a doji |\n| shadow_ratio | 2.0 | Ratio of shadow length to body length |\n\n## Dependencies\n\n```bash\npip install pandas numpy requests\n```\n\n## Signal Convention\n\n- `1` = long, `-1` = short, `0` = stand aside\n","frontmatter":{"name":"candlestick","description":"Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores.","category":"strategy"},"isInternal":false,"tokens":463,"sizeBytes":1870},{"name":"SKILL.md","path":"agent/src/skills/ccxt/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ccxt/SKILL.md","title":"ccxt","category":"anthropic-skill","format":"markdown","content":"---\nname: ccxt\ncategory: data-source\ndescription: CCXT unified crypto exchange library (100+ exchanges). Free public market data. Fallback when OKX is unavailable.\n---\n\n## Overview\n\nCCXT is a unified cryptocurrency exchange trading library supporting 100+ exchanges including Binance, Bybit, OKX, Coinbase, Kraken, and more. Public market data (OHLCV, tickers, order books) requires no API key.\n\n- GitHub: https://github.com/ccxt/ccxt (35k+ stars)\n- Install: `pip install ccxt`\n\n## Quick Start\n\n```python\nimport ccxt\n\nexchange = ccxt.binance({\"enableRateLimit\": True})\n\n# Fetch daily OHLCV\nohlcv = exchange.fetch_ohlcv(\"BTC/USDT\", \"1d\", limit=100)\n# Returns: [[timestamp, open, high, low, close, volume], ...]\n\n# Fetch ticker\nticker = exchange.fetch_ticker(\"ETH/USDT\")\nprint(f\"ETH price: {ticker['last']}\")\n```\n\n## Key Methods\n\n| Method | Description | Returns |\n|--------|-------------|---------|\n| `fetch_ohlcv(symbol, timeframe, since, limit)` | Historical candles | `[[ts, o, h, l, c, v], ...]` |\n| `fetch_ticker(symbol)` | Latest quote | `{last, bid, ask, volume, ...}` |\n| `fetch_tickers(symbols)` | Batch quotes | `{symbol: ticker}` |\n| `fetch_order_book(symbol, limit)` | Order book | `{bids, asks, timestamp}` |\n| `fetch_trades(symbol, since, limit)` | Recent trades | `[{price, amount, side, timestamp}, ...]` |\n\n## Timeframes\n\n`1m`, `3m`, `5m`, `15m`, `30m`, `1h`, `2h`, `4h`, `6h`, `12h`, `1d`, `1w`, `1M`\n\nNote: not all exchanges support all timeframes. Use `exchange.timeframes` to check.\n\n## Symbol Format\n\nCCXT uses slash format: `BTC/USDT`, `ETH/BTC`, `SOL/USDT`\n\nThe project's DataLoader automatically converts `BTC-USDT` (hyphen) to `BTC/USDT` (slash).\n\n## Exchange Selection\n\nSet via environment variable: `CCXT_EXCHANGE=binance` (default)\n\nPopular exchanges: `binance`, `bybit`, `okx`, `coinbase`, `kraken`, `bitget`, `gate`\n\n## Built-in Loader\n\nThe project has a built-in CCXT DataLoader at `backtest/loaders/ccxt_loader.py`. It serves as a fallback when the OKX loader is unavailable.\n\n## Pagination\n\nFor long history, CCXT paginates via the `since` parameter (millisecond timestamp). The built-in loader handles this automatically (up to 200 pages).\n","frontmatter":{"name":"ccxt","category":"data-source","description":"CCXT unified crypto exchange library (100+ exchanges). Free public market data. Fallback when OKX is unavailable."},"isInternal":false,"tokens":619,"sizeBytes":2174},{"name":"一买一卖.md","path":"agent/src/skills/chanlun/references/买卖点/一买一卖.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/一买一卖.md","title":"买卖点 Skill","category":"anthropic-skill","format":"markdown","content":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/一买一卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/一买一卖.md)","tokens":300,"sizeBytes":205},{"name":"三买三卖.md","path":"agent/src/skills/chanlun/references/买卖点/三买三卖.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/三买三卖.md","title":"买卖点 Skill","category":"anthropic-skill","format":"markdown","content":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/三买三卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/三买三卖.md)","tokens":300,"sizeBytes":205},{"name":"二买二卖.md","path":"agent/src/skills/chanlun/references/买卖点/二买二卖.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/二买二卖.md","title":"买卖点 Skill","category":"anthropic-skill","format":"markdown","content":"# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/二买二卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/二买二卖.md)","tokens":300,"sizeBytes":205},{"name":"中枢.md","path":"agent/src/skills/chanlun/references/核心概念/中枢.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/中枢.md","title":"核心概念 Skill","category":"anthropic-skill","format":"markdown","content":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/中枢.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/中枢.md)","tokens":300,"sizeBytes":204},{"name":"分型.md","path":"agent/src/skills/chanlun/references/核心概念/分型.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/分型.md","title":"核心概念 Skill","category":"anthropic-skill","format":"markdown","content":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/分型.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/分型.md)","tokens":300,"sizeBytes":204},{"name":"笔.md","path":"agent/src/skills/chanlun/references/核心概念/笔.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/笔.md","title":"核心概念 Skill","category":"anthropic-skill","format":"markdown","content":"# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/笔.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/笔.md)","tokens":300,"sizeBytes":202},{"name":"SKILL.md","path":"agent/src/skills/chanlun/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/SKILL.md","title":"chanlun","category":"anthropic-skill","format":"markdown","content":"---\nname: chanlun\ndescription: 基于缠论（缠中说禅）的形态识别引擎，使用czsc库自动检测K线分型、笔、中枢，并生成一买/一卖/二买/二卖/三买/三卖等买卖点信号。支持多周期分析和形态分类（3/5/7/9/11笔形态）。\ncategory: strategy\n---\n# 缠论形态识别\n\n## 用途\n\n基于**缠中说禅**理论的价格形态识别。缠论是一套完全基于价格结构的技术分析方法，核心链路：\n\n```\n原始K线 → 去包含处理 → 分型识别 → 笔检测 → 中枢构建 → 买卖点判定\n```\n\n适用于任何有 OHLCV 数据的市场（A股、加密货币、期货等）。\n\n## 核心概念\n\n| 概念 | 说明 | 详细文档 |\n| --- | --- | --- |\n| 分型（FX） | 顶分型：中间K线最高；底分型：中间K线最低 | [分型](references/核心概念/分型.md) |\n| 笔（BI） | 相邻顶底分型之间的一段走势，最小单元 | [笔](references/核心概念/笔.md) |\n| 中枢（ZS） | 至少3笔构成的价格重叠区域，趋势的核心 | [中枢](references/核心概念/中枢.md) |\n\n## 买卖点体系\n\n| 买卖点 | 含义 | 详细文档 |\n| --- | --- | --- |\n| 一买/一卖 | 趋势结束后的第一个反转信号（背驰点） | [一买一卖](references/买卖点/一买一卖.md) |\n| 二买/二卖 | 一买/一卖后回调不破底/顶的确认信号 | [二买二卖](references/买卖点/二买二卖.md) |\n| 三买/三卖 | 中枢上移/下移后回调不进入前中枢的信号 | [三买三卖](references/买卖点/三买三卖.md) |\n\n## 依赖安装\n\n```bash\npip install czsc requests pandas\n```\n\n## 快速上手\n\n```python\nfrom czsc import CZSC, RawBar, Freq\nfrom datetime import datetime\n\n# 准备 RawBar 列表（需按时间正序排列）\nbars = [\n RawBar(symbol=\"BTC-USDT\", id=0, dt=datetime(2026,1,1),\n freq=Freq.D, open=70000, close=71000,\n high=72000, low=69000, vol=1000, amount=71000000),\n # ... 更多K线\n]\n\n# 创建分析器，自动检测分型/笔/中枢\nc = CZSC(bars)\n\n# 访问结果\nprint(c.bi_list) # 已完成的笔\nprint(c.bars_ubi) # 未完成笔中的K线\n```\n\n## 可用信号函数（czsc.signals.cxt）\n\nczsc 内置 43 个缠论信号函数，核心如下：\n\n| 函数 | 说明 | 类型 |\n| --- | --- | --- |\n| `cxt_first_buy_V221126` | 一买信号 | 买卖点 |\n| `cxt_first_sell_V221126` | 一卖信号 | 买卖点 |\n| `cxt_second_bs_V230320` | 均线辅助二买二卖 | 买卖点 |\n| `cxt_third_bs_V230318` | 均线辅助三买三卖 | 买卖点 |\n| `cxt_third_buy_V230228` | 笔三买辅助 | 买卖点 |\n| `cxt_double_zs_V230311` | 两中枢组合判断BS1 | 中枢 |\n| `cxt_three_bi_V230618` | 三笔形态分类 | 形态 |\n| `cxt_five_bi_V230619` | 五笔形态分类 | 形态 |\n| `cxt_seven_bi_V230620` | 七笔形态分类 | 形态 |\n| `cxt_nine_bi_V230621` | 九笔形态分类 | 形态 |\n| `cxt_eleven_bi_V230622` | 十一笔形态分类 | 形态 |\n| `cxt_bi_base_V230228` | BI基础信号（方向/转折） | 基础 |\n| `cxt_bi_end_V230312` | MACD辅助笔结束 | 辅助 |\n| `cxt_range_oscillation_V230620` | 区间震荡判断 | 辅助 |\n| `cxt_zhong_shu_gong_zhen_V221221` | 大小级别中枢共振 | 中枢 |\n\n## 信号约定\n\n- 信号引擎输出：`1`=做多，`-1`=做空，`0`=观望\n- 做多条件：一买信号 或 三笔向上盘背 或 五笔类一买\n- 做空条件：一卖信号 或 三笔向下盘背 或 五笔类一卖\n- 中枢辅助：价格在中枢下沿附近做多优势，上沿附近做空优势\n\n## 数据格式\n\nczsc 接受 `List[RawBar]`，每个 RawBar 包含：\n\n| 字段 | 类型 | 说明 |\n| --- | --- | --- |\n| symbol | str | 标的代码 |\n| id | int | 序号（从0开始） |\n| dt | datetime | 时间 |\n| freq | Freq | 频率：`Freq.F1/F5/F15/F30/F60/D/W/M` |\n| open | float | 开盘价 |\n| close | float | 收盘价 |\n| high | float | 最高价 |\n| low | float | 最低价 |\n| vol | float | 成交量 |\n| amount | float | 成交额 |\n\n## 实现方式\n\n使用 [czsc](https://github.com/waditu/czsc) 库（v0.9.68+），基于纯 Python 实现（可选 Rust 加速后端）。支持增量更新，适合实时分析。\n","frontmatter":{"name":"chanlun","description":"基于缠论（缠中说禅）的形态识别引擎，使用czsc库自动检测K线分型、笔、中枢，并生成一买/一卖/二买/二卖/三买/三卖等买卖点信号。支持多周期分析和形态分类（3/5/7/9/11笔形态）。","category":"strategy"},"isInternal":false,"tokens":1704,"sizeBytes":4197},{"name":"SKILL.md","path":"agent/src/skills/commodity-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/commodity-analysis/SKILL.md","title":"commodity-analysis","category":"anthropic-skill","format":"markdown","content":"# commodity-analysis\n\nPath: `agent/src/skills/commodity-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/commodity-analysis/SKILL.md)","tokens":300,"sizeBytes":208},{"name":"SKILL.md","path":"agent/src/skills/convertible-bond/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/convertible-bond/SKILL.md","title":"convertible-bond","category":"anthropic-skill","format":"markdown","content":"# convertible-bond\n\nPath: `agent/src/skills/convertible-bond/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/convertible-bond/SKILL.md)","tokens":300,"sizeBytes":202},{"name":"SKILL.md","path":"agent/src/skills/corporate-events/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/corporate-events/SKILL.md","title":"corporate-events","category":"anthropic-skill","format":"markdown","content":"# corporate-events\n\nPath: `agent/src/skills/corporate-events/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/corporate-events/SKILL.md)","tokens":300,"sizeBytes":202},{"name":"SKILL.md","path":"agent/src/skills/correlation-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-analysis/SKILL.md","title":"correlation-analysis","category":"anthropic-skill","format":"markdown","content":"# correlation-analysis\n\nPath: `agent/src/skills/correlation-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-analysis/SKILL.md)","tokens":300,"sizeBytes":214},{"name":"SKILL.md","path":"agent/src/skills/correlation-regime/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-regime/SKILL.md","title":"correlation-regime","category":"anthropic-skill","format":"markdown","content":"# correlation-regime\n\nPath: `agent/src/skills/correlation-regime/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-regime/SKILL.md)","tokens":300,"sizeBytes":208},{"name":"SKILL.md","path":"agent/src/skills/credit-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/credit-analysis/SKILL.md","title":"credit-analysis","category":"anthropic-skill","format":"markdown","content":"# credit-analysis\n\nPath: `agent/src/skills/credit-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/credit-analysis/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"SKILL.md","path":"agent/src/skills/cross-market-strategy/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/cross-market-strategy/SKILL.md","title":"cross-market-strategy","category":"anthropic-skill","format":"markdown","content":"# cross-market-strategy\n\nPath: `agent/src/skills/cross-market-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/cross-market-strategy/SKILL.md)","tokens":300,"sizeBytes":217},{"name":"SKILL.md","path":"agent/src/skills/crypto-derivatives/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/crypto-derivatives/SKILL.md","title":"crypto-derivatives","category":"anthropic-skill","format":"markdown","content":"# crypto-derivatives\n\nPath: `agent/src/skills/crypto-derivatives/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/crypto-derivatives/SKILL.md)","tokens":300,"sizeBytes":208},{"name":"SKILL.md","path":"agent/src/skills/data-routing/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/data-routing/SKILL.md","title":"data-routing","category":"anthropic-skill","format":"markdown","content":"# data-routing\n\nPath: `agent/src/skills/data-routing/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/data-routing/SKILL.md)","tokens":300,"sizeBytes":190},{"name":"SKILL.md","path":"agent/src/skills/deep-company-series/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/deep-company-series/SKILL.md","title":"deep-company-series","category":"anthropic-skill","format":"markdown","content":"# deep-company-series\n\nPath: `agent/src/skills/deep-company-series/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/deep-company-series/SKILL.md)","tokens":300,"sizeBytes":211},{"name":"SKILL.md","path":"agent/src/skills/defi-yield/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/defi-yield/SKILL.md","title":"defi-yield","category":"anthropic-skill","format":"markdown","content":"# defi-yield\n\nPath: `agent/src/skills/defi-yield/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/defi-yield/SKILL.md)","tokens":300,"sizeBytes":184},{"name":"SKILL.md","path":"agent/src/skills/dividend-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/dividend-analysis/SKILL.md","title":"dividend-analysis","category":"anthropic-skill","format":"markdown","content":"# dividend-analysis\n\nPath: `agent/src/skills/dividend-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/dividend-analysis/SKILL.md)","tokens":300,"sizeBytes":205},{"name":"SKILL.md","path":"agent/src/skills/doc-reader/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/doc-reader/SKILL.md","title":"doc-reader","category":"anthropic-skill","format":"markdown","content":"# doc-reader\n\nPath: `agent/src/skills/doc-reader/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/doc-reader/SKILL.md)","tokens":300,"sizeBytes":184},{"name":"SKILL.md","path":"agent/src/skills/earnings-forecast/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/earnings-forecast/SKILL.md","title":"earnings-forecast","category":"anthropic-skill","format":"markdown","content":"# earnings-forecast\n\nPath: `agent/src/skills/earnings-forecast/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/earnings-forecast/SKILL.md)","tokens":300,"sizeBytes":205},{"name":"SKILL.md","path":"agent/src/skills/earnings-revision/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/earnings-revision/SKILL.md","title":"earnings-revision","category":"anthropic-skill","format":"markdown","content":"# earnings-revision\n\nPath: `agent/src/skills/earnings-revision/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/earnings-revision/SKILL.md)","tokens":300,"sizeBytes":205},{"name":"大宗交易.md","path":"agent/src/skills/eastmoney/references/参考数据/大宗交易.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/大宗交易.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/大宗交易.md)","tokens":300,"sizeBytes":212},{"name":"股东户数.md","path":"agent/src/skills/eastmoney/references/参考数据/股东户数.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/股东户数.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/股东户数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/股东户数.md)","tokens":300,"sizeBytes":212},{"name":"融资融券.md","path":"agent/src/skills/eastmoney/references/参考数据/融资融券.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/融资融券.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/融资融券.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/融资融券.md)","tokens":300,"sizeBytes":212},{"name":"限售解禁.md","path":"agent/src/skills/eastmoney/references/参考数据/限售解禁.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/限售解禁.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/eastmoney/references/参考数据/限售解禁.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/参考数据/限售解禁.md)","tokens":300,"sizeBytes":212},{"name":"行业概念板块.md","path":"agent/src/skills/eastmoney/references/板块/行业概念板块.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/板块/行业概念板块.md","title":"板块 Skill","category":"anthropic-skill","format":"markdown","content":"# 板块 Skill\n\nPath: `agent/src/skills/eastmoney/references/板块/行业概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/板块/行业概念板块.md)","tokens":300,"sizeBytes":210},{"name":"券商研报.md","path":"agent/src/skills/eastmoney/references/研报舆情/券商研报.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/研报舆情/券商研报.md","title":"研报舆情 Skill","category":"anthropic-skill","format":"markdown","content":"# 研报舆情 Skill\n\nPath: `agent/src/skills/eastmoney/references/研报舆情/券商研报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/研报舆情/券商研报.md)","tokens":300,"sizeBytes":212},{"name":"财经新闻.md","path":"agent/src/skills/eastmoney/references/研报舆情/财经新闻.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/研报舆情/财经新闻.md","title":"研报舆情 Skill","category":"anthropic-skill","format":"markdown","content":"# 研报舆情 Skill\n\nPath: `agent/src/skills/eastmoney/references/研报舆情/财经新闻.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/研报舆情/财经新闻.md)","tokens":300,"sizeBytes":212},{"name":"三大报表与主要指标.md","path":"agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md","title":"财务报表 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务报表 Skill\n\nPath: `agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/财务报表/三大报表与主要指标.md)","tokens":300,"sizeBytes":222},{"name":"资金流向.md","path":"agent/src/skills/eastmoney/references/资金面/资金流向.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/资金面/资金流向.md","title":"资金面 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金面 Skill\n\nPath: `agent/src/skills/eastmoney/references/资金面/资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/资金面/资金流向.md)","tokens":300,"sizeBytes":209},{"name":"代码搜索.md","path":"agent/src/skills/eastmoney/references/选股检索/代码搜索.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/选股检索/代码搜索.md","title":"选股检索 Skill","category":"anthropic-skill","format":"markdown","content":"# 选股检索 Skill\n\nPath: `agent/src/skills/eastmoney/references/选股检索/代码搜索.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/选股检索/代码搜索.md)","tokens":300,"sizeBytes":212},{"name":"全市场选股.md","path":"agent/src/skills/eastmoney/references/选股检索/全市场选股.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/选股检索/全市场选股.md","title":"选股检索 Skill","category":"anthropic-skill","format":"markdown","content":"# 选股检索 Skill\n\nPath: `agent/src/skills/eastmoney/references/选股检索/全市场选股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/选股检索/全市场选股.md)","tokens":300,"sizeBytes":214},{"name":"龙虎榜.md","path":"agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md","title":"龙虎榜 Skill","category":"anthropic-skill","format":"markdown","content":"# 龙虎榜 Skill\n\nPath: `agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/references/龙虎榜/龙虎榜.md)","tokens":300,"sizeBytes":207},{"name":"SKILL.md","path":"agent/src/skills/eastmoney/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/SKILL.md","title":"eastmoney","category":"anthropic-skill","format":"markdown","content":"# eastmoney\n\nPath: `agent/src/skills/eastmoney/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/eastmoney/SKILL.md)","tokens":300,"sizeBytes":181},{"name":"SKILL.md","path":"agent/src/skills/edgar-sec-filings/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/edgar-sec-filings/SKILL.md","title":"edgar-sec-filings","category":"anthropic-skill","format":"markdown","content":"# edgar-sec-filings\n\nPath: `agent/src/skills/edgar-sec-filings/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/edgar-sec-filings/SKILL.md)","tokens":300,"sizeBytes":205},{"name":"Fibonacci浪间关系.md","path":"agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/references/Fibonacci浪间关系.md)","tokens":300,"sizeBytes":232},{"name":"波浪结构.md","path":"agent/src/skills/elliott-wave/references/波浪结构.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/references/波浪结构.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/elliott-wave/references/波浪结构.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/references/波浪结构.md)","tokens":300,"sizeBytes":214},{"name":"SKILL.md","path":"agent/src/skills/elliott-wave/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/SKILL.md","title":"elliott-wave","category":"anthropic-skill","format":"markdown","content":"# elliott-wave\n\nPath: `agent/src/skills/elliott-wave/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/elliott-wave/SKILL.md)","tokens":300,"sizeBytes":190},{"name":"SKILL.md","path":"agent/src/skills/etf-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/etf-analysis/SKILL.md","title":"etf-analysis","category":"anthropic-skill","format":"markdown","content":"# etf-analysis\n\nPath: `agent/src/skills/etf-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/etf-analysis/SKILL.md)","tokens":300,"sizeBytes":190},{"name":"SKILL.md","path":"agent/src/skills/event-driven/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/event-driven/SKILL.md","title":"event-driven","category":"anthropic-skill","format":"markdown","content":"# event-driven\n\nPath: `agent/src/skills/event-driven/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/event-driven/SKILL.md)","tokens":300,"sizeBytes":190},{"name":"SKILL.md","path":"agent/src/skills/execution-model/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/execution-model/SKILL.md","title":"execution-model","category":"anthropic-skill","format":"markdown","content":"# execution-model\n\nPath: `agent/src/skills/execution-model/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/execution-model/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"combination.md","path":"agent/src/skills/factor-research/references/combination.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/references/combination.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/factor-research/references/combination.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/references/combination.md)","tokens":300,"sizeBytes":234},{"name":"neutralization.md","path":"agent/src/skills/factor-research/references/neutralization.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/references/neutralization.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/factor-research/references/neutralization.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/references/neutralization.md)","tokens":300,"sizeBytes":240},{"name":"SKILL.md","path":"agent/src/skills/factor-research/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/SKILL.md","title":"factor-research","category":"anthropic-skill","format":"markdown","content":"# factor-research\n\nPath: `agent/src/skills/factor-research/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/factor-research/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"SKILL.md","path":"agent/src/skills/financial-statement/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/financial-statement/SKILL.md","title":"financial-statement","category":"anthropic-skill","format":"markdown","content":"# financial-statement\n\nPath: `agent/src/skills/financial-statement/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/financial-statement/SKILL.md)","tokens":300,"sizeBytes":211},{"name":"SKILL.md","path":"agent/src/skills/fund-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/fund-analysis/SKILL.md","title":"fund-analysis","category":"anthropic-skill","format":"markdown","content":"# fund-analysis\n\nPath: `agent/src/skills/fund-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/fund-analysis/SKILL.md)","tokens":300,"sizeBytes":193},{"name":"SKILL.md","path":"agent/src/skills/fundamental-filter/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/fundamental-filter/SKILL.md","title":"fundamental-filter","category":"anthropic-skill","format":"markdown","content":"# fundamental-filter\n\nPath: `agent/src/skills/fundamental-filter/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/fundamental-filter/SKILL.md)","tokens":300,"sizeBytes":208},{"name":"SKILL.md","path":"agent/src/skills/geopolitical-risk/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/geopolitical-risk/SKILL.md","title":"geopolitical-risk","category":"anthropic-skill","format":"markdown","content":"# geopolitical-risk\n\nPath: `agent/src/skills/geopolitical-risk/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/geopolitical-risk/SKILL.md)","tokens":300,"sizeBytes":205},{"name":"SKILL.md","path":"agent/src/skills/global-macro/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/global-macro/SKILL.md","title":"global-macro","category":"anthropic-skill","format":"markdown","content":"# global-macro\n\nPath: `agent/src/skills/global-macro/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/global-macro/SKILL.md)","tokens":300,"sizeBytes":190},{"name":"PRZ交易.md","path":"agent/src/skills/harmonic/references/PRZ交易.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/references/PRZ交易.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/harmonic/references/PRZ交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/references/PRZ交易.md)","tokens":300,"sizeBytes":208},{"name":"XABCD形态.md","path":"agent/src/skills/harmonic/references/XABCD形态.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/references/XABCD形态.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/harmonic/references/XABCD形态.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/references/XABCD形态.md)","tokens":300,"sizeBytes":212},{"name":"SKILL.md","path":"agent/src/skills/harmonic/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/SKILL.md","title":"harmonic","category":"anthropic-skill","format":"markdown","content":"# harmonic\n\nPath: `agent/src/skills/harmonic/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/harmonic/SKILL.md)","tokens":300,"sizeBytes":178},{"name":"SKILL.md","path":"agent/src/skills/hedging-strategy/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/hedging-strategy/SKILL.md","title":"hedging-strategy","category":"anthropic-skill","format":"markdown","content":"# hedging-strategy\n\nPath: `agent/src/skills/hedging-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/hedging-strategy/SKILL.md)","tokens":300,"sizeBytes":202},{"name":"SKILL.md","path":"agent/src/skills/hk-connect-flow/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/hk-connect-flow/SKILL.md","title":"hk-connect-flow","category":"anthropic-skill","format":"markdown","content":"# hk-connect-flow\n\nPath: `agent/src/skills/hk-connect-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/hk-connect-flow/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"五线计算.md","path":"agent/src/skills/ichimoku/references/五线计算.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/references/五线计算.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/ichimoku/references/五线计算.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/references/五线计算.md)","tokens":300,"sizeBytes":206},{"name":"信号系统.md","path":"agent/src/skills/ichimoku/references/信号系统.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/references/信号系统.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/ichimoku/references/信号系统.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/references/信号系统.md)","tokens":300,"sizeBytes":206},{"name":"SKILL.md","path":"agent/src/skills/ichimoku/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/SKILL.md","title":"ichimoku","category":"anthropic-skill","format":"markdown","content":"# ichimoku\n\nPath: `agent/src/skills/ichimoku/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ichimoku/SKILL.md)","tokens":300,"sizeBytes":178},{"name":"bogle-cost-drag.md","path":"agent/src/skills/investor-lenses/references/bogle-cost-drag.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/bogle-cost-drag.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/bogle-cost-drag.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/bogle-cost-drag.md)","tokens":300,"sizeBytes":242},{"name":"buffett-quality-franchise.md","path":"agent/src/skills/investor-lenses/references/buffett-quality-franchise.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/buffett-quality-franchise.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/buffett-quality-franchise.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/buffett-quality-franchise.md)","tokens":300,"sizeBytes":262},{"name":"chanos-forensic-short.md","path":"agent/src/skills/investor-lenses/references/chanos-forensic-short.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/chanos-forensic-short.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/chanos-forensic-short.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/chanos-forensic-short.md)","tokens":300,"sizeBytes":254},{"name":"dalio-debt-cycle.md","path":"agent/src/skills/investor-lenses/references/dalio-debt-cycle.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/dalio-debt-cycle.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/dalio-debt-cycle.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/dalio-debt-cycle.md)","tokens":300,"sizeBytes":244},{"name":"duan-right-business.md","path":"agent/src/skills/investor-lenses/references/duan-right-business.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/duan-right-business.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/duan-right-business.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/duan-right-business.md)","tokens":300,"sizeBytes":250},{"name":"fengliu-weak-system.md","path":"agent/src/skills/investor-lenses/references/fengliu-weak-system.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/fengliu-weak-system.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/fengliu-weak-system.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/fengliu-weak-system.md)","tokens":300,"sizeBytes":250},{"name":"fisher-scuttlebutt.md","path":"agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/fisher-scuttlebutt.md)","tokens":300,"sizeBytes":248},{"name":"graham-deep-value.md","path":"agent/src/skills/investor-lenses/references/graham-deep-value.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/graham-deep-value.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/graham-deep-value.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/graham-deep-value.md)","tokens":300,"sizeBytes":246},{"name":"lynch-garp.md","path":"agent/src/skills/investor-lenses/references/lynch-garp.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/lynch-garp.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/lynch-garp.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/lynch-garp.md)","tokens":300,"sizeBytes":232},{"name":"marks-cycle-position.md","path":"agent/src/skills/investor-lenses/references/marks-cycle-position.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/marks-cycle-position.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/marks-cycle-position.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/marks-cycle-position.md)","tokens":300,"sizeBytes":252},{"name":"munger-inversion.md","path":"agent/src/skills/investor-lenses/references/munger-inversion.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/munger-inversion.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/munger-inversion.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/munger-inversion.md)","tokens":300,"sizeBytes":244},{"name":"qiuguolu-three-questions.md","path":"agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/references/qiuguolu-three-questions.md)","tokens":300,"sizeBytes":260},{"name":"SKILL.md","path":"agent/src/skills/investor-lenses/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/SKILL.md","title":"investor-lenses","category":"anthropic-skill","format":"markdown","content":"# investor-lenses\n\nPath: `agent/src/skills/investor-lenses/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/investor-lenses/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"SKILL.md","path":"agent/src/skills/liquidation-heatmap/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/liquidation-heatmap/SKILL.md","title":"liquidation-heatmap","category":"anthropic-skill","format":"markdown","content":"# liquidation-heatmap\n\nPath: `agent/src/skills/liquidation-heatmap/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/liquidation-heatmap/SKILL.md)","tokens":300,"sizeBytes":211},{"name":"SKILL.md","path":"agent/src/skills/macro-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/macro-analysis/SKILL.md","title":"macro-analysis","category":"anthropic-skill","format":"markdown","content":"# macro-analysis\n\nPath: `agent/src/skills/macro-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/macro-analysis/SKILL.md)","tokens":300,"sizeBytes":196},{"name":"SKILL.md","path":"agent/src/skills/management-deep-dive/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/management-deep-dive/SKILL.md","title":"management-deep-dive","category":"anthropic-skill","format":"markdown","content":"# management-deep-dive\n\nPath: `agent/src/skills/management-deep-dive/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/management-deep-dive/SKILL.md)","tokens":300,"sizeBytes":214},{"name":"SKILL.md","path":"agent/src/skills/market-microstructure/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/market-microstructure/SKILL.md","title":"market-microstructure","category":"anthropic-skill","format":"markdown","content":"# market-microstructure\n\nPath: `agent/src/skills/market-microstructure/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/market-microstructure/SKILL.md)","tokens":300,"sizeBytes":217},{"name":"SKILL.md","path":"agent/src/skills/minute-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/minute-analysis/SKILL.md","title":"minute-analysis","category":"anthropic-skill","format":"markdown","content":"# minute-analysis\n\nPath: `agent/src/skills/minute-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/minute-analysis/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"SKILL.md","path":"agent/src/skills/ml-strategy/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ml-strategy/SKILL.md","title":"ml-strategy","category":"anthropic-skill","format":"markdown","content":"# ml-strategy\n\nPath: `agent/src/skills/ml-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/ml-strategy/SKILL.md)","tokens":300,"sizeBytes":187},{"name":"SKILL.md","path":"agent/src/skills/mootdx/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/mootdx/SKILL.md","title":"mootdx","category":"anthropic-skill","format":"markdown","content":"# mootdx\n\nPath: `agent/src/skills/mootdx/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/mootdx/SKILL.md)","tokens":300,"sizeBytes":172},{"name":"SKILL.md","path":"agent/src/skills/multi-factor/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/multi-factor/SKILL.md","title":"multi-factor","category":"anthropic-skill","format":"markdown","content":"# multi-factor\n\nPath: `agent/src/skills/multi-factor/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/multi-factor/SKILL.md)","tokens":300,"sizeBytes":190},{"name":"历史资金费率.md","path":"agent/src/skills/okx-market/references/合约行情/历史资金费率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/历史资金费率.md","title":"合约行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/历史资金费率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/历史资金费率.md)","tokens":300,"sizeBytes":218},{"name":"持仓量.md","path":"agent/src/skills/okx-market/references/合约行情/持仓量.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/持仓量.md","title":"合约行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/持仓量.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/持仓量.md)","tokens":300,"sizeBytes":212},{"name":"标记价格.md","path":"agent/src/skills/okx-market/references/合约行情/标记价格.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/标记价格.md","title":"合约行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/标记价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/标记价格.md)","tokens":300,"sizeBytes":214},{"name":"资金费率.md","path":"agent/src/skills/okx-market/references/合约行情/资金费率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/资金费率.md","title":"合约行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/资金费率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/资金费率.md)","tokens":300,"sizeBytes":214},{"name":"限价.md","path":"agent/src/skills/okx-market/references/合约行情/限价.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/限价.md","title":"合约行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 合约行情 Skill\n\nPath: `agent/src/skills/okx-market/references/合约行情/限价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/合约行情/限价.md)","tokens":300,"sizeBytes":210},{"name":"指数K线.md","path":"agent/src/skills/okx-market/references/指数行情/指数K线.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/指数行情/指数K线.md","title":"指数行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数行情 Skill\n\nPath: `agent/src/skills/okx-market/references/指数行情/指数K线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/指数行情/指数K线.md)","tokens":300,"sizeBytes":214},{"name":"指数行情.md","path":"agent/src/skills/okx-market/references/指数行情/指数行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/指数行情/指数行情.md","title":"指数行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数行情 Skill\n\nPath: `agent/src/skills/okx-market/references/指数行情/指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/指数行情/指数行情.md)","tokens":300,"sizeBytes":214},{"name":"K线数据.md","path":"agent/src/skills/okx-market/references/现货行情/K线数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/K线数据.md","title":"现货行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/K线数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/K线数据.md)","tokens":300,"sizeBytes":214},{"name":"交易产品列表.md","path":"agent/src/skills/okx-market/references/现货行情/交易产品列表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/交易产品列表.md","title":"现货行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/交易产品列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/交易产品列表.md)","tokens":300,"sizeBytes":218},{"name":"单个行情.md","path":"agent/src/skills/okx-market/references/现货行情/单个行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/单个行情.md","title":"现货行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/单个行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/单个行情.md)","tokens":300,"sizeBytes":214},{"name":"批量行情.md","path":"agent/src/skills/okx-market/references/现货行情/批量行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/批量行情.md","title":"现货行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/批量行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/批量行情.md)","tokens":300,"sizeBytes":214},{"name":"最近成交.md","path":"agent/src/skills/okx-market/references/现货行情/最近成交.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/最近成交.md","title":"现货行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/最近成交.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/最近成交.md)","tokens":300,"sizeBytes":214},{"name":"深度数据.md","path":"agent/src/skills/okx-market/references/现货行情/深度数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/深度数据.md","title":"现货行情 Skill","category":"anthropic-skill","format":"markdown","content":"# 现货行情 Skill\n\nPath: `agent/src/skills/okx-market/references/现货行情/深度数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/references/现货行情/深度数据.md)","tokens":300,"sizeBytes":214},{"name":"SKILL.md","path":"agent/src/skills/okx-market/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/SKILL.md","title":"okx-market","category":"anthropic-skill","format":"markdown","content":"# okx-market\n\nPath: `agent/src/skills/okx-market/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/okx-market/SKILL.md)","tokens":300,"sizeBytes":184},{"name":"SKILL.md","path":"agent/src/skills/onchain-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/onchain-analysis/SKILL.md","title":"onchain-analysis","category":"anthropic-skill","format":"markdown","content":"# onchain-analysis\n\nPath: `agent/src/skills/onchain-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/onchain-analysis/SKILL.md)","tokens":300,"sizeBytes":202},{"name":"SKILL.md","path":"agent/src/skills/options-advanced/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-advanced/SKILL.md","title":"options-advanced","category":"anthropic-skill","format":"markdown","content":"# options-advanced\n\nPath: `agent/src/skills/options-advanced/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-advanced/SKILL.md)","tokens":300,"sizeBytes":202},{"name":"SKILL.md","path":"agent/src/skills/options-payoff/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-payoff/SKILL.md","title":"options-payoff","category":"anthropic-skill","format":"markdown","content":"# options-payoff\n\nPath: `agent/src/skills/options-payoff/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-payoff/SKILL.md)","tokens":300,"sizeBytes":196},{"name":"SKILL.md","path":"agent/src/skills/options-strategy/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-strategy/SKILL.md","title":"options-strategy","category":"anthropic-skill","format":"markdown","content":"# options-strategy\n\nPath: `agent/src/skills/options-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/options-strategy/SKILL.md)","tokens":300,"sizeBytes":202},{"name":"SKILL.md","path":"agent/src/skills/pair-trading/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/pair-trading/SKILL.md","title":"pair-trading","category":"anthropic-skill","format":"markdown","content":"# pair-trading\n\nPath: `agent/src/skills/pair-trading/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/pair-trading/SKILL.md)","tokens":300,"sizeBytes":190},{"name":"SKILL.md","path":"agent/src/skills/performance-attribution/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/performance-attribution/SKILL.md","title":"performance-attribution","category":"anthropic-skill","format":"markdown","content":"# performance-attribution\n\nPath: `agent/src/skills/performance-attribution/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/performance-attribution/SKILL.md)","tokens":300,"sizeBytes":223},{"name":"SKILL.md","path":"agent/src/skills/perp-funding-basis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/perp-funding-basis/SKILL.md","title":"perp-funding-basis","category":"anthropic-skill","format":"markdown","content":"# perp-funding-basis\n\nPath: `agent/src/skills/perp-funding-basis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/perp-funding-basis/SKILL.md)","tokens":300,"sizeBytes":208},{"name":"SKILL.md","path":"agent/src/skills/pine-script/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/pine-script/SKILL.md","title":"pine-script","category":"anthropic-skill","format":"markdown","content":"# pine-script\n\nPath: `agent/src/skills/pine-script/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/pine-script/SKILL.md)","tokens":300,"sizeBytes":187},{"name":"SKILL.md","path":"agent/src/skills/private-company-research/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/private-company-research/SKILL.md","title":"private-company-research","category":"anthropic-skill","format":"markdown","content":"# private-company-research\n\nPath: `agent/src/skills/private-company-research/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/private-company-research/SKILL.md)","tokens":300,"sizeBytes":226},{"name":"SKILL.md","path":"agent/src/skills/quant-statistics/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/quant-statistics/SKILL.md","title":"quant-statistics","category":"anthropic-skill","format":"markdown","content":"# quant-statistics\n\nPath: `agent/src/skills/quant-statistics/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/quant-statistics/SKILL.md)","tokens":300,"sizeBytes":202},{"name":"coverage.md","path":"agent/src/skills/qveris/references/coverage.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/references/coverage.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/qveris/references/coverage.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/references/coverage.md)","tokens":300,"sizeBytes":210},{"name":"rest-api.md","path":"agent/src/skills/qveris/references/rest-api.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/references/rest-api.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/qveris/references/rest-api.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/references/rest-api.md)","tokens":300,"sizeBytes":210},{"name":"SKILL.md","path":"agent/src/skills/qveris/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/SKILL.md","title":"qveris","category":"anthropic-skill","format":"markdown","content":"# qveris\n\nPath: `agent/src/skills/qveris/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/qveris/SKILL.md)","tokens":300,"sizeBytes":172},{"name":"SKILL.md","path":"agent/src/skills/regulatory-knowledge/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/regulatory-knowledge/SKILL.md","title":"regulatory-knowledge","category":"anthropic-skill","format":"markdown","content":"# regulatory-knowledge\n\nPath: `agent/src/skills/regulatory-knowledge/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/regulatory-knowledge/SKILL.md)","tokens":300,"sizeBytes":214},{"name":"SKILL.md","path":"agent/src/skills/report-generate/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/report-generate/SKILL.md","title":"report-generate","category":"anthropic-skill","format":"markdown","content":"# report-generate\n\nPath: `agent/src/skills/report-generate/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/report-generate/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"SKILL.md","path":"agent/src/skills/research-discipline/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/research-discipline/SKILL.md","title":"research-discipline","category":"anthropic-skill","format":"markdown","content":"# research-discipline\n\nPath: `agent/src/skills/research-discipline/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/research-discipline/SKILL.md)","tokens":300,"sizeBytes":211},{"name":"SKILL.md","path":"agent/src/skills/research-goal/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/research-goal/SKILL.md","title":"research-goal","category":"anthropic-skill","format":"markdown","content":"# research-goal\n\nPath: `agent/src/skills/research-goal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/research-goal/SKILL.md)","tokens":300,"sizeBytes":193},{"name":"SKILL.md","path":"agent/src/skills/risk-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/risk-analysis/SKILL.md","title":"risk-analysis","category":"anthropic-skill","format":"markdown","content":"# risk-analysis\n\nPath: `agent/src/skills/risk-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/risk-analysis/SKILL.md)","tokens":300,"sizeBytes":193},{"name":"SKILL.md","path":"agent/src/skills/seasonal/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/seasonal/SKILL.md","title":"seasonal","category":"anthropic-skill","format":"markdown","content":"# seasonal\n\nPath: `agent/src/skills/seasonal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/seasonal/SKILL.md)","tokens":300,"sizeBytes":178},{"name":"endpoints_and_limits.md","path":"agent/src/skills/sec-edgar/references/endpoints_and_limits.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/endpoints_and_limits.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/endpoints_and_limits.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/endpoints_and_limits.md)","tokens":300,"sizeBytes":240},{"name":"forms_and_concepts.md","path":"agent/src/skills/sec-edgar/references/forms_and_concepts.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/forms_and_concepts.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/forms_and_concepts.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/forms_and_concepts.md)","tokens":300,"sizeBytes":236},{"name":"get_sec_filings_tool.md","path":"agent/src/skills/sec-edgar/references/get_sec_filings_tool.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/get_sec_filings_tool.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/get_sec_filings_tool.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/get_sec_filings_tool.md)","tokens":300,"sizeBytes":240},{"name":"sec_edgar_client.md","path":"agent/src/skills/sec-edgar/references/sec_edgar_client.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/sec_edgar_client.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/sec-edgar/references/sec_edgar_client.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/references/sec_edgar_client.md)","tokens":300,"sizeBytes":232},{"name":"SKILL.md","path":"agent/src/skills/sec-edgar/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/SKILL.md","title":"sec-edgar","category":"anthropic-skill","format":"markdown","content":"# sec-edgar\n\nPath: `agent/src/skills/sec-edgar/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sec-edgar/SKILL.md)","tokens":300,"sizeBytes":181},{"name":"SKILL.md","path":"agent/src/skills/sector-rotation/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sector-rotation/SKILL.md","title":"sector-rotation","category":"anthropic-skill","format":"markdown","content":"# sector-rotation\n\nPath: `agent/src/skills/sector-rotation/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sector-rotation/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"SKILL.md","path":"agent/src/skills/sentiment-analysis/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sentiment-analysis/SKILL.md","title":"sentiment-analysis","category":"anthropic-skill","format":"markdown","content":"# sentiment-analysis\n\nPath: `agent/src/skills/sentiment-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/sentiment-analysis/SKILL.md)","tokens":300,"sizeBytes":208},{"name":"SKILL.md","path":"agent/src/skills/shadow-account/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/shadow-account/SKILL.md","title":"shadow-account","category":"anthropic-skill","format":"markdown","content":"# shadow-account\n\nPath: `agent/src/skills/shadow-account/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/shadow-account/SKILL.md)","tokens":300,"sizeBytes":196},{"name":"结构突破.md","path":"agent/src/skills/smc/references/结构突破.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/references/结构突破.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/smc/references/结构突破.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/references/结构突破.md)","tokens":300,"sizeBytes":196},{"name":"订单块与缺口.md","path":"agent/src/skills/smc/references/订单块与缺口.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/references/订单块与缺口.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/smc/references/订单块与缺口.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/references/订单块与缺口.md)","tokens":300,"sizeBytes":200},{"name":"SKILL.md","path":"agent/src/skills/smc/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/SKILL.md","title":"smc","category":"anthropic-skill","format":"markdown","content":"# smc\n\nPath: `agent/src/skills/smc/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/smc/SKILL.md)","tokens":300,"sizeBytes":163},{"name":"SKILL.md","path":"agent/src/skills/social-media-intelligence/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/social-media-intelligence/SKILL.md","title":"social-media-intelligence","category":"anthropic-skill","format":"markdown","content":"# social-media-intelligence\n\nPath: `agent/src/skills/social-media-intelligence/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/social-media-intelligence/SKILL.md)","tokens":300,"sizeBytes":229},{"name":"SKILL.md","path":"agent/src/skills/stablecoin-flow/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/stablecoin-flow/SKILL.md","title":"stablecoin-flow","category":"anthropic-skill","format":"markdown","content":"# stablecoin-flow\n\nPath: `agent/src/skills/stablecoin-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/stablecoin-flow/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"examples.md","path":"agent/src/skills/strategy-dev-manager/examples.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/examples.md","title":"Strategy-dev-manager Skill","category":"anthropic-skill","format":"markdown","content":"# Strategy-dev-manager Skill\n\nPath: `agent/src/skills/strategy-dev-manager/examples.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/examples.md)","tokens":300,"sizeBytes":226},{"name":"decay_thresholds.md","path":"agent/src/skills/strategy-dev-manager/references/decay_thresholds.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/decay_thresholds.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/decay_thresholds.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/decay_thresholds.md)","tokens":300,"sizeBytes":254},{"name":"scheduled_decay_scan.md","path":"agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/scheduled_decay_scan.md)","tokens":300,"sizeBytes":262},{"name":"strategy_extraction_guide.md","path":"agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/strategy_extraction_guide.md)","tokens":300,"sizeBytes":272},{"name":"strategy_metrics.md","path":"agent/src/skills/strategy-dev-manager/references/strategy_metrics.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/strategy_metrics.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/strategy-dev-manager/references/strategy_metrics.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/references/strategy_metrics.md)","tokens":300,"sizeBytes":254},{"name":"SKILL.md","path":"agent/src/skills/strategy-dev-manager/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/SKILL.md","title":"strategy-dev-manager","category":"anthropic-skill","format":"markdown","content":"# strategy-dev-manager\n\nPath: `agent/src/skills/strategy-dev-manager/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/SKILL.md)","tokens":300,"sizeBytes":214},{"name":"decay_report.md","path":"agent/src/skills/strategy-dev-manager/templates/decay_report.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/templates/decay_report.md","title":"Templates Skill","category":"anthropic-skill","format":"markdown","content":"# Templates Skill\n\nPath: `agent/src/skills/strategy-dev-manager/templates/decay_report.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-dev-manager/templates/decay_report.md)","tokens":300,"sizeBytes":243},{"name":"SKILL.md","path":"agent/src/skills/strategy-discovery/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-discovery/SKILL.md","title":"strategy-discovery","category":"anthropic-skill","format":"markdown","content":"# strategy-discovery\n\nPath: `agent/src/skills/strategy-discovery/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-discovery/SKILL.md)","tokens":300,"sizeBytes":208},{"name":"examples.md","path":"agent/src/skills/strategy-generate/examples.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-generate/examples.md","title":"Strategy-generate Skill","category":"anthropic-skill","format":"markdown","content":"# Strategy-generate Skill\n\nPath: `agent/src/skills/strategy-generate/examples.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-generate/examples.md)","tokens":300,"sizeBytes":217},{"name":"SKILL.md","path":"agent/src/skills/strategy-generate/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-generate/SKILL.md","title":"strategy-generate","category":"anthropic-skill","format":"markdown","content":"# strategy-generate\n\nPath: `agent/src/skills/strategy-generate/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/strategy-generate/SKILL.md)","tokens":300,"sizeBytes":205},{"name":"SKILL.md","path":"agent/src/skills/technical-basic/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/technical-basic/SKILL.md","title":"technical-basic","category":"anthropic-skill","format":"markdown","content":"# technical-basic\n\nPath: `agent/src/skills/technical-basic/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/technical-basic/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"SKILL.md","path":"agent/src/skills/thesis-tracker/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/thesis-tracker/SKILL.md","title":"thesis-tracker","category":"anthropic-skill","format":"markdown","content":"# thesis-tracker\n\nPath: `agent/src/skills/thesis-tracker/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/thesis-tracker/SKILL.md)","tokens":300,"sizeBytes":196},{"name":"SKILL.md","path":"agent/src/skills/token-unlock-treasury/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/token-unlock-treasury/SKILL.md","title":"token-unlock-treasury","category":"anthropic-skill","format":"markdown","content":"# token-unlock-treasury\n\nPath: `agent/src/skills/token-unlock-treasury/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/token-unlock-treasury/SKILL.md)","tokens":300,"sizeBytes":217},{"name":"SKILL.md","path":"agent/src/skills/trade-journal/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/trade-journal/SKILL.md","title":"trade-journal","category":"anthropic-skill","format":"markdown","content":"# trade-journal\n\nPath: `agent/src/skills/trade-journal/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/trade-journal/SKILL.md)","tokens":300,"sizeBytes":193},{"name":"ETF份额规模.md","path":"agent/src/skills/tushare/references/ETF专题/ETF份额规模.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF份额规模.md","title":"ETF专题 Skill","category":"anthropic-skill","format":"markdown","content":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF份额规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF份额规模.md)","tokens":300,"sizeBytes":217},{"name":"ETF历史分钟.md","path":"agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md","title":"ETF专题 Skill","category":"anthropic-skill","format":"markdown","content":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF历史分钟.md)","tokens":300,"sizeBytes":217},{"name":"ETF基准指数.md","path":"agent/src/skills/tushare/references/ETF专题/ETF基准指数.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF基准指数.md","title":"ETF专题 Skill","category":"anthropic-skill","format":"markdown","content":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF基准指数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF基准指数.md)","tokens":300,"sizeBytes":217},{"name":"ETF基本信息.md","path":"agent/src/skills/tushare/references/ETF专题/ETF基本信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF基本信息.md","title":"ETF专题 Skill","category":"anthropic-skill","format":"markdown","content":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF基本信息.md)","tokens":300,"sizeBytes":217},{"name":"ETF复权因子.md","path":"agent/src/skills/tushare/references/ETF专题/ETF复权因子.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF复权因子.md","title":"ETF专题 Skill","category":"anthropic-skill","format":"markdown","content":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF复权因子.md)","tokens":300,"sizeBytes":217},{"name":"ETF实时分钟.md","path":"agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md","title":"ETF专题 Skill","category":"anthropic-skill","format":"markdown","content":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF实时分钟.md)","tokens":300,"sizeBytes":217},{"name":"ETF实时日线.md","path":"agent/src/skills/tushare/references/ETF专题/ETF实时日线.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF实时日线.md","title":"ETF专题 Skill","category":"anthropic-skill","format":"markdown","content":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF实时日线.md)","tokens":300,"sizeBytes":217},{"name":"ETF日线行情.md","path":"agent/src/skills/tushare/references/ETF专题/ETF日线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF日线行情.md","title":"ETF专题 Skill","category":"anthropic-skill","format":"markdown","content":"# ETF专题 Skill\n\nPath: `agent/src/skills/tushare/references/ETF专题/ETF日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/ETF专题/ETF日线行情.md)","tokens":300,"sizeBytes":217},{"name":"债券回购日行情.md","path":"agent/src/skills/tushare/references/债券专题/债券回购日行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/债券回购日行情.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/债券回购日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/债券回购日行情.md)","tokens":300,"sizeBytes":214},{"name":"全球财经事件.md","path":"agent/src/skills/tushare/references/债券专题/全球财经事件.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/全球财经事件.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/全球财经事件.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/全球财经事件.md)","tokens":300,"sizeBytes":212},{"name":"可转债发行.md","path":"agent/src/skills/tushare/references/债券专题/可转债发行.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债发行.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债发行.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债发行.md)","tokens":300,"sizeBytes":210},{"name":"可转债基础信息.md","path":"agent/src/skills/tushare/references/债券专题/可转债基础信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债基础信息.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债基础信息.md)","tokens":300,"sizeBytes":214},{"name":"可转债技术面因子(专业版).md","path":"agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债技术面因子(专业版).md)","tokens":300,"sizeBytes":226},{"name":"可转债票面利率.md","path":"agent/src/skills/tushare/references/债券专题/可转债票面利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债票面利率.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债票面利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债票面利率.md)","tokens":300,"sizeBytes":214},{"name":"可转债行情.md","path":"agent/src/skills/tushare/references/债券专题/可转债行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债行情.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债行情.md)","tokens":300,"sizeBytes":210},{"name":"可转债赎回信息.md","path":"agent/src/skills/tushare/references/债券专题/可转债赎回信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债赎回信息.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债赎回信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债赎回信息.md)","tokens":300,"sizeBytes":214},{"name":"可转债转股价变动.md","path":"agent/src/skills/tushare/references/债券专题/可转债转股价变动.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债转股价变动.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债转股价变动.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债转股价变动.md)","tokens":300,"sizeBytes":216},{"name":"可转债转股结果.md","path":"agent/src/skills/tushare/references/债券专题/可转债转股结果.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债转股结果.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/可转债转股结果.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/可转债转股结果.md)","tokens":300,"sizeBytes":214},{"name":"国债收益率曲线.md","path":"agent/src/skills/tushare/references/债券专题/国债收益率曲线.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/国债收益率曲线.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/国债收益率曲线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/国债收益率曲线.md)","tokens":300,"sizeBytes":214},{"name":"大宗交易.md","path":"agent/src/skills/tushare/references/债券专题/大宗交易.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/大宗交易.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/大宗交易.md)","tokens":300,"sizeBytes":208},{"name":"大宗交易明细.md","path":"agent/src/skills/tushare/references/债券专题/大宗交易明细.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/大宗交易明细.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/大宗交易明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/大宗交易明细.md)","tokens":300,"sizeBytes":212},{"name":"柜台流通式债券报价.md","path":"agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/柜台流通式债券报价.md)","tokens":300,"sizeBytes":218},{"name":"柜台流通式债券最优报价.md","path":"agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md","title":"债券专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 债券专题 Skill\n\nPath: `agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/债券专题/柜台流通式债券最优报价.md)","tokens":300,"sizeBytes":222},{"name":"基金净值.md","path":"agent/src/skills/tushare/references/公募基金/基金净值.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金净值.md","title":"公募基金 Skill","category":"anthropic-skill","format":"markdown","content":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金净值.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金净值.md)","tokens":300,"sizeBytes":208},{"name":"基金分红.md","path":"agent/src/skills/tushare/references/公募基金/基金分红.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金分红.md","title":"公募基金 Skill","category":"anthropic-skill","format":"markdown","content":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金分红.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金分红.md)","tokens":300,"sizeBytes":208},{"name":"基金列表.md","path":"agent/src/skills/tushare/references/公募基金/基金列表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金列表.md","title":"公募基金 Skill","category":"anthropic-skill","format":"markdown","content":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金列表.md)","tokens":300,"sizeBytes":208},{"name":"基金技术面因子(专业版).md","path":"agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md","title":"公募基金 Skill","category":"anthropic-skill","format":"markdown","content":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金技术面因子(专业版).md)","tokens":300,"sizeBytes":224},{"name":"基金持仓.md","path":"agent/src/skills/tushare/references/公募基金/基金持仓.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金持仓.md","title":"公募基金 Skill","category":"anthropic-skill","format":"markdown","content":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金持仓.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金持仓.md)","tokens":300,"sizeBytes":208},{"name":"基金管理人.md","path":"agent/src/skills/tushare/references/公募基金/基金管理人.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金管理人.md","title":"公募基金 Skill","category":"anthropic-skill","format":"markdown","content":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金管理人.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金管理人.md)","tokens":300,"sizeBytes":210},{"name":"基金经理.md","path":"agent/src/skills/tushare/references/公募基金/基金经理.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金经理.md","title":"公募基金 Skill","category":"anthropic-skill","format":"markdown","content":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金经理.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金经理.md)","tokens":300,"sizeBytes":208},{"name":"基金规模.md","path":"agent/src/skills/tushare/references/公募基金/基金规模.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金规模.md","title":"公募基金 Skill","category":"anthropic-skill","format":"markdown","content":"# 公募基金 Skill\n\nPath: `agent/src/skills/tushare/references/公募基金/基金规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/公募基金/基金规模.md)","tokens":300,"sizeBytes":208},{"name":"外汇基础信息(海外).md","path":"agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md","title":"外汇数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 外汇数据 Skill\n\nPath: `agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/外汇数据/外汇基础信息(海外).md)","tokens":300,"sizeBytes":220},{"name":"外汇日线行情.md","path":"agent/src/skills/tushare/references/外汇数据/外汇日线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/外汇数据/外汇日线行情.md","title":"外汇数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 外汇数据 Skill\n\nPath: `agent/src/skills/tushare/references/外汇数据/外汇日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/外汇数据/外汇日线行情.md)","tokens":300,"sizeBytes":212},{"name":"上市公司公告.md","path":"agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md","title":"大模型语料专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/上市公司公告.md)","tokens":300,"sizeBytes":227},{"name":"上证e互动问答.md","path":"agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md","title":"大模型语料专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/上证e互动问答.md)","tokens":300,"sizeBytes":229},{"name":"券商研究报告.md","path":"agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md","title":"大模型语料专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/券商研究报告.md)","tokens":300,"sizeBytes":227},{"name":"国家政策库.md","path":"agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md","title":"大模型语料专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/国家政策库.md)","tokens":300,"sizeBytes":225},{"name":"新闻快讯(短讯).md","path":"agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md","title":"大模型语料专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻快讯(短讯).md)","tokens":300,"sizeBytes":231},{"name":"新闻联播文字稿.md","path":"agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md","title":"大模型语料专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻联播文字稿.md)","tokens":300,"sizeBytes":229},{"name":"新闻通讯(长篇).md","path":"agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md","title":"大模型语料专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/新闻通讯(长篇).md)","tokens":300,"sizeBytes":231},{"name":"深证易互动问答.md","path":"agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md","title":"大模型语料专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 大模型语料专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/大模型语料专题数据/深证易互动问答.md)","tokens":300,"sizeBytes":229},{"name":"居民消费价格指数(CPI).md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md","title":"价格指数 Skill","category":"anthropic-skill","format":"markdown","content":"# 价格指数 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md)","tokens":300,"sizeBytes":246},{"name":"工业生产者出厂价格指数(PPI).md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md","title":"价格指数 Skill","category":"anthropic-skill","format":"markdown","content":"# 价格指数 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/价格指数/工业生产者出厂价格指数(PPI).md)","tokens":300,"sizeBytes":252},{"name":"Hibor利率.md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md","title":"利率数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Hibor利率.md)","tokens":300,"sizeBytes":234},{"name":"Libor利率.md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md","title":"利率数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Libor利率.md)","tokens":300,"sizeBytes":234},{"name":"LPR贷款基础利率.md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md","title":"利率数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/LPR贷款基础利率.md)","tokens":300,"sizeBytes":238},{"name":"Shibor利率.md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md","title":"利率数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor利率.md)","tokens":300,"sizeBytes":236},{"name":"Shibor报价数据.md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md","title":"利率数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/Shibor报价数据.md)","tokens":300,"sizeBytes":240},{"name":"广州民间借贷利率.md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md","title":"利率数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/广州民间借贷利率.md)","tokens":300,"sizeBytes":236},{"name":"温州民间借贷利率.md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md","title":"利率数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 利率数据 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/利率数据/温州民间借贷利率.md)","tokens":300,"sizeBytes":236},{"name":"国内生产总值(GDP).md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md","title":"国民经济 Skill","category":"anthropic-skill","format":"markdown","content":"# 国民经济 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/国民经济/国内生产总值(GDP).md)","tokens":300,"sizeBytes":242},{"name":"采购经理指数(PMI).md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md","title":"景气度 Skill","category":"anthropic-skill","format":"markdown","content":"# 景气度 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/景气度/采购经理指数(PMI).md)","tokens":300,"sizeBytes":239},{"name":"社融增量(月度).md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md","title":"社会融资 Skill","category":"anthropic-skill","format":"markdown","content":"# 社会融资 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/金融/社会融资/社融增量(月度).md)","tokens":300,"sizeBytes":242},{"name":"货币供应量(月).md","path":"agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md","title":"货币供应量 Skill","category":"anthropic-skill","format":"markdown","content":"# 货币供应量 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国内宏观/金融/货币供应量/货币供应量(月).md)","tokens":300,"sizeBytes":245},{"name":"国债实际收益率曲线利率.md","path":"agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md","title":"美国利率 Skill","category":"anthropic-skill","format":"markdown","content":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债实际收益率曲线利率.md)","tokens":300,"sizeBytes":242},{"name":"国债收益率曲线利率.md","path":"agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md","title":"美国利率 Skill","category":"anthropic-skill","format":"markdown","content":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债收益率曲线利率.md)","tokens":300,"sizeBytes":238},{"name":"国债长期利率.md","path":"agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md","title":"美国利率 Skill","category":"anthropic-skill","format":"markdown","content":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率.md)","tokens":300,"sizeBytes":232},{"name":"国债长期利率平均值.md","path":"agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md","title":"美国利率 Skill","category":"anthropic-skill","format":"markdown","content":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/国债长期利率平均值.md)","tokens":300,"sizeBytes":238},{"name":"短期国债利率.md","path":"agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md","title":"美国利率 Skill","category":"anthropic-skill","format":"markdown","content":"# 美国利率 Skill\n\nPath: `agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/宏观经济/国际宏观/美国利率/短期国债利率.md)","tokens":300,"sizeBytes":232},{"name":"中信行业成分.md","path":"agent/src/skills/tushare/references/指数专题/中信行业成分.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/中信行业成分.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/中信行业成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/中信行业成分.md)","tokens":300,"sizeBytes":212},{"name":"中信行业指数日行情.md","path":"agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/中信行业指数日行情.md)","tokens":300,"sizeBytes":218},{"name":"国际主要指数.md","path":"agent/src/skills/tushare/references/指数专题/国际主要指数.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/国际主要指数.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/国际主要指数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/国际主要指数.md)","tokens":300,"sizeBytes":212},{"name":"大盘指数每日指标.md","path":"agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/大盘指数每日指标.md)","tokens":300,"sizeBytes":216},{"name":"指数历史分钟.md","path":"agent/src/skills/tushare/references/指数专题/指数历史分钟.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数历史分钟.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数历史分钟.md)","tokens":300,"sizeBytes":212},{"name":"指数周线行情.md","path":"agent/src/skills/tushare/references/指数专题/指数周线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数周线行情.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数周线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数周线行情.md)","tokens":300,"sizeBytes":212},{"name":"指数基本信息.md","path":"agent/src/skills/tushare/references/指数专题/指数基本信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数基本信息.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数基本信息.md)","tokens":300,"sizeBytes":212},{"name":"指数实时分钟.md","path":"agent/src/skills/tushare/references/指数专题/指数实时分钟.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数实时分钟.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数实时分钟.md)","tokens":300,"sizeBytes":212},{"name":"指数实时日线.md","path":"agent/src/skills/tushare/references/指数专题/指数实时日线.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数实时日线.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数实时日线.md)","tokens":300,"sizeBytes":212},{"name":"指数成分和权重.md","path":"agent/src/skills/tushare/references/指数专题/指数成分和权重.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数成分和权重.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数成分和权重.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数成分和权重.md)","tokens":300,"sizeBytes":214},{"name":"指数技术面因子(专业版).md","path":"agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数技术面因子(专业版).md)","tokens":300,"sizeBytes":224},{"name":"指数日线行情.md","path":"agent/src/skills/tushare/references/指数专题/指数日线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数日线行情.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数日线行情.md)","tokens":300,"sizeBytes":212},{"name":"指数月线行情.md","path":"agent/src/skills/tushare/references/指数专题/指数月线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数月线行情.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/指数月线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/指数月线行情.md)","tokens":300,"sizeBytes":212},{"name":"沪深市场每日交易统计.md","path":"agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/沪深市场每日交易统计.md)","tokens":300,"sizeBytes":220},{"name":"深圳市场每日交易情况.md","path":"agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/深圳市场每日交易情况.md)","tokens":300,"sizeBytes":220},{"name":"申万实时行情.md","path":"agent/src/skills/tushare/references/指数专题/申万实时行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万实时行情.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万实时行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万实时行情.md)","tokens":300,"sizeBytes":212},{"name":"申万行业分类.md","path":"agent/src/skills/tushare/references/指数专题/申万行业分类.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业分类.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业分类.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业分类.md)","tokens":300,"sizeBytes":212},{"name":"申万行业成分(分级).md","path":"agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业成分(分级).md)","tokens":300,"sizeBytes":220},{"name":"申万行业指数日行情.md","path":"agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md","title":"指数专题 Skill","category":"anthropic-skill","format":"markdown","content":"# 指数专题 Skill\n\nPath: `agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/指数专题/申万行业指数日行情.md)","tokens":300,"sizeBytes":218},{"name":"期权分钟行情.md","path":"agent/src/skills/tushare/references/期权数据/期权分钟行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权分钟行情.md","title":"期权数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权分钟行情.md)","tokens":300,"sizeBytes":212},{"name":"期权合约信息.md","path":"agent/src/skills/tushare/references/期权数据/期权合约信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权合约信息.md","title":"期权数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权合约信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权合约信息.md)","tokens":300,"sizeBytes":212},{"name":"期权日线行情.md","path":"agent/src/skills/tushare/references/期权数据/期权日线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权日线行情.md","title":"期权数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期权数据 Skill\n\nPath: `agent/src/skills/tushare/references/期权数据/期权日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期权数据/期权日线行情.md)","tokens":300,"sizeBytes":212},{"name":"交易日历.md","path":"agent/src/skills/tushare/references/期货数据/交易日历.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/交易日历.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/交易日历.md)","tokens":300,"sizeBytes":208},{"name":"仓单日报.md","path":"agent/src/skills/tushare/references/期货数据/仓单日报.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/仓单日报.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/仓单日报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/仓单日报.md)","tokens":300,"sizeBytes":208},{"name":"南华期货指数行情.md","path":"agent/src/skills/tushare/references/期货数据/南华期货指数行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/南华期货指数行情.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/南华期货指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/南华期货指数行情.md)","tokens":300,"sizeBytes":216},{"name":"历史分钟行情.md","path":"agent/src/skills/tushare/references/期货数据/历史分钟行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/历史分钟行情.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/历史分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/历史分钟行情.md)","tokens":300,"sizeBytes":212},{"name":"合约信息.md","path":"agent/src/skills/tushare/references/期货数据/合约信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/合约信息.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/合约信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/合约信息.md)","tokens":300,"sizeBytes":208},{"name":"实时分钟行情.md","path":"agent/src/skills/tushare/references/期货数据/实时分钟行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/实时分钟行情.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/实时分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/实时分钟行情.md)","tokens":300,"sizeBytes":212},{"name":"日线行情.md","path":"agent/src/skills/tushare/references/期货数据/日线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/日线行情.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/日线行情.md)","tokens":300,"sizeBytes":208},{"name":"期货主力与连续合约.md","path":"agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货主力与连续合约.md)","tokens":300,"sizeBytes":218},{"name":"期货主要品种交易周报.md","path":"agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货主要品种交易周报.md)","tokens":300,"sizeBytes":220},{"name":"期货合约涨跌停价格.md","path":"agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货合约涨跌停价格.md)","tokens":300,"sizeBytes":218},{"name":"期货周月线行情(每日更新).md","path":"agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/期货周月线行情(每日更新).md)","tokens":300,"sizeBytes":226},{"name":"每日持仓排名.md","path":"agent/src/skills/tushare/references/期货数据/每日持仓排名.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/每日持仓排名.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/每日持仓排名.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/每日持仓排名.md)","tokens":300,"sizeBytes":212},{"name":"每日结算参数.md","path":"agent/src/skills/tushare/references/期货数据/每日结算参数.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/每日结算参数.md","title":"期货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 期货数据 Skill\n\nPath: `agent/src/skills/tushare/references/期货数据/每日结算参数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/期货数据/每日结算参数.md)","tokens":300,"sizeBytes":212},{"name":"港股交易日历.md","path":"agent/src/skills/tushare/references/港股数据/港股交易日历.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股交易日历.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股交易日历.md)","tokens":300,"sizeBytes":212},{"name":"港股分钟行情.md","path":"agent/src/skills/tushare/references/港股数据/港股分钟行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股分钟行情.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股分钟行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股分钟行情.md)","tokens":300,"sizeBytes":212},{"name":"港股利润表.md","path":"agent/src/skills/tushare/references/港股数据/港股利润表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股利润表.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股利润表.md)","tokens":300,"sizeBytes":210},{"name":"港股基础信息.md","path":"agent/src/skills/tushare/references/港股数据/港股基础信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股基础信息.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股基础信息.md)","tokens":300,"sizeBytes":212},{"name":"港股复权因子.md","path":"agent/src/skills/tushare/references/港股数据/港股复权因子.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股复权因子.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股复权因子.md)","tokens":300,"sizeBytes":212},{"name":"港股复权行情.md","path":"agent/src/skills/tushare/references/港股数据/港股复权行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股复权行情.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股复权行情.md)","tokens":300,"sizeBytes":212},{"name":"港股实时日线.md","path":"agent/src/skills/tushare/references/港股数据/港股实时日线.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股实时日线.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股实时日线.md)","tokens":300,"sizeBytes":212},{"name":"港股日线行情.md","path":"agent/src/skills/tushare/references/港股数据/港股日线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股日线行情.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股日线行情.md)","tokens":300,"sizeBytes":212},{"name":"港股现金流量表.md","path":"agent/src/skills/tushare/references/港股数据/港股现金流量表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股现金流量表.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股现金流量表.md)","tokens":300,"sizeBytes":214},{"name":"港股财务指标数据.md","path":"agent/src/skills/tushare/references/港股数据/港股财务指标数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股财务指标数据.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股财务指标数据.md)","tokens":300,"sizeBytes":216},{"name":"港股资产负债表.md","path":"agent/src/skills/tushare/references/港股数据/港股资产负债表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股资产负债表.md","title":"港股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 港股数据 Skill\n\nPath: `agent/src/skills/tushare/references/港股数据/港股资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/港股数据/港股资产负债表.md)","tokens":300,"sizeBytes":214},{"name":"上海黄金基础信息.md","path":"agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md","title":"现货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 现货数据 Skill\n\nPath: `agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/现货数据/上海黄金基础信息.md)","tokens":300,"sizeBytes":216},{"name":"上海黄金现货日行情.md","path":"agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md","title":"现货数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 现货数据 Skill\n\nPath: `agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/现货数据/上海黄金现货日行情.md)","tokens":300,"sizeBytes":218},{"name":"美股交易日历.md","path":"agent/src/skills/tushare/references/美股数据/美股交易日历.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股交易日历.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股交易日历.md)","tokens":300,"sizeBytes":212},{"name":"美股利润表.md","path":"agent/src/skills/tushare/references/美股数据/美股利润表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股利润表.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股利润表.md)","tokens":300,"sizeBytes":210},{"name":"美股基础信息.md","path":"agent/src/skills/tushare/references/美股数据/美股基础信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股基础信息.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股基础信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股基础信息.md)","tokens":300,"sizeBytes":212},{"name":"美股复权因子.md","path":"agent/src/skills/tushare/references/美股数据/美股复权因子.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股复权因子.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股复权因子.md)","tokens":300,"sizeBytes":212},{"name":"美股复权行情.md","path":"agent/src/skills/tushare/references/美股数据/美股复权行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股复权行情.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股复权行情.md)","tokens":300,"sizeBytes":212},{"name":"美股日线行情.md","path":"agent/src/skills/tushare/references/美股数据/美股日线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股日线行情.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股日线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股日线行情.md)","tokens":300,"sizeBytes":212},{"name":"美股现金流量表.md","path":"agent/src/skills/tushare/references/美股数据/美股现金流量表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股现金流量表.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股现金流量表.md)","tokens":300,"sizeBytes":214},{"name":"美股财务指标数据.md","path":"agent/src/skills/tushare/references/美股数据/美股财务指标数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股财务指标数据.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股财务指标数据.md)","tokens":300,"sizeBytes":216},{"name":"美股资产负债表.md","path":"agent/src/skills/tushare/references/美股数据/美股资产负债表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股资产负债表.md","title":"美股数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 美股数据 Skill\n\nPath: `agent/src/skills/tushare/references/美股数据/美股资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/美股数据/美股资产负债表.md)","tokens":300,"sizeBytes":214},{"name":"做市借券交易汇总(停).md","path":"agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md","title":"两融及转融通 Skill","category":"anthropic-skill","format":"markdown","content":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/做市借券交易汇总(停).md)","tokens":300,"sizeBytes":238},{"name":"融资融券交易明细.md","path":"agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md","title":"两融及转融通 Skill","category":"anthropic-skill","format":"markdown","content":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易明细.md)","tokens":300,"sizeBytes":232},{"name":"融资融券交易汇总.md","path":"agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md","title":"两融及转融通 Skill","category":"anthropic-skill","format":"markdown","content":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券交易汇总.md)","tokens":300,"sizeBytes":232},{"name":"融资融券标的(盘前).md","path":"agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md","title":"两融及转融通 Skill","category":"anthropic-skill","format":"markdown","content":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/融资融券标的(盘前).md)","tokens":300,"sizeBytes":236},{"name":"转融券交易明细(停).md","path":"agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md","title":"两融及转融通 Skill","category":"anthropic-skill","format":"markdown","content":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易明细(停).md)","tokens":300,"sizeBytes":236},{"name":"转融券交易汇总(停).md","path":"agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md","title":"两融及转融通 Skill","category":"anthropic-skill","format":"markdown","content":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融券交易汇总(停).md)","tokens":300,"sizeBytes":236},{"name":"转融资交易汇总.md","path":"agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md","title":"两融及转融通 Skill","category":"anthropic-skill","format":"markdown","content":"# 两融及转融通 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/两融及转融通/转融资交易汇总.md)","tokens":300,"sizeBytes":230},{"name":"前十大流通股东.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/前十大流通股东.md)","tokens":300,"sizeBytes":224},{"name":"前十大股东.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/前十大股东.md)","tokens":300,"sizeBytes":220},{"name":"大宗交易.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/大宗交易.md)","tokens":300,"sizeBytes":218},{"name":"股东人数.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股东人数.md)","tokens":300,"sizeBytes":218},{"name":"股东增减持.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股东增减持.md)","tokens":300,"sizeBytes":220},{"name":"股权质押明细数据.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股权质押明细数据.md)","tokens":300,"sizeBytes":226},{"name":"股权质押统计数据.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股权质押统计数据.md)","tokens":300,"sizeBytes":226},{"name":"股票回购.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票回购.md)","tokens":300,"sizeBytes":218},{"name":"股票开户数据(停).md","path":"agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(停).md)","tokens":300,"sizeBytes":228},{"name":"股票开户数据(旧).md","path":"agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/股票开户数据(旧).md)","tokens":300,"sizeBytes":228},{"name":"限售股解禁.md","path":"agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md","title":"参考数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 参考数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/参考数据/限售股解禁.md)","tokens":300,"sizeBytes":220},{"name":"IPO新股上市.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/IPO新股上市.md)","tokens":300,"sizeBytes":224},{"name":"ST股票列表.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/ST股票列表.md)","tokens":300,"sizeBytes":222},{"name":"ST风险警示板股票.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/ST风险警示板股票.md)","tokens":300,"sizeBytes":228},{"name":"上市公司基本信息.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/上市公司基本信息.md)","tokens":300,"sizeBytes":226},{"name":"上市公司管理层.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/上市公司管理层.md)","tokens":300,"sizeBytes":224},{"name":"交易日历.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/交易日历.md)","tokens":300,"sizeBytes":218},{"name":"北交所新旧代码对照.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/北交所新旧代码对照.md)","tokens":300,"sizeBytes":228},{"name":"每日股本(盘前).md","path":"agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/每日股本(盘前).md)","tokens":300,"sizeBytes":226},{"name":"沪深港通股票列表.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/沪深港通股票列表.md)","tokens":300,"sizeBytes":226},{"name":"管理层薪酬和持股.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/管理层薪酬和持股.md)","tokens":300,"sizeBytes":226},{"name":"股票列表.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票列表.md)","tokens":300,"sizeBytes":218},{"name":"股票历史列表.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票历史列表.md)","tokens":300,"sizeBytes":222},{"name":"股票曾用名.md","path":"agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md","title":"基础数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基础数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/基础数据/股票曾用名.md)","tokens":300,"sizeBytes":220},{"name":"东方财富App热榜.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富App热榜.md)","tokens":300,"sizeBytes":234},{"name":"东方财富概念成分.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念成分.md)","tokens":300,"sizeBytes":232},{"name":"东方财富概念板块.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东方财富概念板块.md)","tokens":300,"sizeBytes":232},{"name":"东财概念和行业指数行情.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/东财概念和行业指数行情.md)","tokens":300,"sizeBytes":238},{"name":"同花顺App热榜数.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺App热榜数.md)","tokens":300,"sizeBytes":234},{"name":"同花顺概念和行业指数行情.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺概念和行业指数行情.md)","tokens":300,"sizeBytes":240},{"name":"同花顺涨跌停榜单.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺涨跌停榜单.md)","tokens":300,"sizeBytes":232},{"name":"同花顺行业概念成分.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念成分.md)","tokens":300,"sizeBytes":234},{"name":"同花顺行业概念板块.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/同花顺行业概念板块.md)","tokens":300,"sizeBytes":234},{"name":"市场游资最全名录.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/市场游资最全名录.md)","tokens":300,"sizeBytes":232},{"name":"开盘竞价成交(当日).md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/开盘竞价成交(当日).md)","tokens":300,"sizeBytes":236},{"name":"榜单数据(开盘啦).md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/榜单数据(开盘啦).md)","tokens":300,"sizeBytes":234},{"name":"涨停最强板块统计.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨停最强板块统计.md)","tokens":300,"sizeBytes":232},{"name":"涨停股票连板天梯.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨停股票连板天梯.md)","tokens":300,"sizeBytes":232},{"name":"涨跌停和炸板数据.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/涨跌停和炸板数据.md)","tokens":300,"sizeBytes":232},{"name":"游资交易每日明细.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/游资交易每日明细.md)","tokens":300,"sizeBytes":232},{"name":"通达信板块信息.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块信息.md)","tokens":300,"sizeBytes":230},{"name":"通达信板块成分.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块成分.md)","tokens":300,"sizeBytes":230},{"name":"通达信板块行情.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/通达信板块行情.md)","tokens":300,"sizeBytes":230},{"name":"题材成分(开盘啦).md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/题材成分(开盘啦).md)","tokens":300,"sizeBytes":234},{"name":"龙虎榜机构交易单.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜机构交易单.md)","tokens":300,"sizeBytes":232},{"name":"龙虎榜每日统计单.md","path":"agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md","title":"打板专题数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 打板专题数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/打板专题数据/龙虎榜每日统计单.md)","tokens":300,"sizeBytes":232},{"name":"AH股比价.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/AH股比价.md)","tokens":300,"sizeBytes":220},{"name":"中央结算系统持股明细.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股明细.md)","tokens":300,"sizeBytes":230},{"name":"中央结算系统持股统计.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/中央结算系统持股统计.md)","tokens":300,"sizeBytes":230},{"name":"券商月度金股.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/券商月度金股.md)","tokens":300,"sizeBytes":222},{"name":"券商盈利预测数据.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/券商盈利预测数据.md)","tokens":300,"sizeBytes":226},{"name":"机构调研数据.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/机构调研数据.md)","tokens":300,"sizeBytes":222},{"name":"每日筹码分布.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/每日筹码分布.md)","tokens":300,"sizeBytes":222},{"name":"每日筹码及胜率.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/每日筹码及胜率.md)","tokens":300,"sizeBytes":224},{"name":"沪深股通持股明细.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/沪深股通持股明细.md)","tokens":300,"sizeBytes":226},{"name":"神奇九转指标.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/神奇九转指标.md)","tokens":300,"sizeBytes":222},{"name":"股票开盘集合竞价数据.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票开盘集合竞价数据.md)","tokens":300,"sizeBytes":230},{"name":"股票技术面因子(专业版).md","path":"agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票技术面因子(专业版).md)","tokens":300,"sizeBytes":234},{"name":"股票收盘集合竞价数据.md","path":"agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md","title":"特色数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 特色数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/特色数据/股票收盘集合竞价数据.md)","tokens":300,"sizeBytes":230},{"name":"历史分钟.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/历史分钟.md)","tokens":300,"sizeBytes":218},{"name":"历史日线.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/历史日线.md)","tokens":300,"sizeBytes":218},{"name":"周月线复权行情(每日更新).md","path":"agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周月线复权行情(每日更新).md)","tokens":300,"sizeBytes":236},{"name":"周月线行情(每日更新).md","path":"agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周月线行情(每日更新).md)","tokens":300,"sizeBytes":232},{"name":"周线行情.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/周线行情.md)","tokens":300,"sizeBytes":218},{"name":"备用行情.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/备用行情.md)","tokens":300,"sizeBytes":218},{"name":"复权因子.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/复权因子.md)","tokens":300,"sizeBytes":218},{"name":"复权行情.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/复权行情.md)","tokens":300,"sizeBytes":218},{"name":"实时分钟.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/实时分钟.md)","tokens":300,"sizeBytes":218},{"name":"实时日线.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/实时日线.md)","tokens":300,"sizeBytes":218},{"name":"月线行情.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/月线行情.md)","tokens":300,"sizeBytes":218},{"name":"每日停复牌信息.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日停复牌信息.md)","tokens":300,"sizeBytes":224},{"name":"每日指标.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日指标.md)","tokens":300,"sizeBytes":218},{"name":"每日涨跌停价格.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/每日涨跌停价格.md)","tokens":300,"sizeBytes":224},{"name":"沪深股通十大成交股.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/沪深股通十大成交股.md)","tokens":300,"sizeBytes":228},{"name":"港股通十大成交股.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通十大成交股.md)","tokens":300,"sizeBytes":226},{"name":"港股通每日成交统计.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通每日成交统计.md)","tokens":300,"sizeBytes":228},{"name":"港股通每月成交统计.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/港股通每月成交统计.md)","tokens":300,"sizeBytes":228},{"name":"通用行情接口.md","path":"agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md","title":"行情数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 行情数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/行情数据/通用行情接口.md)","tokens":300,"sizeBytes":222},{"name":"业绩快报.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/业绩快报.md)","tokens":300,"sizeBytes":218},{"name":"业绩预告.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/业绩预告.md)","tokens":300,"sizeBytes":218},{"name":"主营业务构成.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/主营业务构成.md)","tokens":300,"sizeBytes":222},{"name":"分红送股数据.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/分红送股数据.md)","tokens":300,"sizeBytes":222},{"name":"利润表.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/利润表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/利润表.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/利润表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/利润表.md)","tokens":300,"sizeBytes":216},{"name":"现金流量表.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/现金流量表.md)","tokens":300,"sizeBytes":220},{"name":"财务审计意见.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财务审计意见.md)","tokens":300,"sizeBytes":222},{"name":"财务指标数据.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财务指标数据.md)","tokens":300,"sizeBytes":222},{"name":"财报披露日期表.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/财报披露日期表.md)","tokens":300,"sizeBytes":224},{"name":"资产负债表.md","path":"agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md","title":"财务数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 财务数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/财务数据/资产负债表.md)","tokens":300,"sizeBytes":220},{"name":"个股资金流向.md","path":"agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md","title":"资金流向数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向.md)","tokens":300,"sizeBytes":228},{"name":"个股资金流向(DC).md","path":"agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md","title":"资金流向数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(DC).md)","tokens":300,"sizeBytes":236},{"name":"个股资金流向(THS).md","path":"agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md","title":"资金流向数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/个股资金流向(THS).md)","tokens":300,"sizeBytes":238},{"name":"大盘资金流向(DC).md","path":"agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md","title":"资金流向数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/大盘资金流向(DC).md)","tokens":300,"sizeBytes":236},{"name":"板块资金流向(DC).md","path":"agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md","title":"资金流向数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(DC).md)","tokens":300,"sizeBytes":236},{"name":"板块资金流向(THS).md","path":"agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md","title":"资金流向数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/板块资金流向(THS).md)","tokens":300,"sizeBytes":238},{"name":"沪深港通资金流向.md","path":"agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md","title":"资金流向数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/沪深港通资金流向.md)","tokens":300,"sizeBytes":232},{"name":"行业资金流向(THS).md","path":"agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md","title":"资金流向数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 资金流向数据 Skill\n\nPath: `agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/股票数据/资金流向数据/行业资金流向(THS).md)","tokens":300,"sizeBytes":238},{"name":"全国电影剧本备案数据.md","path":"agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md","title":"TMT行业 Skill","category":"anthropic-skill","format":"markdown","content":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/全国电影剧本备案数据.md)","tokens":300,"sizeBytes":233},{"name":"全国电视剧备案公示数据.md","path":"agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md","title":"TMT行业 Skill","category":"anthropic-skill","format":"markdown","content":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/全国电视剧备案公示数据.md)","tokens":300,"sizeBytes":235},{"name":"台湾电子产业月营收.md","path":"agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md","title":"TMT行业 Skill","category":"anthropic-skill","format":"markdown","content":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收.md)","tokens":300,"sizeBytes":231},{"name":"台湾电子产业月营收明细.md","path":"agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md","title":"TMT行业 Skill","category":"anthropic-skill","format":"markdown","content":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/台湾电子产业月营收明细.md)","tokens":300,"sizeBytes":235},{"name":"影院日度票房.md","path":"agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md","title":"TMT行业 Skill","category":"anthropic-skill","format":"markdown","content":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/影院日度票房.md)","tokens":300,"sizeBytes":225},{"name":"电影周度票房.md","path":"agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md","title":"TMT行业 Skill","category":"anthropic-skill","format":"markdown","content":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影周度票房.md)","tokens":300,"sizeBytes":225},{"name":"电影日度票房.md","path":"agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md","title":"TMT行业 Skill","category":"anthropic-skill","format":"markdown","content":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影日度票房.md)","tokens":300,"sizeBytes":225},{"name":"电影月度票房.md","path":"agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md","title":"TMT行业 Skill","category":"anthropic-skill","format":"markdown","content":"# TMT行业 Skill\n\nPath: `agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/行业经济/TMT行业/电影月度票房.md)","tokens":300,"sizeBytes":225},{"name":"各渠道公募基金销售保有规模占比.md","path":"agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md","title":"基金销售行业数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基金销售行业数据 Skill\n\nPath: `agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/财富管理/基金销售行业数据/各渠道公募基金销售保有规模占比.md)","tokens":300,"sizeBytes":252},{"name":"销售机构公募基金销售保有规模.md","path":"agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md","title":"基金销售行业数据 Skill","category":"anthropic-skill","format":"markdown","content":"# 基金销售行业数据 Skill\n\nPath: `agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/references/财富管理/基金销售行业数据/销售机构公募基金销售保有规模.md)","tokens":300,"sizeBytes":250},{"name":"SKILL.md","path":"agent/src/skills/tushare/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/SKILL.md","title":"tushare","category":"anthropic-skill","format":"markdown","content":"# tushare\n\nPath: `agent/src/skills/tushare/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/tushare/SKILL.md)","tokens":300,"sizeBytes":175},{"name":"SKILL.md","path":"agent/src/skills/us-etf-flow/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/us-etf-flow/SKILL.md","title":"us-etf-flow","category":"anthropic-skill","format":"markdown","content":"# us-etf-flow\n\nPath: `agent/src/skills/us-etf-flow/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/us-etf-flow/SKILL.md)","tokens":300,"sizeBytes":187},{"name":"SKILL.md","path":"agent/src/skills/valuation-model/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/valuation-model/SKILL.md","title":"valuation-model","category":"anthropic-skill","format":"markdown","content":"# valuation-model\n\nPath: `agent/src/skills/valuation-model/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/valuation-model/SKILL.md)","tokens":300,"sizeBytes":199},{"name":"SKILL.md","path":"agent/src/skills/vnpy-export/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/vnpy-export/SKILL.md","title":"vnpy-export","category":"anthropic-skill","format":"markdown","content":"# vnpy-export\n\nPath: `agent/src/skills/vnpy-export/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/vnpy-export/SKILL.md)","tokens":300,"sizeBytes":187},{"name":"SKILL.md","path":"agent/src/skills/volatility/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/volatility/SKILL.md","title":"volatility","category":"anthropic-skill","format":"markdown","content":"# volatility\n\nPath: `agent/src/skills/volatility/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/volatility/SKILL.md)","tokens":300,"sizeBytes":184},{"name":"SKILL.md","path":"agent/src/skills/web-reader/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/web-reader/SKILL.md","title":"web-reader","category":"anthropic-skill","format":"markdown","content":"# web-reader\n\nPath: `agent/src/skills/web-reader/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/web-reader/SKILL.md)","tokens":300,"sizeBytes":184},{"name":"tool_get_options_chain.md","path":"agent/src/skills/yfinance/references/tool_get_options_chain.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/tool_get_options_chain.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/tool_get_options_chain.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/tool_get_options_chain.md)","tokens":300,"sizeBytes":242},{"name":"tool_get_stock_profile.md","path":"agent/src/skills/yfinance/references/tool_get_stock_profile.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/tool_get_stock_profile.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/tool_get_stock_profile.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/tool_get_stock_profile.md)","tokens":300,"sizeBytes":242},{"name":"yahoo_client_get_chart.md","path":"agent/src/skills/yfinance/references/yahoo_client_get_chart.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_chart.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_chart.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_chart.md)","tokens":300,"sizeBytes":242},{"name":"yahoo_client_get_options.md","path":"agent/src/skills/yfinance/references/yahoo_client_get_options.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_options.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_options.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_options.md)","tokens":300,"sizeBytes":246},{"name":"yahoo_client_get_quote_summary.md","path":"agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_get_quote_summary.md)","tokens":300,"sizeBytes":258},{"name":"yahoo_client_search.md","path":"agent/src/skills/yfinance/references/yahoo_client_search.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_search.md","title":"References Skill","category":"anthropic-skill","format":"markdown","content":"# References Skill\n\nPath: `agent/src/skills/yfinance/references/yahoo_client_search.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/references/yahoo_client_search.md)","tokens":300,"sizeBytes":236},{"name":"SKILL.md","path":"agent/src/skills/yfinance/SKILL.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/SKILL.md","title":"yfinance","category":"anthropic-skill","format":"markdown","content":"# yfinance\n\nPath: `agent/src/skills/yfinance/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/yfinance/SKILL.md)","tokens":300,"sizeBytes":178},{"name":"paper_trade_step-v1.1.json","path":"agent/src/tools/paper_trade/paper_trade_step-v1.1.json","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/tools/paper_trade/paper_trade_step-v1.1.json","title":"Paper_trade Skill","category":"anthropic-skill","format":"json","content":"# Paper_trade Skill\n\nPath: `agent/src/tools/paper_trade/paper_trade_step-v1.1.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/tools/paper_trade/paper_trade_step-v1.1.json)","tokens":300,"sizeBytes":227},{"name":"README.md","path":"agent/src/tools/paper_trade/README.md","rawUrl":"https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/tools/paper_trade/README.md","title":"paper_trade Documentation","category":"plugin-manifest","format":"markdown","content":"# paper_trade Documentation\n\nPath: `agent/src/tools/paper_trade/README.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/tools/paper_trade/README.md)","tokens":300,"sizeBytes":201}],"systemPromptSnippet":"\n\n# Identity Skill\n\nPath: `agent/evals/harness/cases/identity/explicit_a_share.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/explicit_a_share.json)\n\n\n# Identity Skill\n\nPath: `agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/cases/identity/fixtures/identity/a_h_hengrui.v1.json)\n\n\n# Harness Skill\n\nPath: `agent/evals/harness/README.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/evals/harness/README.md)\n\n\n---\nname: vibe-trading\nversion: 0.1.18\ndescription: Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).\ndependencies:\n python: \">=3.11\"\n pip:\n - vibe-trading-ai\nenv:\n - name: TUSHARE_TOKEN\n description: \"Tushare API token for China A-share data (optional — HK/US/Canada/crypto work without any key)\"\n required: false\n - name: OPENAI_API_KEY\n description: \"OpenAI-compatible API key — only needed for run_swarm (multi-agent teams). All other tools work without it.\"\n required: false\n - name: LANGCHAIN_MODEL_NAME\n description: \"LLM model name for run_swarm (e.g. deepseek/deepseek-v4-pro). Only needed if using run_swarm.\"\n required: false\nmcp:\n command: vibe-trading-mcp\n args: []\n---\n\n# Vibe-Trading\n\nProfessional finance research toolkit with AI-powered backtesting (11 engines), multi-agent teams, 90 specialized skills, the **Alpha Zoo** (478 pre-built quantitative alphas across qlib158 / alpha101 / gtja191 / academic / fundamental with one-line CLI benchmarking), and the Shadow Account loop — extract your implicit trading rules from a journal, backtest them across A股/港股/美股/crypto, then see where they would have served you better.\n\n## Setup\n\n```bash\npip install vibe-trading-ai\n```\n\n> **Package name vs commands:** The PyPI package is `vibe-trading-ai`. Once installed, you get:\n>\n> | Command | Purpose |\n> |---------|---------|\n> | `vibe-trading` | Interactive CLI / TUI |\n> | `vibe-trading serve` | Launch FastAPI web server |\n> | `vibe-trading-mcp` | Start MCP server (for Claude Desktop, OpenClaw, Cursor, etc.) |\n\nAdd to your agent's MCP config:\n\n```json\n{\n \"mcpServers\": {\n \"vibe-trading\": {\n \"command\": \"vibe-trading-mcp\"\n }\n }\n}\n```\n\n### API Key Requirements\n\nCore research MCP tools work with zero API keys for HK/US/Canada/crypto. After `pip install`, backtesting, market data, factor analysis, options pricing, chart patterns, web search, document reading, trade journal analysis, shadow-account extraction/backtest/report, the Alpha Zoo (478 pre-built alphas), and all 90 skills are ready to use. IBKR tools require a local TWS / IB Gateway session; `run_swarm` requires an LLM key.\n\n| Feature | Key needed | When |\n|---------|-----------|------|\n| HK/US/Canada equities & crypto | None | Always free (yfinance / stooq / yahoo + OKX) |\n| China A-share data | None | Free via akshare / baostock / tencent / sina / eastmoney fallback (`TUSHARE_TOKEN` optional for premium quality) |\n| Premium US fundamentals/quotes | `FINNHUB_API_KEY` / `ALPHAVANTAGE_API_KEY` / `TIINGO_API_KEY` / `FMP_API_KEY` | Only for optional-key providers (graceful fallback to free sources) |\n| Multi-agent swarm (`run_swarm`) | `OPENAI_API_KEY` + `LANGCHAIN_MODEL_NAME` | Swarm spawns internal LLM workers |\n\n## What You Can Do\n\n### Shadow Account — flagship loop\n\nFeed a CSV broker export (同花顺 / 东财 / 富途 / generic), and the agent will:\n1. `analyze_trade_journal` — profile your behavior (holding period, win rate, disposition effect, chasing, overtrading, anchoring).\n2. `extract_shadow_strategy` — distill 3-5 if-then rules that describe your profitable roundtrips.\n3. `run_shadow_backtest` — backtest those rules across A/HK/US/crypto and compute delta-PnL vs your realized trades.\n4. `render_shadow_report` — produce an HTML/PDF report (8 sections + charts) with today's matching signals.\n5. `scan_shadow_signals` — list today's symbols that match your shadow's entry cadence (research only).\n\n### Backtesting\nCreate and run quantitative strategies across 11 engines (ChinaA, GlobalEquity, IndiaEquity, KoreaEquity, VietnamEquity, BrazilEquity, Crypto, ChinaFutures, GlobalFutures, Forex, cross-market Composite) plus an options portfolio engine, with 29 market-data sources (auto-detect + ordered fallback; the hosted forex `tickerall` source is explicit-only):\n- **HK/US equities** via yfinance / stooq / yahoo (free, no API key); optionally via **Longbridge** historical OHLCV (`longbridge`, requires the optional SDK and `LONGBRIDGE_APP_KEY` / `LONGBRIDGE_APP_SECRET` / `LONGBRIDGE_ACCESS_TOKEN`). To force it for a run, set `\"source\": \"longbridge\"` in `config.json`.\n- **Canada equities (TSX/TSXV)** via yahoo / yfinance using Yahoo's canonical `.TO` (TSX, e.g. `TD.TO`) or `.V` (TSXV, e.g. `PNG.V`) suffixes — free, no API key. The GlobalEquity engine uses CAD identity, whole-share orders, configurable Canadian commission/slippage, and the TSX/TSXV price-increment grid.\n- **India equities (NSE/BSE)** via yahoo / yfinance using `.NS` (NSE, e.g. `RELIANCE.NS`) or `.BO` (BSE, e.g. `500325.BO`) — free, no API key. The `IndiaEquityEngine` models T+1 delivery, no overnight shorts (set `allow_short` for intraday), configurable circuit bands, 1-share lots, and the STT/stamp-duty/exchange/GST cost stack. Optionally back-fill from your live broker via the `india_broker` source (Shoonya/Dhan; requires broker login).\n- **Korea equities (KRX: KOSPI/KOSDAQ)** via pykrx using `.KS` (KOSPI, e.g. `005930.KS`) or `.KQ` (KOSDAQ, e.g. `247540.KQ`) — free, no API key (`pip install \"vibe-trading-ai[krx]\"`; yahoo/yfinance fallback needs no extra). pykrx serves **daily bars only** (an intraday request falls through to another source) and its adjusted series is Naver-backed rather than a verbatim KRX print. The `KoreaEquityEngine` models same-day round trips (no T+1), the ±30% daily price limit measured from the previous close and quantized to the KRX tick grid, tick-rounded fills, the 0.20% sell-side transaction tax (2026 rate), and 1-share lots. It is **long-only**: `allow_short` is refused, because KRX covered-short and uptick rules cannot be enforced on daily bars.\n- **Vietnam equities (HOSE)** via yahoo / yfinance using `.VN` (e.g. `VIC.VN`) — no API key. Yahoo officially lists HOSE but not HNX or UPCOM; those venues are unsupported and need the `local` source. yfinance is an unofficial Yahoo client, so availability is best-effort and subject to Yahoo's personal-use terms. The `VietnamEquityEngine` approximates the formal T+2 settlement cycle — shares bought on day T normally become sellable during the afternoon session on T+2, informally called T+1.5 — as a two-bar hold on daily data (`vn_settlement_bars` covers scenario testing and future rule changes). It applies HOSE's normal ±7% band around the reference price, rounding the ceiling down and the floor up to the 10/50/100-VND tick grid, and uses 100-share round lots; odd-lot trading is not modelled. Costs are configurable brokerage plus, for individual investors, 0.1% sell-side personal income tax on gross proceeds. It is **long-only**, because operational cash-equity short selling is not generally available in Vietnam.\n- **Cryptocurrency** via OKX or CCXT/100+ exchanges (free, no API key)\n- **China A-shares** via AKShare / baostock / tencent / sina / eastmoney (free, no API key) — `TUSHARE_TOKEN` optional for premium quality\n- **Futures, forex, macro** via AKShare (free, no API key)\n- **Forex / metals with no local terminal** via the hosted **TickerAll** MetaTrader 5 feed (`source=\"tickerall\"`, `TICKERALL_API_KEY` + `TICKERALL_ACCOUNT_ID`, read-only) — the same broker feed as the `mt5` loader but over a hosted API on any OS. **Explicit-only** (never an automatic fallback).\n- **HK & A-share equities** via Futu (broker login required, optional)\n- **Local CSV/parquet bars** via the `local` loader (offline, no network)\n- **Premium cross-market data** via QVeris (optional API key)\n- **Premium US data** via optional-key finnhub / alphavantage / tiingo / fmp (graceful fallback to free sources)\n\nFactors: the Alpha101 and QLib158 zoos are tagged for the `equity_in` and `equity_kr` universes, so they compute on NSE/BSE and KRX bars (the GTJA191 zoo stays China-only). Live/paper India trading uses the Shoonya / Dhan connectors (paper + read-only live; live order placement is structurally disabled because those brokers expose no paper/live switch). Live/paper Korea equity trading uses the **KIS** connector (한국투자증권; genuine 모의투자 paper sandbox on a separate host/port from 실전투자 live, plus read-only live — no mandate-gated live-trade profile yet). Live/paper Korea crypto trading uses the **Upbit** connector (KRW markets; paper + read-only live — no runtime paper/live discriminator, so live order placement is structurally disabled, same as Shoonya/Dhan). Read-only Korea/US equity access is also available via the **Toss Securities** connector (토스증권) — no verified sandbox, so order placement always refuses, same as Trading 212.\n\nExample workflow:\n1. Use `list_skills()` to discover strategy patterns\n2. Use `load_skill(\"strategy-generate\")` for the strategy creation guide\n3. Use `write_file()` to create `config.json` and `code/signal_engine.py`\n4. Use `backtest()` to run and get metrics (Sharpe, return, drawdown, etc.)\n\n### Multi-Agent Swarm Teams\n30 pre-built agent teams for complex research:\n- **Investment Committee**: bull/bear debate → risk review → PM decision\n- **Global Equities Desk**: A-share + HK/US + crypto → global strategist\n- **Crypto Trading Desk**: funding/basis + liquidation + flow → risk manager\n- **Earnings Research Desk**: fundamentals + revisions + options → earnings strategist\n- **Macro/Rates/FX Desk**: rates + FX + commodities → macro PM\n- **Quant Strategy Desk**: screening → factor research → backtest → risk audit\n- **Risk Committee**: drawdown, tail risk, regime analysis\n- And 23 more specialized teams\n\nUse `list_swarm_presets()` to see all teams, then `run_swarm()` to execute.\n\n### Alpha Zoo (478 pre-built alphas)\nOne-line cross-sectional IC / IR / alive-reversed-dead categorisation across five bundled zoos:\n- **qlib158** (154 alphas) — Microsoft Qlib's `Alpha158` feature handler, Apache-2.0 with pinned commit SHA.\n- **alpha101** (101 alphas) — Kakushadze (2015) \"101 Formulaic Alphas\" (arXiv:1601.00991), written from the paper appendix.\n- **gtja191** (191 alphas) — Guotai Junan 2014 \"191 Short-period Trading Alpha Factors\" research report.\n- **academic** (12 factors) — Fama-French 5 + Carhart momentum + Jegadeesh reversal + George-Hwang 52-week-high + Amihud illiquidity + Harvey-Siddique skew + Frazzini-Pedersen betting-against-beta (price-based proxies) + a correlation-rewiring stability score (from the in-repo correlation-regime skill).\n- **fundamental** (20 factors) — PIT-safe earnings yield, ROE, gross profitability and asset growth, plus ten SEC XBRL additions: book-to-market, FCF yield, accruals, ROA, CFO yield, operating margin, cash-to-assets, leverage, revenue growth and earnings growth, from daily fundamental panels; three CN A-share daily-valuation factors (PB, PS_TTM, dividend yield) fed by the ``val:*`` panel namespace from tushare daily_basic; and three industry-neutralized variants of those CN value factors, z-scored within Shenwan L1 peers against a PIT SW2021 industry tag from tushare index_member_all (membership is point-in-time; the SW2021 taxonomy itself is retroactive).\n\nEach alpha ships with `__alpha_meta__` (formula LaTeX + theme + universe + warmup + columns required), guarded by an AST purity gate + 300-row lookahead sentinel test. Use the `vibe-trading alpha {list,show,bench,compare,export-manifest}` CLI, the `/alpha/*` REST routes (browser at `/alpha-zoo`), or compose multi-factor signals via `ZooSignalEngine.from_zoo(...)`.\n\n### Finance Skills (90)\nComprehensive knowledge base covering:\n- Technical analysis (candlestick, Elliott wave, Ichimoku, SMC, harmonic, chanlun)\n- Quantitative methods (factor research, ML strategy, pair trading, multi-factor)\n- Risk management (VaR/CVaR, stress testing, hedging)\n- Options (Black-Scholes, Greeks, multi-leg strategies, payoff diagrams)\n- HK/US equities (SEC filings, earnings revisions, ETF flows, ADR/H-share arbitrage)\n- Crypto trading desk (funding rates, liquidation heatmaps, stablecoin flows, token unlocks, DeFi yields)\n- Behavioral finance, trade journal diagnostics, shadow account\n- Macro analysis, credit research, sector rotation, and more\n\nUse `load_skill(name)` to access full methodology docs with code templates.\n\n## Available MCP Tools (82)\n\n| Tool | Description | API Key |\n|------|-------------|---------|\n| `list_skills` | List all 90 finance skills | None |\n| `load_skill` | Load full skill documentation | None |\n| `start_research_goal` | Create an auditable research goal | None |\n| `get_research_goal` | Read the current research goal | None |\n| `add_goal_evidence` | Attach evidence to a research goal | None |\n| `update_research_goal_status` | Update goal lifecycle status | None |\n| `backtest` | Run vectorized backtest engine | None* |\n| `paper_trade_step` | Deterministic paper-trading matching engine; never places real orders | None* |\n| `factor_analysis` | IC/IR analysis + layered backtest | None* |\n| `alpha_zoo` | Browse bundled alpha metadata and registry health | None |\n| `alpha_bench` | Benchmark one alpha or a complete zoo | None* |\n| `analyze_options` | Black-Scholes price + Greeks | None |\n| `analyze_options_payoff` | Multi-leg expiry payoff + spot/IV scenarios | None |\n| `pattern_recognition` | Detect chart patterns (H&S, double top, etc.) | None |\n| `get_market_data` | Fetch OHLCV data (auto-detect + ordered fallback across 29 sources) | None* |\n| `get_fund_flow` | Capital fund-flow (main/retail net inflow) | None* |\n| `get_dragon_tiger` | Dragon-tiger list (龙虎榜) top buyer/seller seats | None* |\n| `get_northbound_flow` | Northbound (Stock Connect) net flow | None* |\n| `get_southbound_flow` | Southbound (Stock Connect) net buy into HK (Eastmoney/HKEX) | None |\n| `get_margin_trading` | Margin trading & short-selling balances | None* |\n| `get_block_trades` | Block-trade (大宗交易) records | None* |\n| `get_shareholder_count` | Shareholder-count history per symbol | None* |\n| `get_lockup_expiry` | Restricted-share lockup release schedule | None* |\n| `get_sector_info` | Sector / industry constituents & performance | None* |\n| `get_research_reports` | Sell-side analyst research reports | None* |\n| `get_stock_news` | Market & company news headlines | None* |\n| `get_sec_filings` | SEC EDGAR filings (10-K/10-Q/8-K, etc.) | None |\n| `get_financial_statements` | Income / balance / cash-flow statements | None* |\n| `get_options_chain` | Options chain (strikes, IV, OI, Greeks) | None* |\n| `get_stock_profile` | Valuation, analyst estimates & institutional holdings (US/HK) | None |\n| `screen_market` | Market screener with fundamental/technical filters | None* |\n| `search_symbol` | Symbol / ticker search across markets | None |\n| `get_macro_series` | FRED macroeconomic series | FRED_API_KEY |\n| `get_fund_nav` | Chinese off-exchange fund NAV history | GILDATA_TOKEN |\n| `get_cn_macro_series` | China macro/industry EDB series (NL query) | GILDATA_TOKEN |\n| `get_cn_announcements` | A-share/HK/fund announcement search (NL query) | GILDATA_TOKEN |\n| `search_broker_reports` | Broker research search over the Juyuan library (NL query) | GILDATA_TOKEN |\n| `iwencai_search` | A-share natural-language research search | IWENCAI_KEY |\n| `qveris_search` | Search QVeris premium data/tool marketplace (free discovery) | QVERIS_API_KEY + paid mode |\n| `qveris_inspect` | Inspect QVeris tool schemas before executing (free) | QVERIS_API_KEY + paid mode |\n| `qveris_execute` | Execute a QVeris capability; budget-bounded, may be billable | QVERIS_API_KEY + paid mode |\n| `web_search` | Search the web via DuckDuckGo | None |\n| `read_url` | Fetch web page as Markdown | None |\n| `read_document` | Extract text from PDF/DOCX/XLSX/PPTX/images | None |\n| `write_file` | Write files (config, strategy code) | None |\n| `read_file` | Read file contents | None |\n| `read_run_artifact` | Structured run-artifact reads (rows / downsample / meta) | None |\n| `list_strategies` | Browse discoverable strategies (Alpha Zoo + SDM store) | None |\n| `query_strategies` | Evidence-gated query: regime / Sharpe / quality / cost filters | None |\n| `get_strategy_evidence` | Per-regime evidence rows for one strategy | None |\n| `adapt_strategy` | Prepare an evidence-gated child strategy for generation and its own backtest |\n| `refresh_strategy_evidence` | Rebuild the disposable strategy-evidence cache from run artifacts | None |\n| `analyze_trade_journal` | Parse broker CSV → profile + behavior diagnostics | None |\n| `extract_shadow_strategy` | Distill 3-5 if-then rules from profitable roundtrips | None |\n| `run_shadow_backtest` | Multi-market backtest + delta-PnL attribution | None* |\n| `render_shadow_report` | HTML/PDF shadow report (8 sections + charts) | None |\n| `scan_shadow_signals` | Today's symbols matching the shadow's cadence | None |\n| `list_swarm_presets` | List multi-agent team presets | None |\n| `run_swarm` | Execute a multi-agent research team | LLM key |\n| `get_swarm_status` | Poll swarm run status without blocking | None |\n| `get_run_result` | Get final report and task summaries | None |\n| `list_runs` | List recent swarm runs with metadata | None |\n| `reap_stale_runs` | Finalize stale swarm runs | None |\n| `retry_run` | Re-run a failed/stale swarm run | LLM key |\n| `trading_connections` | List selectable connector profiles | None |\n| `trading_select_connection` | Select the default connector profile | None |\n| `trading_check` | Check connector readiness | Connector app/OAuth |\n| `trading_account` | Read account summary from selected connector | Connector app/OAuth |\n| `trading_positions` | Read positions from selected connector | Connector app/OAuth |\n| `trading_orders` | Read open orders from selected connector | Connector app/OAuth |\n| `trading_quote` | Read a quote snapshot from selected connector | Connector app/OAuth |\n| `trading_history` | Read historical bars from selected connector | Connector app/OAuth |\n| `get_institutional_holdings` | SEC 13F-HR holdings by manager/ticker + quarter-over-quarter position diffs | None |\n| `etf_holdings` | ETF look-through — SEC N-PORT (US) and full-book A-share fund reports | None |\n| `prediction_market` | Event-contract search/market/history as labelled implied probability | None |\n| `research_papers` | arXiv + OpenAlex search/read with source-anchored claim extraction | None |\n| `quantlib_call` | Pure-compute finance math — 308 functions across 24 quantlib modules | None |\n| `cashflow_performance` | XIRR / MOIC / DPI / TVPI / TWR / Modified Dietz over dated cash flows | None |\n| `orderbook_depth` | Crypto L2 ladder — spread bps, depth imbalance, impact cost | None |\n| `sentiment` | Local lexicon text scoring + crypto Fear & Greed Index | None |\n| `technical_indicators` | RSI / MACD / Bollinger / SMA / EMA through the existing loaders | None* |\n| `get_fundamentals` | PIT-safe SEC fundamentals panels (filed-date anchored) | None |\n\n*A-share symbols require `TUSHARE_TOKEN`. HK/US/Canada/crypto are free. Trading connector rows use the selected connector profile, e.g. IBKR local TWS/Gateway or Robinhood MCP OAuth.\n\n## Quick Start\n\n```bash\npip install vibe-trading-ai\n```\n\nThat's it — no API keys needed for HK/US/Canada/crypto markets. Start using `backtest`, `get_market_data`, `analyze_options`, `analyze_trade_journal`, `extract_shadow_strategy`, `web_search`, the **Alpha Zoo** (`vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025`), and all 90 skills immediately.\n\n## Loading Tools from External MCP Servers\n\nThe built-in agent can load tools from your own external MCP servers in addition to its local toolset.\n\n> **Note:** This is the *MCP client* path — the opposite of the MCP plugin listed above. The plugin above makes Vibe-Trading's tools available to your agents. This section lets Vibe-Trading's own agent call tools from *your* servers.\n\n### Setup\n\nCreate `~/.vibe-trading/agent.json`:\n\n```json\n{\n \"mcpServers\": {\n \"my-server\": {\n \"command\": \"uvx\",\n \"args\": [\"my-mcp-server\"],\n \"toolTimeout\": 30,\n \"enabledTools\": [\"*\"]\n }\n }\n}\n```\n\nOrdinary external MCP tools appear automatically in every `vibe-trading run` / `vibe-trading chat` call. They are injected after local tools under stable names: `mcp__`. Live-broker MCP servers are consumed through the connector-scoped `trading_*` tools instead of exposing raw `mcp__*` tools to the agent.\n\n### Official IBKR MCP read-only probe\n\nAdd Interactive Brokers' official MCP endpoint as a read-only external server:\n\n```json\n{\n \"mcpServers\": {\n \"ibkr\": {\n \"type\": \"streamableHttp\",\n \"url\": \"https://api.ibkr.com/v1/api/mcp-public\",\n \"auth\": {\n \"type\": \"oauth\",\n \"scopes\": [\"mcp.read\"],\n \"clientName\": \"Vibe-Trading\",\n \"cacheDir\": \"~/.vibe-trading/live/ibkr/oauth\"\n },\n \"enabledTools\": [\"*\"]\n }\n }\n}\n```\n\nAuthorize it with `vibe-trading connector authorize ibkr-live-official-mcp-readonly`. The wildcard is accepted\nonly for this `mcp.read` probe. Generic `trading_account` and `trading_positions`\ncalls stay disabled until IBKR publishes stable read tool names that Vibe-Trading\ncan map safely; `mcp.write` requires an explicit tool allowlist and live\norder-guard handling. If IBKR issues a pre-registered OAuth client, add\n`clientId` and `clientSecret` inside `auth`.\n\n### Official eToro Public API MCP (discovery + dev)\n\neToro ships a hosted MCP at `https://mcp.public-api.etoro.com` with live OpenAPI\nroute discovery (`get-all-routes`, `get-route-spec`) and optional execution\n(`execute-read`, `execute-write`). Use it for **API exploration and codegen** —\nproduction agent trading in Vibe-Trading goes through the built-in `etoro-*`\nconnector profiles and `trading_*` / `etoro_*` tools (mandate gate on live writes).\n\nAdd to `~/.vibe-trading/agent.json` (credentials on the connection, not in chat):\n\n```json\n{\n \"mcpServers\": {\n \"etoro-public-api\": {\n \"type\": \"streamableHttp\",\n \"url\": \"https://mcp.public-api.etoro.com\",\n \"headers\": {\n \"x-api-key\": \"YOUR_PUBLIC_API_KEY\",\n \"x-user-key\": \"YOUR_USER_KEY\"\n },\n \"enabledTools\": [\"get-all-routes\", \"get-route-spec\", \"execute-read\"]\n }\n }\n}\n```\n\nOmit `execute-write` unless you want the MCP to place trades directly (bypasses\nVibe-Trading's live mandate gate). Install skill:\n`https://mcp.public-api.etoro.com/skill`\n\n### Trading connector profiles\n\nThe public trading surface is connector-first. Choose a connector profile, then\npaper/live is just an attribute under that connector.\n\n```bash\npip install \"vibe-trading-ai[ibkr]\"\nvibe-trading connector list\nvibe-trading connector use ibkr-paper-local\nvibe-trading connector configure ibkr-paper-local --yes\nvibe-trading connector check\nvibe-trading connector account\nvibe-trading connector positions\nvibe-trading connector orders\nvibe-trading connector quote AAPL\nvibe-trading connector history AAPL --duration \"30 D\" --bar-size \"1 day\"\n```\n\nLive-runner model spending is independent of mandate trade limits. Operators\nconfigure it through Runtime / the chat runner panel, or\n`vibe-trading connector runner-settings --profile robinhood-live-mcp`\n(`--file settings.json` saves settings while stopped). Defaults are one model\ndecision every 15 minutes, 26 decisions and 1,000,000 reported tokens per UTC day,\nwith fresh conversation context per decision. A once-daily time and IANA timezone\nare optional. Dollar limits require operator-supplied input/output prices and are\nestimates, not provider billing guarantees. Usage is checked after each response;\nbudget exhaustion or missing usage activates the existing kill switch. The agent\nmust not edit this operator state or raise its own spending limits.\n\nDefault ports are TWS paper `7497`, IB Gateway paper `4002`, TWS live-readonly\n`7496`, and IB Gateway live-readonly `4001`.\n\n### Config fields\n\n| Field | Required | Default | Description |\n|-------|----------|---------|-------------|\n| `type` | stdio: no, HTTP: yes | inferred only for stdio | Transport type. Use `sse` or `streamableHttp` for URL-based servers. |\n| `command` | stdio: yes | — | Executable to launch |\n| `args` | no | `[]` | Command arguments |\n| `env` | no | `{}` | Extra env vars for the subprocess |\n| `url` | HTTP: yes | — | Remote SSE / streamable HTTP endpoint URL |\n| `headers` | no | `{}` | Extra HTTP headers for SSE / streamable HTTP servers |\n| `toolTimeout` | no | `30` | Seconds before a tool call is cancelled |\n| `enabledTools` | no | `[\"*\"]` | Allowlist of remote tool names. `[\"*\"]` enables all |\n\nFor URL-based transports, `type` is required. The agent no longer guesses between SSE and streamable HTTP from the URL suffix.\n\n### Per-session override (API)\n\n> **Security — disabled by default.** `mcpServers` defines subprocess `command`/`args`/`env` and is therefore restricted to operator-level trust. API callers **cannot** inject MCP server definitions through `POST /sessions` unless the server operator explicitly opts in.\n\nTo enable session-level MCP injection, set the environment variable on the server before starting the agent:\n\n```bash\nexport ALLOW_SESSION_MCP_SERVERS=1\n```\n\nWith the opt-in active, pass `mcpServers` inside `session.config` to extend or replace the global config for that session only:\n\n```json\n{\n \"config\": {\n \"mcpServers\": {\n \"research\": {\n \"command\": \"uvx\",\n \"args\": [\"research-mcp\"],\n \"enabledTools\": [\"search\"]\n }\n }\n }\n}\n```\n\nWithout `ALLOW_SESSION_MCP_SERVERS=1`, any `mcpServers` key in `session.config` is silently stripped before config loading. The global operator config on disk (`~/.vibe-trading/agent.json`) is always respected regardless of this flag.\n\n### v1 limits\n\n- **Transport:** stdio, SSE, and streamable HTTP.\n- **Execution:** serial only. MCP tools never enter the parallel readonly path.\n- **Surfaces:** tools only. Resources and prompts are not exposed.\n- **Swarm:** MCP tools are excluded from Swarm worker registries in v1.\n- **Hot reload:** not supported. Restart the process to pick up config changes.\n\n### Failure handling\n\n| Case | Behavior |\n|------|----------|\n| Missing config file | falls back to empty config — no MCP servers loaded |\n| Invalid config file | logs a warning and falls back to empty config |\n| Server fails to start | that server is skipped; local tools and other servers still load |\n| Tool call times out | returns a normalized error payload instead of raising |\n| Two server names collide after sanitization | deterministic hash suffix appended; operator warning emitted |\n\n\n\n## Examples\n\n**Backtest a MACD strategy on Apple:**\n> Backtest AAPL with MACD crossover strategy (fast=12, slow=26, signal=9) for 2024\n\n**Analyze my trade journal and build a Shadow Account:**\n> Call analyze_trade_journal on ~/Downloads/tonghuashun.csv, then extract_shadow_strategy with min_support=3, then run_shadow_backtest for the last year, then render_shadow_report.\n\n**Run an investment committee review:**\n> Use run_swarm with investment_committee preset to evaluate NVDA. Variables: target=NVDA.US, market=US\n\n**Factor analysis on CSI 300:**\n> Run factor_analysis on CSI 300 stocks using pe_ttm factor from 2023 to 2024\n\n**Options analysis:**\n> Use analyze_options: spot=100, strike=105, 90 days, vol=25%, rate=3%\n\n**Multi-leg options payoff:**\n> Use analyze_options_payoff for a 95/105 bull call spread at spot 100 with 30 days remaining: long one 95 call at premium 8, short one 105 call at premium 3, multiplier 100, commission rate 0.001.\n\n\n---\nname: adr-hshare\ndescription: ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.\ncategory: flow\n---\n# ADR / H-Share / A-Share Cross-Listing Analysis\n\n## Overview\n\nMany Chinese companies are listed on multiple exchanges — A-shares in Shanghai/Shenzhen, H-shares in Hong Kong, and ADRs in the US. Pricing gaps between these listings create arbitrage opportunities and reveal market-specific sentiment differences. This skill provides frameworks for analyzing cross-listing premiums, identifying arbitrage signals, and assessing delisting risk for US-listed Chinese ADRs.\n\n## Core Concepts\n\n### 1. Cross-Listing Structures\n\n| Structure | Description | Examples |\n|-----------|-------------|---------|\n| A + H dual-listed | Same company listed on both A-share and HK exchange | PetroChina (601857.SH / 0857.HK), ICBC (601398.SH / 1398.HK) |\n| H + ADR dual-listed | HK-listed with US ADR | Alibaba (9988.HK / BABA), JD.com (9618.HK / JD) |\n| A + H + ADR triple-listed | All three markets | China Life (601628.SH / 2628.HK / LFC) |\n| HK primary + US secondary | Primary listing in HK, secondary ADR | Tencent (0700.HK / TCEHY OTC) |\n| US primary → HK secondary | Originally US, added HK listing | Alibaba (BABA → 9988.HK), Baidu (BIDU → 9888.HK) |\n\n### 2. AH Premium Analysis\n\n**AH Premium = (A-share price / H-share price in CNY terms - 1) × 100%**\n\n```python\ndef calculate_ah_premium(a_price_cny, h_price_hkd, usdcny, usdhkd):\n \"\"\"Calculate AH premium for a dual-listed stock.\"\"\"\n h_price_cny = h_price_hkd * (usdcny / usdhkd) # Convert HKD to CNY\n ah_premium = (a_price_cny / h_price_cny - 1) * 100\n return ah_premium\n\n# Example: PetroChina\n# A-share: 8.50 CNY, H-share: 6.20 HKD\n# USDCNY: 7.25, USDHKD: 7.82\n# H in CNY: 6.20 * (7.25/7.82) = 5.75 CNY\n# AH Premium: (8.50/5.75 - 1) * 100 = 47.8%\n```\n\n**AH Premium signal interpretation:**\n\n| Premium Level | Interpretation | Action |\n|--------------|----------------|--------|\n| >50% | Extreme A-share premium; A-share speculative bubble or H-share extreme undervaluation | Strong: buy H, sell/avoid A |\n| 30-50% | Elevated premium; normal for high-retail-participation names | Moderate: favor H if fundamentals same |\n| 10-30% | Normal range for most AH pairs | Neutral; no strong arbitrage signal |\n| 0-10% | Compressed premium; A-shares relatively cheap | Unusual; investigate catalyst |\n| <0% | H-share premium over A-share | Very rare; usually near-term event-driven |\n\n**Structural drivers of AH premium:**\n1. **Liquidity premium**: A-shares have much higher retail participation and turnover → liquidity premium\n2. **Access premium**: A-shares were historically hard for foreigners to access → scarcity premium\n3. **Currency expectations**: CNY depreciation expectations widen the premium\n4. **Regulatory arbitrage**: different trading rules (T+1 in A-shares vs T+0 in HK)\n5. **Investor composition**: A-share retail speculative premium vs HK institutional valuation discipline\n\n### 3. ADR Premium/Discount Analysis\n\n**ADR premium = (ADR price in USD / HK equivalent in USD - 1) × 100%**\n\n```python\ndef calculate_adr_premium(adr_price_usd, hk_price_hkd, adr_ratio, usdhkd):\n \"\"\"\n Calculate ADR premium over HK listing.\n adr_ratio: number of HK shares per 1 ADR (e.g., BABA: 1 ADR = 8 HK shares)\n \"\"\"\n hk_equivalent_usd = (hk_price_hkd * adr_ratio) / usdhkd\n premium = (adr_price_usd / hk_equivalent_usd - 1) * 100\n return premium\n\n# Example: Alibaba\n# BABA ADR: $85.00, 9988.HK: HKD 82.50\n# ADR ratio: 1 ADR = 8 HK shares\n# HK equivalent: (82.50 * 8) / 7.82 = $84.40\n# ADR premium: (85.00/84.40 - 1) * 100 = 0.71%\n```\n\n**Key ADR conversion ratios:**\n\n| Company | ADR Ticker | HK Ticker | ADR Ratio (HK:ADR) | ADR Exchange |\n|---------|-----------|-----------|---------------------|--------------|\n| Alibaba | BABA | 9988.HK | 8:1 | NYSE |\n| JD.com | JD | 9618.HK | 2:1 | NASDAQ |\n| Baidu | BIDU | 9888.HK | 8:1 | NASDAQ |\n| Bilibili | BILI | 9626.HK | 1:1 | NASDAQ |\n| NIO | NIO | 9866.HK | 1:1 | NYSE |\n| XPeng | XPEV | 9868.HK | 2:1 | NYSE |\n| Li Auto | LI | 2015.HK | 2:1 | NASDAQ |\n| NetEase | NTES | 9999.HK | 5:1 | NASDAQ |\n| Trip.com | TCOM | 9961.HK | 1:1 | NASDAQ |\n| Pinduoduo | PDD | N/A (US-only) | N/A | NASDAQ |\n\n**ADR premium drivers:**\n- US trading hours sentiment (earnings releases, macro data during US hours)\n- US-specific regulatory events (SEC, PCAOB audits)\n- Liquidity premium (ADR often more liquid for global funds)\n- Time zone gap: ADR closes at HK's open → overnight gap creates premium/discount\n\n### 4. Delisting Risk Assessment\n\n**HFCAA (Holding Foreign Companies Accountable Act) framework:**\n\nSince 2022, PCAOB gained access to audit workpapers of Chinese companies. Key risks:\n\n| Risk Level | Criteria | Impact |\n|------------|----------|--------|\n| Low | PCAOB inspection completed, no issues | ADR status stable |\n| Medium | PCAOB inspection completed, deficiencies noted | Monitor for resolution |\n| High | PCAOB access revoked or restricted | 3-year delisting countdown activated |\n| Critical | On SEC \"identified issuer\" list for 3 consecutive years | Forced delisting |\n\n**Delisting risk indicators:**\n```python\ndelisting_risk_factors = {\n \"pcaob_status\": \"inspected\", # inspected / pending / blocked\n \"sec_identified_years\": 0, # 0, 1, 2, or 3 (3 = delist)\n \"has_hk_listing\": True, # Backup listing reduces impact\n \"hk_listing_type\": \"primary\", # primary (can be in Connect) vs secondary\n \"vie_structure\": True, # Variable Interest Entity adds legal risk\n \"state_owned\": False, # SOE status adds geopolitical risk\n}\n\n# Companies with HK primary listing (BABA, JD, BIDU, NTES, etc.) have\n# a safety net if US delisting occurs → fungible conversion ADR → HK shares\n# Companies with US-only listing (PDD until HK listing) face higher risk\n```\n\n### 5. Cross-Listing Arbitrage Strategies\n\n**Strategy 1: AH Premium Mean-Reversion**\n```python\n# When AH premium for a specific stock diverges significantly from its historical average\nah_premium_current = 45 # current premium\nah_premium_mean_12m = 35 # 12-month average\nah_premium_std = 8 # standard deviation\n\nz_score = (ah_premium_current - ah_premium_mean_12m) / ah_premium_std\n\nif z_score > 2.0:\n signal = \"fade_premium\" # A-share overvalued vs H; buy H, avoid A\nelif z_score < -2.0:\n signal = \"buy_premium\" # A-share undervalued vs H; buy A, avoid H\nelse:\n signal = \"neutral\"\n```\n\n**Strategy 2: ADR-HK Intraday Arbitrage**\n- During overlapping trading hours (HK morning = US pre-market via ADR), price gaps can appear\n- Professional arbitrageurs use ADR↔HK fungible conversion to capture these gaps\n- For research purposes: tracking ADR premium trend indicates which market is leading sentiment\n\n**Strategy 3: Event-Driven Cross-Listing**\n- New HK listing announcement (US→HK): ADR typically dips 2-5% on dilution fear, then recovers\n- MSCI / FTSE index inclusion of HK listing: triggers passive fund buying in HK\n- Stock Connect inclusion (HK primary listing eligible): triggers mainland institutional buying\n\n## Data Access\n\n```python\nimport yfinance as yf\n\n# Fetch ADR and HK prices simultaneously\nbaba_adr = yf.download(\"BABA\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\nbaba_hk = yf.download(\"9988.HK\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\n\n# For A+H pairs\npetrochina_a = yf.download(\"601857.SS\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\npetrochina_h = yf.download(\"0857.HK\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False)\n\n# FX rates for premium calculation\ncny = yf.download(\"CNY=X\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False) # USD/CNY\nhkd = yf.download(\"HKD=X\", start=\"2025-01-01\", end=\"2026-03-30\", progress=False) # USD/HKD\n\n# AH Premium Index (HSAHP)\n# Not directly on yfinance; use Hang Seng website or Tushare\n```\n\n## Output Format\n\n```\n## Cross-Listing Analysis — [Company Name]\n\n### Listing Structure\n- **A-share**: [code] @ [price CNY]\n- **H-share**: [code] @ [price HKD]\n- **ADR**: [ticker] @ [price USD] (ratio: X HK shares = 1 ADR)\n\n### Premium/Discount\n- **AH Premium**: [X%] (12m avg: X%, z-score: X.X)\n- **ADR-HK Premium**: [X%] (5d avg: X%)\n- **Direction**: [AH premium widening / narrowing / stable]\n\n### Valuation Comparison\n| Metric | A-share | H-share | ADR |\n|--------|---------|---------|-----|\n| PE (TTM) | XX.X | XX.X | XX.X |\n| PB | X.X | X.X | X.X |\n| Dividend yield | X.X% | X.X% | X.X% |\n\n### Delisting Risk (ADR)\n- **PCAOB status**: [inspected / pending]\n- **SEC identified years**: [0/1/2/3]\n- **HK backup**: [yes-primary / yes-secondary / no]\n- **Risk level**: [low / medium / high / critical]\n\n### Arbitrage Signal\n- **AH premium z-score**: [X.X] → [fade premium / neutral / buy premium]\n- **Best market to buy**: [A / H / ADR] — rationale\n- **Catalyst**: [index inclusion, Connect eligibility, earnings]\n\n### Investment Implication\n- **Preferred listing**: [H-share / ADR / A-share] for new position\n- **Risk**: [delisting, FX, regulatory, liquidity]\n```\n\n## Notes\n\n- AH premium arbitrage is not freely executable: A-shares and H-shares are NOT fungible (no direct conversion), so true arbitrage requires separate capital pools\n- ADR↔HK conversion IS possible for most dual-listed names (via depositary bank), but takes 2-3 business days and involves fees\n- Currency risk (CNY, HKD, USD) is a major driver of cross-listing premiums; always hedge or account for FX when comparing\n- VIE (Variable Interest Entity) structure adds a layer of legal risk for many Chinese ADRs; this is a structural risk, not a trading signal\n- Stock Connect eligibility requirements mean not all HK-listed Chinese companies are accessible to mainland investors\n- This framework is for research purposes only and does not constitute investment advice\n\n\n---\nname: akshare\ncategory: data-source\ndescription: AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance.\n---\n\n## Overview\n\nAKShare is a completely free, open-source Python financial data library. No registration or API key required. It aggregates data from public sources (Sina, East Money, etc.) covering Chinese and global markets.\n\n- GitHub: https://github.com/akfamily/akshare (18k+ stars)\n- Install: `pip install akshare`\n\n## Quick Start\n\n```python\nimport akshare as ak\n\n# A-share daily OHLCV (前复权)\ndf = ak.stock_zh_a_hist(symbol=\"000001\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n\n# US stock daily\ndf = ak.stock_us_hist(symbol=\"105.AAPL\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n\n# HK stock daily\ndf = ak.stock_hk_hist(symbol=\"00700\", period=\"daily\",\n start_date=\"20240101\", end_date=\"20260101\", adjust=\"qfq\")\n```\n\n## Top 10 High-Frequency Interfaces\n\n### A-shares\n\n| Function | Description | Key Params |\n|----------|-------------|------------|\n| `stock_zh_a_hist()` | A-share OHLCV | symbol, period, start_date, end_date, adjust |\n| `stock_zh_a_spot_em()` | Real-time A-share quotes | (none) |\n| `stock_individual_info_em()` | Stock basic info | symbol |\n| `stock_zh_a_hist_min_em()` | Intraday bars | symbol, period(1/5/15/30/60) |\n\n### US / HK\n\n| Function | Description | Key Params |\n|----------|-------------|------------|\n| `stock_us_hist()` | US stock OHLCV | symbol (e.g. \"105.AAPL\"), period, start_date, end_date |\n| `stock_hk_hist()` | HK stock OHLCV | symbol (e.g. \"00700\"), period, start_date, end_date |\n\n### Macro / Forex / Futures\n\n| Function | Description |\n|----------|-------------|\n| `macro_china_gdp()` | China GDP data |\n| `macro_china_cpi()` | China CPI data |\n| `futures_main_sina()` | Futures main contract quotes |\n| `currency_boc_sina()` | BOC forex rates |\n\n## Column Names\n\nAKShare returns Chinese column names by default:\n\n| Chinese | English | Description |\n|---------|---------|-------------|\n| 日期 | date | Trade date |\n| 开盘 | open | Open price |\n| 最高 | high | High price |\n| 最低 | low | Low price |\n| 收盘 | close | Close price |\n| 成交量 | volume | Volume |\n| 成交额 | amount | Turnover |\n| 涨跌幅 | pct_change | % change |\n| 换手率 | turnover_rate | Turnover rate |\n\n## Date Format\n\n- Input: `YYYYMMDD` string (e.g. `\"20240101\"`)\n- Output: `日期` column as string, convert with `pd.to_datetime()`\n\n## Symbol Format\n\n- A-shares: pure digits `\"000001\"` (no .SZ suffix)\n- US stocks: `\"105.AAPL\"` (NASDAQ prefix 105), `\"106.BABA\"` (NYSE prefix 106)\n- HK stocks: `\"00700\"` (5-digit zero-padded)\n\n## Built-in Loader\n\nThe project has a built-in AKShare DataLoader at `backtest/loaders/akshare_loader.py`. When backtesting, the runner automatically falls back to AKShare when tushare/yfinance are unavailable.\n\n## Reference Docs\n\nFor less common interfaces, see the `references/` subdirectory or the official docs at https://akshare.akfamily.xyz/\n\n\n---\nname: alpha-zoo\ndescription: Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks \"which alphas exist\", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.\ncategory: research\n---\n# Alpha Zoo\n\n## Purpose\n\nWhen the user asks about prebuilt cross-sectional alphas — Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart — or wants to bench a whole zoo on an investable universe (CSI 300, S&P 500, BTC-USDT, ...), this skill orients you. The zoo is the curated library; the bench is the evaluator.\n\n## Tools Available\n\n| Tool | When to use |\n|------|------|\n| `alpha_zoo` | Browse the library. `action=list_alphas` to enumerate (filterable by zoo / theme / universe), `action=get_alpha` for one alpha's metadata, `action=health` for registry load status. |\n| `alpha_bench` | Run IC / IR on one alpha or a whole zoo over a universe + period. Emits an HTML report. |\n| `factor_analysis` | Ad-hoc factor evaluation from a user-supplied factor CSV + return CSV. Use this when the user has their own factor (not in the zoo). |\n\n## Decision Tree\n\n- \"list all momentum alphas\" → `alpha_zoo` with `action=list_alphas, theme=momentum`.\n- \"show me gtja191_alpha_001\" → `alpha_zoo` with `action=get_alpha, alpha_id=gtja191_alpha_001`.\n- \"bench all of GTJA 191 on CSI 300 from 2020 to 2024\" → `alpha_bench` with `zoo=gtja191, universe=csi300, period=2020-2024`.\n- \"is the registry healthy\" → `alpha_zoo` with `action=health` — surfaces `loaded`, `failed`, and per-error reasons.\n- User uploads `my_factor.csv` → `factor_analysis` (zoo tools are for prebuilt alphas only).\n\n## Zoo Inventory\n\n| Zoo | Description | Approx. count |\n|------|------|------|\n| `kakushadze101` | Formulaic alphas from Kakushadze's 2015 paper. Mix of momentum, reversal, volume, and microstructure. | ~101 |\n| `gtja191` | Guotai Junan 191 alphas — A-share focused cross-sectional factors. | ~191 |\n| `qlib158` | Microsoft Qlib's 158 alpha factors — features tuned for ML pipelines. | ~158 |\n| `classical` | Fama-French 3/5-factor + Carhart momentum. | <10 |\n\nCounts are nominal; check `alpha_zoo action=health` for the live count currently loaded.\n\n## Constraints\n\n- **No per-stock per-date factor values are surfaced to the agent.** IC results are aggregate stats (mean / std / IR / positive-ratio); the HTML report shows top-N by IR plus formulas, never the underlying panel.\n- **Lookahead is banned in the operator set.** `delta(df, d)` requires `d >= 1`; the negative-shift `Ref(df, -n)` form does not exist. See `docs/alpha-zoo/spec.md` for the full operator catalogue.\n- **Universe loaders may not be wired for every market yet.** When `alpha_bench` returns `universe loader for X not yet implemented`, that's the W2 scaffold — the universe is recognised but the data pull lands in W4.\n- **Do not expose absolute filesystem paths in agent output.** The bench tool writes to `~/.vibe-trading/reports/` by default; refer to it by that shorthand, not by the resolved absolute path.\n- **`alpha_zoo` is read-only.** `alpha_bench` writes a single HTML file per run — no scratch state elsewhere.\n\n## Common Pitfalls\n\n- Filter mismatch on `list_alphas`: theme / universe must match the alpha's declared metadata exactly (e.g. `equity_cn`, not `cn` or `china`).\n- Calling `alpha_bench` with both `alpha_id` and `zoo` set — they are mutually exclusive; pick one.\n- Empty registry (`loaded=0`) means no zoo modules are populated yet; treat it as \"zoos pending W3 porting\" rather than a bug.\n\n## Reference\n\n- Operator catalogue: `docs/alpha-zoo/spec.md`\n- Registry contract: `src/factors/registry.py` (frozen; do not modify)\n- IC / layered NAV math: `src/factors/factor_analysis_core.py`\n\n\n---\nname: ashare-pre-st-filter\ndescription: A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报，预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示，并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股，不预测财务造假。\ncategory: risk-analysis\n---\n\n> **依赖**：本 skill 必须配合 [tushare skill](../tushare/SKILL.md) 使用，缺数据时回退到 [akshare skill](../akshare/SKILL.md)。\n\n# A 股 ST/*ST 风险预测\n\n## 适用范围\n\n- 仅 A 股**主板 / 创业板 / 科创板**。港股、美股、加密、商品、**北交所**（`8xxxxx.BJ` 监管规则与本 skill 阈值不一致）均不适用，本 skill 直接拒绝。\n- 仅做\"下一财年是否会被 ST/*ST\"的前瞻预测，不做财务造假预测。\n- 输出双轴结果：**触线风险等级** + **预测可信度**。\n\n## 触发条件\n\n用户提问形如：\n- \"分析 000729.SZ 的 ST 风险\"\n- \"600xxx 下个年报会不会被 ST\"\n- \"帮我看一下 300xxx 是否有退市风险\"\n\n收到此类请求时，按下面\"分析流程\"执行；其他场景不要主动启用本 skill。\n\n## 数据获取规范（tushare 优先 → akshare 兜底）\n\n**铁律**：\n\n1. **所有财务/基本面/分红/审计/ST 状态数据，必须先调用 [tushare skill](../tushare/SKILL.md)**，按下表指定的接口名、`ts_code`、`period` 拉取。\n2. 当 tushare 接口因积分不足、token 缺失或返回空时，**回退到 [akshare skill](../akshare/SKILL.md)** 对应函数。\n3. **akshare 兜底数据必须在最终输出\"备注\"中显式标注**：`数据源：akshare（非官方聚合，不保证完整性，建议核对原始公告）`。**不允许只用 akshare 给出\"高可信度\"预测**——akshare 兜底的红线项可信度强制降一档（高→中高，中高→中，中→低）。\n4. 监管处罚（E2）走本 skill 自带的 [scripts/fetch_sina_penalties.py](scripts/fetch_sina_penalties.py)，不经过 tushare/akshare。\n\n### 数据需求映射表\n\n| 数据需求 | tushare 接口（首选） | 抓取参数 / 时间窗口 | akshare 兜底 | akshare 注意事项 |\n|---------|---------------------|-------------------|-------------|----------------|\n| 当前 ST 状态 | `stock_st` | 最近一个交易日 | `stock_zh_a_st_em()` | 仅返回当前 ST/退市整理板列表，历史不可查 |\n| 历史改名 | `namechange` | `ts_code` 全历史 | `stock_zh_a_new_em()`（仅新股，无改名） | akshare 无完整改名接口，必要时跳过 |\n| 股票基础信息（板块判断） | `stock_basic` | `list_status='L'` | `stock_individual_info_em(symbol)` | 字段名不同，需自行映射 |\n| 业绩预告 | `forecast` | `ts_code` + 最近 2 个 period（`{Y}0630`/`{Y}0930`/`{Y}1231`） | `stock_yjyg_em(date='YYYYMMDD')` | 按报告期日期反查，需先按 period 拉全市场再筛 ts_code |\n| 业绩快报 | `express` | 同上，最近 2 个 period | `stock_yjbb_em(date='YYYYMMDD')` | 同上，全市场快照需筛 |\n| 利润表 | `income` | `ts_code` + 最近 5 个 period（过去 4 年年报 `{Y}1231` + 最新中报/三季报） | `stock_financial_report_sina(stock='sh600000', symbol='利润表')` | 单位不一致（万元 vs 元），需做归一；季报缺失常见 |\n| 资产负债表 | `balancesheet` | 同上 | `stock_financial_report_sina(stock='sh600000', symbol='资产负债表')` | 同上 |\n| 现金流量表 | `cashflow` | 同上 | `stock_financial_report_sina(stock='sh600000', symbol='现金流量表')` | 同上 |\n| 财务指标（扣非净利润等） | `fina_indicator` | `ts_code` + 最近 5 个 period | `stock_financial_analysis_indicator(symbol='600000')` | 字段中文，扣非字段名为\"扣除非经常性损益后的净利润\" |\n| 审计意见 | `fina_audit` | `ts_code` + 最近 2 个年报 period | **akshare 无对应接口** | 缺失时在输出中标注\"审计意见数据缺失\"，**不得跳过**——必须提示用户去官网查 |\n| 分红 | `dividend` | `ts_code` + 近 5 年 `div_proc='实施'` | `stock_history_dividend_detail(symbol='600000', indicator='分红')` | 字段中文，需把\"派息\"金额×总股本得到现金分红总额 |\n| 日线行情（1 元退市预警） | `daily` | `ts_code` + 最近 30 个交易日 | `stock_zh_a_hist(symbol='600000', period='daily', adjust='')` | 必须用**不复权**价格判断 1 元退市线 |\n| 每日指标（市值） | `daily_basic` | `ts_code` + 最近一个交易日 | `stock_zh_a_spot_em()` 全市场快照筛 `代码=600000` | 字段名为\"总市值\"（**单位：元**，东方财富 push2 原始口径，akshare 不做缩放），换算成亿元需 `/1e8`。⚠️ 不要与 tushare `daily_basic.total_mv`（万元）混淆——本列是 akshare 兜底口径 |\n| 财报披露日期 | `disclosure_date` | `ts_code` + 当前年度 | **akshare 无对应接口** | 缺失时按经验估算（4/30 年报、8/31 中报、10/31 三季报） |\n| 监管处罚 | — | — | **走本 skill 自带 sina 脚本** | 见 E2 章节 |\n\n### 时间窗口口径统一\n\n按数据类型分别处理，**禁止用单一 60 天经验值统一对齐**：\n\n- **财务报表（income / balancesheet / cashflow / fina_indicator）**：以 `disclosure_date` 实际公告日为准；该接口不可用时按法定截止日（年报 4/30、中报 8/31、三季报 10/31、一季报 4/30）做兜底。在公告日 +1 天起方可视该 period 为\"最新可得\"。\n- **\"过去 N 年年报\"**：一律 `period = {Y}1231`，不要用日历年起止。\n- **业绩预告 forecast / 业绩快报 express**：只取 `ann_date ≥ 当前年度 1 月 1 日` 的记录；按 `ann_date` 倒序取最新一条，老 period 的预告对预测无效。\n- **dividend**：按 `(end_date, ann_date, cash_div_tax)` 三元组**强制去重**——tushare 同一笔分红会出现 3-4 行（预案/股东大会通过/实施），直接累加会三倍误算。\n\n## 第一步：当前状态核查（M0）\n\n**判断公司当前是否已被风险警示，并据此调整分析方向（不一刀切结束）**：\n\n调用顺序：\n1. 优先调用 `stock_st`（tushare skill，3000 积分），按当前最近交易日查询；记录 `type` / `type_name`。\n2. 如积分不足，回退到 `namechange`（免费）：拉取 `ts_code` 全部历史改名记录，取按 `start_date` 排序的最新一条；判断 `name` 前缀。\n3. 兜底用 `stock_basic.name` 做名称确认。\n\n**分支处理**：\n\n| 当前状态 | 分析方向 | 是否继续 |\n|---------|---------|---------|\n| 正常股票 | 预测下一财年是否会被 ST/*ST | **是**（默认流程） |\n| 已 ST（普通） | 改为预测：是否会进一步转 *ST 或退市；R1-R4 阈值需更严格（亏损链门槛降一年） | **是** |\n| 已 *ST | 改为预测：是否会被强制退市；重点看 R1 营收 + R2 净资产 + E1 审计意见 | **是** |\n| 已退市整理期 / 已摘牌 | 直接结束 | **否** |\n\n## 第二步：板块阈值表（决定红线）\n\n**先用 `ts_code` 前缀判断板块，再套用对应阈值。不要混用。**\n\n| 板块 | 代码识别 | 营收红线 | 市值退市线 | 三年累计分红阈值 |\n|------|---------|---------|-----------|----------------|\n| 主板 | `60xxxx.SH` / `00xxxx.SZ`（除 30 / 688 开头外） | 净利润为负 且 营收 < 3 亿 | 市值 < 5 亿 | 累计 < 5000 万 且 < 年均净利润 30% |\n| 创业板 | `30xxxx.SZ` | 净利润为负 且 营收 < 1 亿 | 市值 < 3 亿 | 累计 < 3000 万 且 < 年均净利润 30%（研发豁免：研发投入占营收 ≥ 5% 或近三年累计 ≥ 6000 万） |\n| 科创板 | `688xxx.SH` | 净利润为负 且 营收 < 1 亿 | 市值 < 3 亿 | 累计 < 3000 万 且 < 年均净利润 30%（研发豁免：研发投入占营收 ≥ 5% 或近三年累计 ≥ 6000 万） |\n\n## 第三步：预测时点选择\n\n按\"最新可得证据\"原则确定基准时点，从下到上择优：\n\n| 基准时点 | 数据接口 | 可信度基线 | 适用季节 |\n|---------|---------|-----------|---------|\n| **业绩预告 forecast** | `forecast` | **高** | 1 月底 / 7 月中旬 / 10 月底高发期 |\n| **业绩快报 express** | `express` | **高** | 1-4 月年报前夕 |\n| **三季报** | `income/balancesheet/cashflow` (`period=YYYYMMDD`，9 月末) | **中高** | 10 月底披露后到次年年报前 |\n| **中报** | 同上，6 月末 period | **中** | 8 月底披露后到三季报前 |\n| **去年年报** | 同上，12 月末 period | **低** | 仅作为基准对照 |\n\n**证据优先级**（同一指标多个来源时，高的覆盖低的）：\n\n```\nforecast / express > 三季报 > 中报 > 去年年报 > 经验外推\n```\n\n输出时**必须在每个预测项明示采用了哪一级证据**。如果存在 forecast 给出的全年净利润区间，**禁止再用三季报机械年化覆盖**。\n\n## 第四步：四项可预测红线\n\n### R1 营收 + 净利润红线\n\n**目标**：预测全年营收、归母净利润和**扣非净利润**，按监管口径\"扣非前后孰低者\"判定。\n\n**口径关键**：监管原文是 `min(n_income, profit_dedt) < 0` 且 `revenue < 板块阈值` —— **必须同时预测扣非净利润**，不能只用归母。仅看归母会漏掉\"归母为正但扣非为负\"的造壳公司。\n\n**预测方法（按基准时点选用，至少一种）**：\n\n- **forecast 直采法**：若 `forecast` 给出 `net_profit_min` / `net_profit_max`，直接取区间作为全年归母预测；扣非用过去两年扣非率（`profit_dedt / n_income` 中位数）折算；可信度=高。营收红线仍需结合最新报表外推。\n- **express 直采法**：若 `express` 已披露，`revenue` / `n_income` 即为全年快报值；扣非同样按过去两年率折算（express 一般无扣非字段）；可信度=高。\n- **Q4 单季回补法**（三季报基准）：\n - `Q1Q3_revenue` = 三季报营业收入\n - `Q4_revenue_est` = 过去两年 Q4 营收占全年比例的中位数 × 当前年化预估值\n - `revenue_full_year_est = Q1Q3_revenue + Q4_revenue_est`\n - `n_income` / `profit_dedt` 同法处理\n - 可信度=中高\n- **下半年情景外推法**（中报基准，强季节性公司必须用）：\n - 取过去两年 H2 营收占全年比例均值，给出悲观/基准/乐观三情景\n - `revenue_full_year_est = H1_revenue / H1_share_avg`\n - 可信度=中\n- **同比延续法**（中报基准，弱季节性公司可用）：\n - `revenue_full_year_est = H1_revenue × (last_year_full_revenue / last_year_H1_revenue)`\n - 可信度=中\n\n**禁止**：H1 直接乘 2、Q1-Q3 直接乘 4/3。任何机械年化**强制降到\"低\"可信度**并在输出中注明。\n\n**触线判定**（口径：`worst_profit = min(n_income_est, profit_dedt_est)`）：\n- 高风险：`worst_profit < 0` 且 `revenue_est < 板块阈值`\n- 中风险：`worst_profit < 0` 但营收达标；或营收逼近阈值（< 阈值 × 1.2）\n- 低风险：上述均不满足\n\n> **系数说明**：R1 中风险的 1.2 是预测项预警 buffer（预测本身有误差，需窄 buffer）；R4 高风险的 1.5 是双年联动判定 buffer（两年都亏且营收接近阈值才是真退市信号，需宽 buffer）。两个系数不同是有意为之，**不要错误地统一**。\n\n### R2 年末净资产红线\n\n**目标**：判断年末归母股东权益是否可能转负。\n\n**方法**：\n1. 取最新一期 `balancesheet.total_hldr_eqy_exc_min_int`（最新中报或三季报），记为 `current_eq`。\n2. 计算 `eq_year_end_est = current_eq + 剩余季度净利润预测 − 当前年度已实施现金分红总额`。\n - 剩余季度净利润：中报基准用 R1 的 H2 预测，三季报基准用 R1 的 Q4 预测。\n - 已实施分红总额：见 R3 的去重 + 量纲公式 `cash_div_tax / 10 × total_share`。\n\n**触线判定**（统一相对阈值，单位：元）：\n- 高风险：`current_eq < 0` 或 `eq_year_end_est < 0`\n- 中风险：`0 ≤ eq_year_end_est < 1e8`（1 亿元）\n- 低风险：`eq_year_end_est ≥ 1e8`\n\n**注意**：商誉减值、长期股权投资减值在 Q3/Q4 集中确认，应在备注提示\"若计提大额减值，净资产可能进一步下行\"，但**不得在没有公告依据时擅自减值**。\n\n### R3 分红达标前瞻\n\n**目标**：判断\"近三年累计现金分红 < 年均净利润 30% 且 < 板块累计阈值\"是否会在下一次年度考核时同时满足（监管原文：两条件**同时**触发才警示）。\n\n**单笔分红量纲公式**（**必须用此公式，不要用 `cash_div_tax × base_share`**）：\n\n```\ncash_dividend_total = cash_div_tax / 10 × total_share\n```\n\n其中 `cash_div_tax` 来自 `dividend`（每 10 股派现金额，含税），`total_share` 来自 `daily_basic.total_share`（最新总股本，单位万股 → 需 ×1e4 换成股）或 `stock_basic`。tushare `dividend.base_share` 字段对很多公司为 None，**禁止直接使用**。\n\n**方法**：\n1. 用 `dividend` 拉取该 `ts_code` 近五年记录，按 `(end_date, ann_date, cash_div_tax)` 三元组**强制去重**——同一笔分红 tushare 会返回\"预案/股东大会通过/实施\"3-4 行。\n2. 折算系数（按 `div_proc` 状态）：`实施=1.0` / `股东大会通过=0.7` / `预案=0.5`；同一 `end_date` 取系数最高那条。\n3. 用 `income` 拉取近三年完整年度归母净利润（period=YYYY1231）。\n4. 计算考核窗口 = `[当前年-2, 当前年]`，即\"过去两年已落地分红 + 本年度预计分红\"。\n5. 本年度预计分红：\n - 若 `dividend` 已查到本年度记录：按上面折算系数计入。\n - 否则用\"过去两年平均分红率\"× R1 预测全年净利润（基准情景）做估算，可信度降一档。\n6. 与年均净利润比较：\n - 条件 A：`cumulative_div_3y < 30% × avg_n_income_3y`\n - 条件 B：`cumulative_div_3y < 板块累计阈值`\n7. 创业板 / 科创板若研发投入占比满足豁免条件，备注提示\"研发达标可能豁免\"。\n\n**触线判定**：\n- 高风险：A 和 B 同时满足 **且** 过去三年至少一年净利润为正（即\"盈利年份未达标\"——监管以盈利年为基准）。\n- 中风险：仅一个条件满足；或本年度分红尚未公告。\n- 低风险：累计分红已稳定达标；或过去三年累计净利润 ≤ 0（无分红基础，按 R1 联动）。\n\n**禁止**：本年度尚未公告分红时，不得断言\"必然不达标\"，只能给出概率。\n\n### R4 连续亏损 / 扣非亏损链\n\n**目标**：判断是否会触发\"连续两年扣非前后净利润孰低者为负\" → *ST。\n\n**口径关键**：监管原文是**连续两年**（不是三年），且 *ST 财务类强制退市的法定条件是 **\"亏损 + 营收<板块阈值\" 必须同时满足**——只看亏损链会系统性高估风险（实证：闻泰科技 2024 年亏损但营收超千亿，不会被 *ST）。\n\n**方法**：\n1. 取过去一年完整年报的 `n_income`、`fina_indicator.profit_dedt`，记 `worst_last = min(n_income, profit_dedt)`、`revenue_last`。\n2. 用 R1 已经预测好的 `worst_profit_est` 和 `revenue_full_year_est`（基准情景），对应当前年度。\n3. 检查两年的 `worst < 0` 组合，**并叠加营收联动条件**。\n\n**触线判定**（双条件，亏损链 + 营收联动）：\n- 高风险：`worst_last < 0` 且 `worst_profit_est < 0`，**且**（R1 当前年命中高风险 **或** `revenue_full_year_est < 板块阈值 × 1.5`）——即两年均亏 + 营收处于退市风险区间。\n- 中风险：(a) 连续两年均亏但营收远超阈值（≥ 阈值 × 1.5）——亏损链成立但不会触发财务类 *ST，仍可能因经营恶化转 ST；(b) 仅 `worst_profit_est < 0`（当前年预测亏损，去年盈利，未形成连续）；(c) 仅 `worst_last < 0` 但当前年预测为正且接近 0（< 板块阈值 × 0.5）。\n- 低风险：`worst_last ≥ 0` 且 `worst_profit_est ≥ 0`。\n\n**为什么要叠加营收**：A 股 *ST 财务类强制退市的法定条件是\"扣非前后净利润孰低 < 0 **且** 营收 < 板块阈值\"，二者必须同时满足。仅亏损不达营收阈值的公司（如大型周期股短期亏损）会被市场和监管视作\"周期性亏损\"而非\"持续经营存疑\"。\n\n## 第五步：三项事实/临界证据面（不参与预测可信度）\n\n### E1 审计意见（事实）\n\n调用 `fina_audit`，取最近 2 个完整年报：\n- 高风险：最新年报 `audit_result` 含\"无法表示意见\"或\"否定意见\"；**或单次出现\"保留意见\"**（实证支持：上交所近年规范类 *ST 案例多为单次保留即触发）。\n- 中风险：连续两年同一负面结论但本期已修正；或带强调事项段的无保留意见涉及持续经营。\n- 低风险：标准无保留意见。\n\n**禁止预测下一份审计意见**——审计是事后行为。\n\n**akshare 兜底缺失时的人工核查路径**（`fina_audit` 无 akshare 替代接口，必须给出可点击 URL）：\n\n| 来源 | URL 模板 | 查询方式 |\n|------|---------|---------|\n| 巨潮资讯网（公告原文，权威） | `http://www.cninfo.com.cn/new/disclosure/stock?orgId=&stockCode={code6}` | 进入页面后筛选\"定期报告 → 年度报告\"，下载 PDF 在第十节\"财务报告 → 审计报告\"查 `审计意见类型` |\n| 巨潮高级搜索（按关键词） | `http://www.cninfo.com.cn/new/fulltextSearch/full?searchkey={code6}+审计报告&sdate=&edate=&isfulltext=false&sortName=time&sortType=desc` | 直接命中含\"审计报告\"标题的公告 |\n| 上交所信息披露 | `http://www.sse.com.cn/disclosure/listedinfo/regular/?stockCode={code6}` | 仅限 60xxxx / 688xxx |\n| 深交所信息披露 | `http://www.szse.cn/disclosure/listed/notice/index.html?stock={code6}` | 仅限 00xxxx / 30xxxx |\n\n其中 `{code6}` 为 6 位股票代码（去掉 `.SH` / `.SZ` 后缀）。**强制要求**：当 `fina_audit` 返回空时，输出报告的\"备注\"必须列出上面巨潮的两条 URL 之一，便于用户人工核查；不得只写一句\"审计意见数据缺失\"了事。\n\n### E2 监管处罚（事实，双窗口：上一财年 + 过去 12 个月）\n\n**这是本 skill 的关键证据面。**\n\n**窗口规则（双窗口策略，单次抓取在内存中切片）**：\n\n- 窗口 A — `[当前年-1 的 1 月 1 日, 当前年-1 的 12 月 31 日]`（上一个完整财年）：用于 reason_normalized 单条评级。\n- 窗口 B — `[分析日 - 365 天, 分析日]`（过去 12 个月滚动）：用于**频次增强规则**。\n- 若分析时点已经在 5 月之后且最新年报已披露完毕，窗口 A 可改为 `[最新年报 end_date - 365 天, 最新年报 end_date]`。\n\n**调用方式（推荐：单次拉全量 + 内存切片）**：\n\n```bash\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --no-filter\n```\n\n返回页面全量记录后，由 LLM 在内存中按窗口 A、窗口 B 切两份。也可以用 `--start-date / --end-date` 单独抓取，但同一次分析**避免发起两次 HTTP**。\n\n输出 JSON 列表，字段：`ann_date / event_type / title / reason / reason_normalized / content / issuer / issuer_normalized / subject_normalized / target_relevance / e2_countable / source_url`。\n\n**相关性铁律**：调用脚本时必须传入 `--stock-name {股票简称}`（来自 `stock_basic.name`），必要时用 `--alias` 补充曾用名/简称。新浪页面可能收录“某个人证券账户交易列表里出现目标股票”的记录，这类记录只说明目标股票被交易过，**不是上市公司/股东/董监高处罚**。脚本会将其标为 `target_relevance=security_mention_only` 且 `e2_countable=false`，E2 频次统计必须排除。\n\n**`subject_normalized` 字段**（用于频次权重折算）：\n- `company` — 处罚主体是公司本身（默认；不含个人身份关键词时归此类）\n- `shareholder` — 控股股东 / 实控人 / 5% 以上大股东\n- `officer` — 董事 / 监事 / 高管（董事长、总经理、董秘、财务总监等）\n\n**风险等级判定**：\n\n| 命中 `reason_normalized` | 等级贡献 |\n|---|---|\n| 财务造假 / 虚假陈述 / 信息披露违规 | 高（强烈预警） |\n| 违规担保 / 占用资金 | 高（直接触及 ST 红线） |\n| 内幕交易 / 市场操纵 / 违规减持 | 中 |\n| 其他/unknown | 低（仅作背景） |\n\n**频次增强规则（治理质量预警，窗口 B）**：\n\n监管函本身反映公司治理与内控质量，密集出现是\"规范类 ST\"（保留意见、内控非标、信披重大缺陷）的强领先指标。但**处罚主体不同，治理含义不同**：股东减持违规、董监高短线交易反映的是个人合规问题，不应等同于\"公司治理失序\"。\n\n**主体加权计数**（按 `subject_normalized` 折算后再查表）：\n\n```\n加权条数 = Σ 单条权重\n 其中：subject_normalized == \"company\" → 权重 1.0\n subject_normalized == \"officer\" → 权重 0.5\n subject_normalized == \"shareholder\" → 权重 0.5\n```\n\n| 过去 12 个月监管函/问询/警示函**加权条数** | 频次等级 |\n|---|---|\n| ≥ 5.0 | **极高** |\n| 3.0 - 4.5 | 高 |\n| 2.0 - 2.5 | 中 |\n| < 2.0 | 低 |\n\n**判定来源**：仅当 `e2_countable=true` 时，脚本输出的 `event_type ∈ {警示, 问讯, 监管关注, 监管函}` 或 `issuer_normalized ∈ {上交所, 深交所, 北交所, 证监会, 地方证监局}` 才计入条数；条数按 `subject_normalized` 折算后再查表。`target_relevance=security_mention_only` 的记录必须排除。\n\n**输出要求**：必须同时给出\"原始条数\"和\"加权条数\"两个数字，便于用户审阅折算逻辑。例如：`窗口 B 命中 6 条（公司 2 条 + 股东 3 条 + 高管 1 条 → 加权 4.0 条），频次等级：高`。\n\n**E2 综合等级合成规则**（解决单条评级与频次叠加的问题）：\n\n```\nE2_单条等级 = 窗口 A 内所有 reason_normalized 单条等级的最大值\nE2_频次等级 = 窗口 B 频次表查得\nE2_综合等级 = max(E2_单条等级, E2_频次等级)\n```\n\n**叠加加成**：若同时满足 `E2_频次等级 ≥ 高` 且 `E2_单条等级 ≥ 高`（含财务造假/虚假陈述/违规担保/占用资金）→ **直升极高**。\n\n**为什么要这条规则**：纯量化红线只能捕捉\"财务类 ST\"，无法预警\"规范类 ST\"。频次规则把\"治理失序\"作为前瞻信号纳入。\n\n**注意**：处罚证据**只抬升风险等级，不影响预测可信度**——这是事实证据，不是预测项。\n\n### E3 交易类临界预警（事实）\n\n调用 `daily`（近 30 个交易日，**必须 `adjust=''` 不复权**）和 `daily_basic`（最新一日，单位：万元）：\n\n**1 元退市预警**（用 `daily.close`，**不复权**）：\n- 高风险：近 20 个交易日中收盘价 < 1 元的天数 ≥ 10\n- 中风险：≥ 1 但 < 10\n- 低风险：0 天\n\n**市值退市预警**（用 `daily_basic.total_mv`，单位万元 → ×1e4 换算成元 → /1e8 换算成亿元）：\n\n| 板块 | 高风险阈值 | 中风险阈值 |\n|---|---|---|\n| 主板 | < 5 亿 | < 5 × 1.5 = 7.5 亿 |\n| 创业板 / 科创板 | < 3 亿 | < 3 × 1.5 = 4.5 亿 |\n\n## 第六步：双轴输出模板\n\n每次分析必须按下面模板输出。**风险等级 + 可信度两个维度都要给**。\n\n```markdown\n## ST 风险预测：{name}（{ts_code}）\n\n### 当前状态\n- 是否已 ST/*ST：{是 / 否}\n- 板块：{主板 / 创业板 / 科创板}\n- 基准预测时点：{forecast / express / 三季报（period） / 中报（period）}\n\n### 量化红线预测（双轴）\n\n| 红线 | 基准情景结论 | 风险等级 | 可信度 | 主要证据 |\n|------|-------------|---------|-------|---------|\n| R1 营收+净利润 | 全年营收预测 X 亿，净利润 Y 亿 | 高/中/低 | 高/中高/中/低 | forecast/三季报/... |\n| R2 年末净资产 | 当前 X 亿，年末预计 Y 亿 | 高/中/低 | 高/中高/中/低 | balancesheet + R1 |\n| R3 分红达标 | 三年累计预计 X，对应阈值 Y | 高/中/低 | 中/低 | dividend + income |\n| R4 连续亏损链 | 过去两年 + 当前年是否均亏 | 高/中/低 | 同 R1 | income + fina_indicator |\n\n### 事实证据面（不参与预测可信度）\n\n| 证据 | 结论 | 影响 |\n|------|------|------|\n| E1 审计意见 | 最新结论 | 抬升/无影响 |\n| E2 监管处罚（窗口 A 上一财年 / 窗口 B 过去 12 个月） | A 命中 Na 条（最高单条等级：…），B 原始 Nb_raw 条 / 加权 Nb_w 条（公司 a + 股东 b + 高管 c → a×1.0+b×0.5+c×0.5），频次等级：…，E2_综合等级：… | 抬升/无影响 |\n| E3 交易类临界 | 收盘价/市值情况 | 抬升/无影响 |\n\n### 综合结论\n- **下一财年被 ST/*ST 风险等级**：{极高 / 高 / 中 / 低}\n- **预测综合可信度**：{高 / 中高 / 中 / 低}\n- **关键风险点**（最多 3 条）：...\n- **缓解信号**（如有）：...\n\n### 备注\n- 数据时点：{所用最新报告期}\n- 已忽略的不可预测项：财务造假、未来审计意见\n- 季节性提醒（如适用）：...\n```\n\n## 禁止事项\n\n1. 不预测财务造假（事前不可知）。\n2. 不预测下一份审计意见（事后才出）。\n3. 不在没有公告依据时擅自计提减值。\n4. 不混用板块阈值（主板 vs 创业板/科创板的营收/市值/分红阈值不同）。\n5. 不把机械年化（H1×2、Q1Q3×4/3）伪装成\"高可信度\"。\n6. 不在港股 / 美股 / 加密 / **北交所**（`8xxxxx.BJ`，监管规则与本 skill 阈值不一致）上启用本 skill。\n7. 不把\"违规处罚记录\"计入预测可信度（仅抬升风险等级）。\n8. 不在 R3 分红计算中使用 `cash_div_tax × base_share`（base_share 经常为 None，会得 0）；必须用 `cash_div_tax / 10 × total_share`，且按 `(end_date, ann_date, cash_div_tax)` 三元组去重。\n9. 不在 R1 触线判定中只用 `n_income`；必须用 `min(n_income, profit_dedt)`，否则会漏掉造壳公司。\n10. 不把 R4 写成\"连续三年\"；监管原文是\"连续两年\"。**且 R4 高风险必须叠加营收联动条件**——仅\"连续两年亏损\"不构成 *ST 财务类强制退市，必须同时满足\"营收 < 板块阈值 × 1.5\"或 R1 已命中高风险，否则只能给中风险。\n11. 不把 E2 监管处罚\"按主体一刀切\"计数——必须用 `subject_normalized` 加权（公司 ×1.0 / 股东 ×0.5 / 董监高 ×0.5），否则股东动荡的公司会被系统性高估。\n\n## 依赖的 tushare 接口清单\n\n| 用途 | 接口 | 备注 |\n|------|------|------|\n| 当前 ST 状态 | `stock_st` | 3000 积分；不可用时回退 `namechange` |\n| 历史改名 | `namechange` | 免费 |\n| 板块判断 | `stock_basic` | 免费 |\n| 利润表 | `income` | 2000 积分，多 period 拉取 |\n| 资产负债表 | `balancesheet` | 2000 积分 |\n| 财务指标（扣非等） | `fina_indicator` | 2000 积分 |\n| 业绩预告 | `forecast` | 2000 积分 |\n| 业绩快报 | `express` | 2000 积分 |\n| 审计意见 | `fina_audit` | 500 积分 |\n| 分红 | `dividend` | 2000 积分 |\n| 日线 | `daily` | 免费 |\n| 每日指标（市值） | `daily_basic` | 免费 |\n| 财报披露日期 | `disclosure_date` | 500 积分 |\n\n## 自带脚本\n\n- [scripts/fetch_sina_penalties.py](scripts/fetch_sina_penalties.py) — 抓取新浪财经 vGP_GetOutOfLine 处罚页，stdlib 实现，无外部依赖；reason / issuer 标准化。\n\n调用方式：\n\n```bash\n# 单次拉全量（推荐：本 skill 双窗口策略由 LLM 在内存中切片）\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --no-filter\n\n# 或指定单一窗口\npython agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py \\\n --ts-code 000729.SZ --stock-name 燕京啤酒 --start-date 2025-01-01 --end-date 2025-12-31\n```\n\n正常情况下输出 JSON 到 stdout；网络/解析失败或参数非法时返回 `{\"source\": \"unavailable\", \"error\": \"...\"}` 到 stdout 并以非零退出码结束（不抛 traceback），便于 LLM 兜底处理。\n\n## 端到端调用伪代码\n\n用于减少 LLM 调用顺序漂移，按下列骨架组织：\n\n```python\nimport os, json, subprocess\nimport tushare as ts\n\nts_code = '000729.SZ'\npro = ts.pro_api(os.environ['TUSHARE_TOKEN'])\n\n# === Step 0: M0 当前状态 ===\nbasic = pro.stock_basic(ts_code=ts_code, fields='ts_code,name,market').to_dict('records')[0]\nst_hit = pro.stock_st() # 失败则回退 namechange\nname_chg = pro.namechange(ts_code=ts_code).sort_values('start_date', ascending=False)\n\n# === Step 1: 板块判断 ===\nboard = '科创板' if ts_code.startswith('688') else '创业板' if ts_code.startswith('30') else '主板'\nthreshold_revenue = 1e8 if board != '主板' else 3e8\nthreshold_mv = 3e8 if board != '主板' else 5e8\n\n# === Step 2: 拉财务（最新 5 期 + 过去 4 年年报） ===\nincome = pro.income(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nbalance = pro.balancesheet(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nfina_ind = pro.fina_indicator(ts_code=ts_code).sort_values('end_date', ascending=False).drop_duplicates('end_date')\nforecast = pro.forecast(ts_code=ts_code).sort_values('ann_date', ascending=False)\nexpress = pro.express(ts_code=ts_code).sort_values('ann_date', ascending=False)\n\n# === Step 3: R1-R4 计算（按各章节口径，注意 min(n_income, profit_dedt)） ===\n# ... 略\n\n# === Step 4: E1 审计 ===\naudit = pro.fina_audit(ts_code=ts_code).sort_values('end_date', ascending=False).head(2)\n\n# === Step 5: E2 监管处罚（双窗口 + 主体加权）===\nfrom datetime import date, timedelta\nresult = subprocess.run([\n 'python', 'agent/src/skills/ashare-pre-st-filter/scripts/fetch_sina_penalties.py',\n '--ts-code', ts_code, '--stock-name', basic['name'], '--no-filter',\n], capture_output=True, text=True)\npenalties = json.loads(result.stdout).get('records', [])\n\n# 在内存中切双窗口（避免两次 HTTP）\nthis_year = date.today().year\nwin_a_start, win_a_end = f'{this_year-1}-01-01', f'{this_year-1}-12-31'\nwin_b_start = (date.today() - timedelta(days=365)).isoformat()\nwin_b_end = date.today().isoformat()\n\ndef _in(rec, s, e):\n d = rec.get('ann_date') or ''\n return bool(d) and s <= d <= e\n\nwin_a = [r for r in penalties if _in(r, win_a_start, win_a_end)]\nwin_b = [r for r in penalties if _in(r, win_b_start, win_b_end)]\n\n# 窗口 A：单条等级取最高\nREASON_LEVEL = {\n '财务造假': 3, '虚假陈述': 3, '信息披露违规': 3,\n '违规担保': 3, '占用资金': 3,\n '内幕交易': 2, '市场操纵': 2, '违规减持': 2,\n}\nlevels_a = [REASON_LEVEL.get(r['reason_normalized'], 1) for r in win_a]\ne2_single = max(levels_a) if levels_a else 0\n\n# 窗口 B：原始条数 + 主体加权条数\nFREQ_EVENT_TYPES = {'警示', '问讯', '监管关注', '监管函', '警示函'}\nFREQ_ISSUERS = {'上交所', '深交所', '北交所', '证监会', '地方证监局'}\nSUBJECT_WEIGHT = {'company': 1.0, 'shareholder': 0.5, 'officer': 0.5}\n\nfreq_pool = [r for r in win_b\n if r.get('e2_countable', True)\n and (r['event_type'] in FREQ_EVENT_TYPES\n or r['issuer_normalized'] in FREQ_ISSUERS)]\nnb_raw = len(freq_pool)\nnb_weighted = round(sum(SUBJECT_WEIGHT.get(r['subject_normalized'], 1.0)\n for r in freq_pool), 1)\n\n# 频次等级（基于加权条数）\nif nb_weighted >= 5.0: e2_freq = 4 # 极高\nelif nb_weighted >= 3.0: e2_freq = 3 # 高\nelif nb_weighted >= 2.0: e2_freq = 2 # 中\nelse: e2_freq = 1 # 低\n\ne2_overall = max(e2_single, e2_freq)\n# 叠加加成：频次高 + 单条高同时命中 → 直升极高\nif e2_freq >= 3 and e2_single >= 3:\n e2_overall = 4\n\n# === Step 6: E3 交易临界 ===\ndaily = pro.daily(ts_code=ts_code).sort_values('trade_date', ascending=False).head(30)\ndaily_basic = pro.daily_basic(ts_code=ts_code).sort_values('trade_date', ascending=False).head(1).iloc[0]\ntotal_mv_yi = daily_basic['total_mv'] * 1e4 / 1e8 # 万元 → 亿元\n\n# === Step 7: 双轴合成输出 ===\n# 风险等级 = max(R1, R2, R3, R4, E1, E2_综合, E3)\n# 可信度 = 加权平均(R1, R2, R3, R4 各项可信度)，akshare 兜底项降一档\n```\n\n\n---\nname: asset-allocation\ndescription: Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.\ncategory: asset-class\n---\n\n# Asset Allocation and Portfolio Optimization\n\n## Overview\n\nFrom asset allocation theory to practical implementation, this skill covers classical frameworks (MPT, BL, risk budgeting, all-weather) and the usage of the four optimizers built into this system. The output can be written directly into `config.json`.\n\n## Asset Allocation Theory\n\n### 1. Modern Portfolio Theory (MPT, Markowitz)\n\n**Core idea**: maximize expected return for a given level of risk (the efficient frontier).\n\n```\nOptimization problem:\nmin w'Σw (portfolio variance)\ns.t. w'μ = target_return\n Σw = 1\n w ≥ 0 (no shorting)\n```\n\n| Advantages | Disadvantages |\n|------|------|\n| Mathematically rigorous | Extremely sensitive to inputs (garbage in, garbage out) |\n| Efficient frontier is visualizable | Concentrated-allocation problem (often produces extreme weights) |\n| Foundational framework | Assumes normality and ignores fat tails |\n\n**Practical advice**: do not use raw MPT directly. Add constraints (upper/lower bounds, sector limits) or use a regularized version.\n\n### 2. Black-Litterman Model\n\n**Core idea**: start from market equilibrium and incorporate investor views.\n\n```\nSteps:\n1. Reverse-imply market equilibrium returns: π = δΣw_mkt\n2. Build the view matrices: P (selection matrix), Q (view returns), Ω (view uncertainty)\n3. Blend the posterior: μ_BL = [(τΣ)^-1 + P'Ω^-1 P]^-1 [(τΣ)^-1 π + P'Ω^-1 Q]\n4. Run Markowitz optimization using posterior μ_BL\n```\n\n**Example views**:\n- Absolute view: \"China A-shares will return 10% over the next year\" → `P=[1,0,0], Q=[0.10]`\n- Relative view: \"China A-shares will outperform US equities by 5%\" → `P=[1,-1,0], Q=[0.05]`\n\n**Parameter guidance**:\n- `τ` (uncertainty scaling): `0.025-0.05`\n- `Ω`: set according to view confidence, where higher confidence = smaller variance\n\n### 3. Risk Budgeting\n\n**Core idea**: allocate by risk contribution rather than by capital share.\n\n```\nRisk contribution: RC_i = w_i × (Σw)_i / σ_p\nTarget: RC_i / σ_p = budget_i (for all i)\n```\n\n| Strategy | Risk Budget | Best Use Case |\n|------|---------|---------|\n| Equal risk contribution | Each asset 1/N | When you do not know which asset is best |\n| Equity-tilted risk budget | Stocks 60%, bonds 30%, commodities 10% | When you want equities to contribute more risk |\n| Dynamic risk budget | Adjust dynamically by signal strength | When you have market-timing ability |\n\n### 4. All-Weather Strategy\n\n**Bridgewater framework**: allocate risk equally across economic environments.\n\n```\nEconomic environment Asset allocation\n───────── ─────────\nGrowth rising Equities + commodities + corporate bonds\nGrowth falling Government bonds + inflation-protected bonds\nInflation rising Commodities + inflation-protected bonds + EM debt\nInflation falling Equities + government bonds\n\nSimplified allocation example for China-focused portfolios:\n- 30% CSI 300 / CSI 500\n- 40% government bonds / credit bonds\n- 15% gold\n- 15% commodities / REITs\n```\n\n## Guide to the 5 Optimizers\n\n### Overview of the Built-In Optimizers\n\nConfigure them in `config.json` through `optimizer` and `optimizer_params`:\n\n| optimizer | Display Name | Core Idea | Best Use Case |\n|-----------|--------|---------|---------|\n| `equal_volatility` | Equal Volatility | Allocate weights by inverse volatility | Simple and effective baseline |\n| `risk_parity` | Risk Parity | Equalize risk contribution while accounting for correlation | Long-term robust allocation |\n| `mean_variance` | Mean-Variance | Maximize Sharpe ratio or minimize variance | When return forecasts are available |\n| `max_diversification` | Maximum Diversification | Maximize the diversification ratio | When pursuing a low-correlation portfolio |\n| `turnover_aware` | Turnover-Aware | Mean-variance utility with an L1 penalty on weight changes vs the previous rebalance | When trading costs matter; tune `turnover_penalty` to your data frequency |\n\n### 1. `equal_volatility`\n\n```json\n{\n \"optimizer\": \"equal_volatility\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: `w_i = (1/σ_i) / Σ(1/σ_j)`\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Volatility calculation window (trading days) |\n\n**Advantages**: simple and fast, no return forecast required, no correlation matrix required. \n**Disadvantages**: ignores cross-asset correlation.\n\n### 2. `risk_parity`\n\n```json\n{\n \"optimizer\": \"risk_parity\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: solve for weights such that each asset contributes the same amount of risk.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Covariance-matrix estimation window |\n\n**Advantages**: accounts for correlation, spreads risk more evenly, and is robust over long horizons. \n**Disadvantages**: requires iterative solving and is sensitive to covariance estimates.\n\n### 3. `mean_variance`\n\n```json\n{\n \"optimizer\": \"mean_variance\",\n \"optimizer_params\": {\n \"lookback\": 60,\n \"risk_free\": 0.0\n }\n}\n```\n\n**Principle**: Markowitz optimization that maximizes the Sharpe ratio.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Window for estimating means and covariances |\n| risk_free | 0.0 | Risk-free rate (annualized) |\n\n**Advantages**: theoretically optimal (if inputs are accurate). \n**Disadvantages**: extremely sensitive to inputs, prone to extreme weights, and often performs poorly out of sample. \n**Recommendation**: do not make `lookback` too short (`<30` easily overfits), and add upper/lower weight constraints.\n\n### 4. `max_diversification`\n\n```json\n{\n \"optimizer\": \"max_diversification\",\n \"optimizer_params\": {\n \"lookback\": 60\n }\n}\n```\n\n**Principle**: maximize `DR = (w'σ) / σ_p` (the diversification ratio).\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Calculation window |\n\n**Advantages**: does not require return forecasts and seeks true diversification. \n**Disadvantages**: effectiveness is limited in highly correlated environments.\n\n### 5. `turnover_aware`\n\n```json\n{\n \"optimizer\": \"turnover_aware\",\n \"optimizer_params\": {\n \"lookback\": 60,\n \"risk_aversion\": 1.0,\n \"turnover_penalty\": 0.5\n }\n}\n```\n\n**Principle**: minimize `-w'μ + λ·w'Σw + γ·||w - w_prev||₁` subject to long-only, fully-invested weights — mean-variance utility with an L1 penalty on weight changes versus the previous rebalance, so the optimizer only trades when the expected improvement outweighs the (implicit) cost.\n\n| Parameter | Default | Description |\n|------|--------|------|\n| lookback | 60 | Calculation window |\n| risk_aversion | 1.0 | Weight on the variance term (λ) |\n| turnover_penalty | 0.0 | Weight on the L1 turnover term (γ); 0 reduces to the mean-variance baseline |\n\n**Advantages**: dampens rebalancing churn, which usually dominates realized costs; the first rebalance is unpenalized so the cold start is undistorted. \n**Disadvantages**: `turnover_penalty` is scale-sensitive to the return frequency of the input window — for daily returns even γ ≈ 0.5 strongly prefers holding still, so tune it per data frequency.\n\n### Optimizer Selection Decision Tree\n\n```\nDo you have return forecasts?\n├── Yes → Do trading costs / churn matter?\n│ ├── Yes → turnover_aware (tune turnover_penalty to data frequency)\n│ └── No → mean_variance (remember to add constraints)\n└── No → Do you need to account for correlation?\n ├── Yes → risk_parity (recommended default)\n └── No → Are volatility differences across assets large?\n ├── Yes → equal_volatility\n └── No → max_diversification\n```\n\n## Rebalancing Strategy\n\n### Three Rebalancing Triggers\n\n| Method | Trigger Condition | Advantages | Disadvantages |\n|------|---------|------|------|\n| Periodic rebalancing | Fixed monthly / quarterly date | Simple, predictable trading cost | May miss or delay adjustments |\n| Threshold trigger | Deviation from target weight > X% | Trades only when needed | Frequent trading in high-volatility markets |\n| Volatility trigger | VIX / volatility breaks a threshold | Adapts to market regime | Parameter selection is difficult |\n\n### Suggested Rebalancing Frequency\n\n| Asset Class | Suggested Frequency | Threshold |\n|---------|---------|------|\n| Equity portfolio | Monthly | ±5% |\n| Stock-bond mix | Quarterly | ±10% |\n| Global macro | Quarterly / semiannual | ±10% |\n| Cryptocurrency | Weekly / biweekly | ±15% (high volatility) |\n\n### Rebalancing in Backtests\n\nImplement rebalancing logic in `signal_engine.py`:\n\n```python\n# Periodic rebalancing example (every 20 trading days)\nif bar_count % rebalance_freq == 0:\n # Recompute weights\n new_weights = calculate_target_weights(data_map)\n for code, weight in new_weights.items():\n signals[code].iloc[i] = weight\n```\n\n## Cross-Asset Correlation Analysis\n\n### Typical Correlation Matrix (China-Focused Portfolio Example)\n\n| | CSI 300 | CSI 500 | Government Bonds | Gold | BTC |\n|--|--------|--------|------|------|-----|\n| CSI 300 | 1.00 | 0.85 | -0.15 | 0.05 | 0.10 |\n| CSI 500 | 0.85 | 1.00 | -0.10 | 0.03 | 0.12 |\n| Government Bonds | -0.15 | -0.10 | 1.00 | 0.20 | -0.05 |\n| Gold | 0.05 | 0.03 | 0.20 | 1.00 | 0.15 |\n| BTC | 0.10 | 0.12 | -0.05 | 0.15 | 1.00 |\n\n**Key patterns**:\n- Negative stock-bond correlation is the foundation of allocation (but it does not always hold; in 2022 both stocks and bonds sold off)\n- Gold has low correlation with equities and serves as a hedge\n- BTC's correlation with traditional assets is unstable and tends to become positive in crises\n- Large-cap versus small-cap China A-shares have high correlation (`0.85`), so diversification benefits are limited\n\n## Output Format\n\n```markdown\n## Asset Allocation Recommendation\n\n### Allocation Plan\n| Asset | Weight | Risk Contribution | Expected Return (Annualized) |\n|------|------|---------|--------------|\n| CSI 300 | 30% | 45% | 8% |\n| Government Bond ETF | 40% | 15% | 3% |\n| Gold | 15% | 20% | 5% |\n| BTC | 15% | 20% | 15% |\n\n### Optimizer Configuration\n```json\n{\n \"optimizer\": \"risk_parity\",\n \"optimizer_params\": {\"lookback\": 60}\n}\n```\n\n### Expected Risk / Return\n| Metric | Value |\n|------|-----|\n| Expected annualized return | 7.2% |\n| Expected annualized volatility | 8.5% |\n| Expected Sharpe | 0.85 |\n| Expected maximum drawdown | -12% |\n\n### Rebalancing Rules\n- Frequency: quarterly (first trading day of March / June / September / December)\n- Threshold: trigger when any asset deviates from target by ±10%\n- Cost: estimated annual trading cost 0.15%\n```\n\n## Notes\n\n1. **The optimizer needs enough instruments**: at least 3 instruments are needed for meaningful optimization; with 2 instruments, `equal_volatility` is usually enough\n2. **`lookback` window**: too short (`<20`) is noisy, too long (`>120`) reacts slowly, and 60 is a reasonable default\n3. **`mean_variance` trap**: it is the easiest to overfit, and out-of-sample Sharpe is often cut by half or more\n4. **Rebalancing cost**: frequent rebalancing eats into returns; for China A-share portfolios, stamp duty of 0.05% plus commissions is material\n5. **Cross-market allocation**: use `\"source\": \"auto\"` in `config.json`, and let `codes` mix instruments from different markets\n6. **Leverage constraint**: the sum of weights must be ≤ 1.0, and leverage is not allowed unless explicitly specified\n7. **Survivorship bias**: historical correlations may be distorted by delistings and new listings\n\n\n---\nname: backtest-diagnose\ndescription: Diagnose failed or underperforming backtests, locate the root cause, and fix the issue\ncategory: tool\n---\n\n# Backtest Diagnosis\n\n## Overview\n\nUse this skill when a user reports that a backtest failed, raised an error, or produced poor results.\n\n## Diagnostic Workflow\n\n1. **Read existing artifacts**: use `read_file` to inspect `artifacts/metrics.csv`, `equity.csv`, and `trades.csv`\n2. **Read the code**: use `read_file` to inspect `code/signal_engine.py` and `config.json`\n3. **Classify the issue**: determine the root cause using the error taxonomy below\n4. **Apply the fix**: use `edit_file` to modify the code, then rerun the backtest\n5. **Verify the fix**: use `read_file` to inspect the new `metrics.csv`\n\n## Error Taxonomy\n\n### Runtime Errors (`exit_code != 0`)\n\n| Error Type | Common Cause | Fix |\n|---------|---------|---------|\n| ImportError | Missing dependency | `bash(\"pip install xxx\")` |\n| KeyError | DataFrame column-name mismatch | Check the actual column names in `data_map` |\n| IndexError | Empty data or insufficient length | Add length checks |\n| TypeError | Incorrect signal type | Ensure the return value is `pd.Series` |\n\n### Logic Bugs (Backtest Succeeds but Results Are Abnormal)\n\n1. **Zero trades** (`trade_count=0`): signal-logic bug. Conditions are too strict, so the signal stays at 0. Check whether entry and exit logic is reasonable, and inspect the signal series to confirm it is not all zeros.\n2. **Late trades** (first trade occurs more than 2 years after the backtest start): data-filtering bug. The lookback window may be too long, or the initial data segment may have been dropped. Shorten the window or check whether `dropna` is too aggressive.\n3. **Capital utilization < 50%** (mostly in cash): position-sizing bug. Signal triggers may be too sparse, or the position-sizing logic may be wrong.\n4. **Open position at the end** (a position still exists when the backtest ends): exit-timing bug. Forced liquidation may be missing, or exit logic does not cover the final segment.\n\n### Data Errors\n\n| Symptom | Root Cause | Fix |\n|------|------|---------|\n| No data fetched | Invalid API token or code issue | Check `config.json` |\n| Too little data | Date range too narrow | Expand the date range |\n\n### Data-Source Error Ignore List\n\nIf you encounter the following keywords, **do not modify the code**. The problem is on the data-provider side:\n- a provider-side \"no data available\" response\n- `rate limit`\n- `API limit`\n- `daily limit`\n- `Information` (common in Tushare API responses)\n\nThese issues require the user to check the API token, switch data sources, or wait for the quota to reset.\n\n## Hard-Gate Checklist\n\n1. `artifacts/metrics.csv` exists and is non-empty\n2. `artifacts/equity.csv` exists and is non-empty\n3. `trade_count > 0` (`0` trades means a signal bug)\n4. The equity series contains no `NaN`\n5. `exit_code == 0`\n\n## Evidence hookup\n\nThis Hard-Gate Checklist is also the evidence ingestion gate for Strategy Discovery: a run failing any gate produces **no evidence rows** — never partial rows — and is skipped with a stable machine-readable token (`hard-gate:exit-nonzero`, `hard-gate:metrics-missing`, `hard-gate:zero-trades`, `hard-gate:equity-empty`, `hard-gate:equity-nan`). Diagnose and fix the failing gate as usual, rerun the backtest, then repopulate the evidence cache with `refresh_strategy_evidence` (agent tool / MCP tool, or `vibe-trading strategy-evidence refresh --manifest `) so the fixed run becomes queryable evidence. See the `strategy-discovery` skill for the manifest format and the full gate list.\n\n## Fixing Principles\n\n- Use **edit_file** to make precise code fixes instead of rewriting the entire file with `write_file`, unless the structure is fundamentally broken\n- Fix the bug only, do not change strategy logic unless the user explicitly asks\n- Fix one issue at a time, and rerun the backtest immediately after each fix\n- Limit yourself to at most 3 repair iterations\n\n## Post-Fix Validation Rules\n\nAfter modifying `signal_engine.py`, you must confirm:\n1. **AST syntax passes**: `bash(\"python -c \\\"import ast; ast.parse(open('code/signal_engine.py').read()); print('OK')\\\"\")`\n2. **Contains `class SignalEngine`**: the file must define `class SignalEngine`\n3. **Contains `def generate`**: the class must contain a `def generate` method\n4. **Rerun the backtest**: after the fix, rerun the backtest and verify the results\n\n## `action_items` Writing Rules\n\nAfter diagnosis, output actionable improvement suggestions:\n- Format: `\"Change X from A to B\"` or `\"Add X logic in signal_engine.py\"`\n- Be specific about parameter values, filenames, and function names\n- Provide at least 2 items\n- Examples:\n - `\"Change RSI threshold from 30 to 25 in signal_engine.py line 42\"`\n - `\"Add signals = signals.fillna(0) after signal calculation to prevent NaN propagation\"`\n - `\"Add a volume filter: skip buy signals when volume is below the 20-day average\"`\n\n\n---\nname: behavioral-finance\ndescription: \"Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.\"\ncategory: analysis\n---\n\n# Behavioral Finance Applications\n\n## Overview\n\nTranslate behavioral-finance theory into quantifiable trading signals and risk-control rules. Core assumption: market participants systematically deviate from rational decision-making, and these biases can be predicted and exploited.\n\nApplicable scenarios:\n- Behavioral interpretation and parameter optimization for momentum / reversal strategies\n- Contrarian signals when market sentiment becomes extreme\n- Debiasing mechanisms in portfolio construction\n- Capturing behavior patterns specific to retail-driven China A-share markets\n\n## Core Concepts\n\n### Overreaction and Underreaction\n\n**Underreaction** → momentum effect:\n```\nMechanism: anchoring bias + conservatism\n Investors anchor on old information and update insufficiently to new information\n After an earnings beat, the stock price digests it gradually rather than all at once\nChina A-share evidence:\n - Earnings-guidance beats still produce 3-5% excess return over the following 20 days\n - After analyst rating upgrades, momentum often persists for 1-3 months\nQuant signal:\n SUE (standardized unexpected earnings) > 2σ -> buy and hold for 60 days\n Top 10% 20-day return -> continue holding for 20 days (China A-share momentum cycles are shorter)\n```\n\n**Overreaction** → reversal effect:\n```\nMechanism: representativeness heuristic + availability bias\n Investors extrapolate recent trends too aggressively and ignore mean reversion\n Panic / euphoria drives reactions beyond what fundamentals support\nChina A-share evidence:\n - Rebounds after consecutive limit-downs (after 3 limit-downs, the average 20-day rebound is 8%)\n - Big annual losers often earn 5-10% excess return the next year\nQuant signal:\n Bottom 10% of 250-day return -> buy and hold for 250 days\n RSI(5) < 10 -> short-term rebound signal (5-10 days)\n```\n\n**Key distinction**:\n| Dimension | Underreaction (Momentum) | Overreaction (Reversal) |\n|------|------------------|------------------|\n| Time scale | 1-12 months | <1 week or >12 months |\n| Information type | Clear events (earnings / announcements) | Ambiguous information (sentiment / trend) |\n| Best China A-share window | 20-60 days | 5-10 days (short term) / 1 year (long term) |\n\n### Cognitive Bias Checklist\n\n**Individual decision biases**:\n| Bias | Manifestation | Quant Detection | Debiasing Strategy |\n|------|------|----------|------------|\n| Loss aversion | Hold losing stocks, sell winners too early | Holding period: losing positions > winning positions by 2-3x | Pre-set stop-loss line and execute mechanically |\n| Overconfidence | Overtrading, concentrated positions | Monthly turnover > 100%, single-stock weight > 30% | Limit the number of trades per month |\n| Anchoring effect | Anchoring to entry price / historical highs | Abnormal volume expansion near the entry price | Use relative valuation instead of absolute price |\n| Confirmation bias | Focus only on information that supports the existing view | Single-source information, ignoring bearish news | Force reading the opposing view |\n| Recency bias | Overweight recent events | Recent gains/losses have too much influence on position size | Lengthen the evaluation window (≥60 days) |\n| Framing effect | Same information framed differently leads to different decisions | Decision differences between return format and absolute-PnL format | Evaluate consistently in return space |\n\n**Group behavior biases**:\n| Bias | Manifestation | China A-share Characteristics | Quant Indicator |\n|------|------|---------|----------|\n| Herding | Chasing rallies and panic-selling together | Extremely fast sector rotation (3-5 days) | Intra-sector stock correlation > 0.8 |\n| Information cascades | Ignoring private information and following public signals | Sector follow-through after a leader stock hits limit-up | Sector return on the day after leader-stock limit-up |\n| Attention effect | Buying stocks that attract attention | Explosive turnover in limit-up / news-driven stocks | Abnormal turnover > 3x average |\n\n### Investor Sentiment Cycle\n\n```\nFear -> Caution -> Optimism -> Excitement -> Euphoria -> Denial -> Panic -> Fear\n | | | | | | |\n Bottom Recovery Mid-uptrend Pre-top Top Early selloff Pre-bottom\n\nQuant sentiment indicators:\n 1. Closed-end fund discount: discount > 15% -> extreme fear -> buy signal\n 2. Margin-financing growth: monthly growth > 20% -> euphoria -> reduce position\n 3. New account openings: weekly openings > 2x average -> overheated market\n 4. Turnover ratio: All-A daily turnover > 3% -> euphoric; < 0.5% -> deeply depressed\n 5. Number of limit-up stocks: > 100 -> euphoric; < 10 -> weak\n```\n\n## Analysis Framework\n\n### 1. Disposition-Effect Signal\n\n**Principle**: investors tend to sell winners and hold losers. Once winning positions are largely cleared, selling pressure eases; when trapped holders are deeply underwater, selling pressure can also ease.\n\n```\nChina A-share application:\n Compute the profit ratio in the chip-distribution structure:\n - Profit ratio > 90% and shrinking volume -> winners are reluctant to sell -> may continue rising\n - Profit ratio > 90% and expanding volume -> winners are exiting -> topping signal\n - Profit ratio < 10% and shrinking volume -> low willingness to cut losses -> bottom stabilization\n - Profit ratio < 10% and expanding volume -> panic selling -> short-term oversold\n\nQuant implementation:\n capital_gain_overhang = (current_price - avg_cost) / avg_cost\n where avg_cost is approximated by 60-day VWAP\n CGO > 0.2 -> strong unrealized gains, watch for disposition-effect selling pressure\n CGO < -0.3 -> deeply trapped holders, selling pressure may actually ease\n```\n\n### 2. Composite Sentiment Indicator\n\n```python\n# Multi-dimensional sentiment score (0-100, 50 = neutral)\nsentiment_components = {\n 'turnover_ratio': normalize(all_a_turnover, historical_percentile), # weight 25%\n 'margin_growth': normalize(monthly_margin_growth, historical_percentile), # weight 25%\n 'new_high_ratio': normalize(new_high_ratio, historical_percentile), # weight 20%\n 'limit_up_count': normalize(limit_up_count, historical_percentile), # weight 15%\n 'fund_discount': normalize(closed_end_fund_discount, historical_percentile), # weight 15% (inverse)\n}\n\nsentiment_score = weighted_sum(components)\n# > 80: extreme greed -> cut exposure below 60%\n# 60-80: optimistic -> maintain normal exposure\n# 40-60: neutral -> keep exposure unchanged\n# 20-40: pessimistic -> add gradually\n# < 20: extreme fear -> increase exposure above 80%\n```\n\n### 3. Behavioral Optimization of Momentum Strategies\n\nTraditional momentum (sorting by past 12-month returns) is unstable in China A-shares. A behavioral-finance perspective suggests the following optimizations:\n\n```\nOptimization 1: Separate sentiment momentum from fundamental momentum\n Sentiment momentum = part of recent price rise with no fundamental support -> short-term reversal\n Fundamental momentum = price rise consistent with earnings revisions -> can persist\n Trade: buy stocks with \"strong fundamental momentum + weak sentiment momentum\"\n\nOptimization 2: Attention-weighted momentum\n High-attention retail names reverse faster\n Indicator: if abnormal turnover > 3x average, cut momentum holding period by 50%\n Example: if a normal momentum basket holds for 60 days, high-attention names hold only 30 days\n\nOptimization 3: Combine cross-sectional momentum and time-series momentum\n Cross-sectional: relative strength (top 20% in return ranking)\n Time-series: absolute trend (price > MA60)\n Both satisfied -> strong signal; only one satisfied -> half position\n```\n\n### 4. Contrarian Trading Signals\n\n```\nExtreme-fear buy conditions (at least 3 items):\n □ Shanghai Composite RSI(5) < 15\n □ All-A daily turnover < 0.5%\n □ Weekly margin-financing decline > 5%\n □ Limit-up count < 10 and limit-down count > 50\n □ Closed-end fund discount > 15%\n\nExtreme-greed sell conditions (at least 3 items):\n □ Shanghai Composite RSI(5) > 90\n □ All-A daily turnover > 3%\n □ Weekly margin-financing growth > 10%\n □ Limit-up count > 150\n □ Weekly increase in new account openings > 100%\n```\n\n## Output Format\n\nBehavioral-finance analysis report:\n```\n=== Market Sentiment Diagnosis ===\nDate: 2026-03-28\nSentiment score: 72/100 (optimistic bias)\nCurrent phase: transition from optimism to excitement\n\n=== Behavioral-Bias Signals ===\nOverreaction detection: 127 stocks rose > 15% in the past 5 days -> 65% probability of short-term reversal\nDisposition effect: winner-clearing ratio is low (35%) -> overhead selling pressure remains\nHerding effect: sector correlation 0.85 -> severe follow-the-leader behavior, divergence likely soon\n\n=== Strategy Recommendations ===\nMomentum strategy: shorten holding period from 60 days to 30 days (market attention is elevated)\nContrarian signal: not triggered (sentiment is not yet extreme)\nPosition suggestion: maintain 70% exposure, and prioritize names with \"strong fundamental momentum + weak sentiment momentum\"\n\n=== Debiasing Checklist ===\n□ Are you overconfident because of recent profits? -> check position concentration\n□ Are you anchored to your entry price? -> re-evaluate using current PE/PB\n□ Are you ignoring bearish information? -> force yourself to read bearish research reports\n```\n\n## Notes\n\n1. **High retail participation in China A-shares**: behavioral-bias signals are more pronounced than in US equities, but sector rotation is also faster, so momentum windows should be shorter\n2. **Lag in sentiment indicators**: margin-financing balance is released T+1, and new account openings are weekly, so they are not suitable for intraday trading\n3. **Structural changes**: after 2019, foreign capital and quant participation rose, so the effectiveness of traditional behavioral-finance signals may have weakened\n4. **Behavioral factors correlate with traditional factors**: disposition-effect factors correlate about 0.3-0.5 with momentum, so control collinearity\n5. **Overfitting risk**: behavioral stories are easy to explain after the fact, so out-of-sample validation is mandatory\n6. **Extreme sentiment is rare**: extreme fear / greed appears only 2-3 times per year, so strategy capacity is limited\n\n## Dependencies\n\n```bash\npip install pandas numpy scipy\n```\n\n\n---\nname: bottleneck-hunter\ncategory: strategy\ndescription: \"Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.\"\n---\n\n# Supply-Chain Bottleneck Hunter\n\nDecompose a super-trend (user-specified, e.g. \"AI infrastructure\", \"energy transition\") into its physical supply chain and hunt for bottleneck-arbitrage opportunities.\n\n## Core Idea\n\nDon't ask \"which AI stock to buy\" — ask \"if this trend keeps expanding, which link runs out first?\"\n\nTraditional research chases leaders and known tracks. This skill inverts: **start from the choke points of the physical supply chain and find companies nobody notices but that the whole industry must wait on when they run short.**\n\nThe edge: Layer-1 bottlenecks (GPU, HBM, power) are already priced in. The real alpha is in **Layer 2 and Layer 3** — optical modules, lasers, InP substrates, SOI wafers, epitaxy equipment, wafer-level test, IC substrates, specialty fiberglass.\n\n## Step 1: Super-Trend Confirmation\n\n### Trend Filter Criteria\n\n| Criterion | Requirement | How to verify |\n|-----------|-------------|---------------|\n| Durability | ≥3-5 years of certain growth | Search industry forecasts, capex plans |\n| Physicality | Needs real hardware/material/equipment build | Distinguish \"software upgrade\" from \"physical expansion\" |\n| Scale | Global capex >$50B/year | Search top players' capex guidance |\n| Acceleration | Demand growth > supply expansion | Compare demand growth vs capacity plans |\n\nUse `web_search` to verify. Reference super-trends: AI infrastructure, energy transition (nuclear/grid/storage), defense modernization, semiconductor reshoring, space economy.\n\n## Step 2: Physical Supply-Chain Decomposition\n\nDon't stop at concepts — decompose to physical entities.\n\n```\nLayer 0 (end): final product/service\nLayer 1 (core component): already closely watched → priced in, limited alpha\nLayer 2 (sub-component/material): low attention, alpha-rich\nLayer 3 (upstream equipment/raw material)\nLayer 4 (infrastructure): power, cooling, land, talent, certifications\n```\n\n### AI Infrastructure Example\n\n```\nLayer 0: AI model training/inference services\nLayer 1: GPU/accelerators, HBM, servers, data centers\nLayer 2 (focus zone):\n - Network interconnect: optical modules, fiber, switch ASICs, copper cables\n - Optical comms core: lasers (EML/VCSEL/CW), modulators, photodetectors\n - Semiconductor materials: InP substrates, GaAs substrates, SOI wafers, SiC substrates\n - Advanced packaging: CoWoS interposers, HBM TSV, ABF substrate film\n - PCB/substrate: high-frequency PCB, IC substrates, specialty fiberglass\n - Test: wafer-level test (probe cards), burn-in, ATE\n - Thermal/cooling: liquid cooling, CDU, immersion fluid\n - Power connection: busbars, UPS, distribution, transformers\nLayer 3: epitaxy equipment (MOCVD/MBE), lithography/etch, high-purity metals (In/Ga/Ge), specialty gases, sputtering targets, certifications (MSA/Telcordia)\nLayer 4: power (nuclear/gas/transmission), cooling water, data-center land/permits\n```\n\nFor other trends, use `web_search` with queries like `{trend} supply chain bottleneck`, `{trend} shortage critical component`, `{trend} capacity constraint`, `{trend} sole source supplier`.\n\n## Step 3: Bottleneck Identification — Finding \"Choke Points\"\n\nFor each Layer 2-3 link, evaluate 6 criteria:\n\n| # | Criterion | Question | Score |\n|---|-----------|----------|-------|\n| 1 | Supply concentration | ≤3 global suppliers? | 🔴 ≤2 / 🟡 3-5 / 🟢 >5 |\n| 2 | Expansion lead time | How long to add capacity? | 🔴 >2y / 🟡 1-2y / 🟢 <1y |\n| 3 | Substitutability | Can other tech/material replace it? | 🔴 irreplaceable / 🟡 partial / 🟢 easy |\n| 4 | Capacity utilization | Current utilization? | 🔴 >90% / 🟡 70-90% / 🟢 <70% |\n| 5 | Demand growth | Downstream demand growth? | 🔴 >50%/yr / 🟡 20-50% / 🟢 <20% |\n| 6 | Customer qualification cycle | How long for a new supplier to qualify? | 🔴 >1y / 🟡 6-12m / 🟢 <6m |\n\n**Bottleneck grade**: 🔴×≥4 → **S-grade** (single-point failure, highest priority); 🔴×3 → **A-grade** (severely constrained); 🔴×1-2 → **B-grade** (stressed but manageable); no 🔴 → not a bottleneck, skip.\n\n## Step 4: Company Screening — From Bottleneck to Tickers\n\nFor each S/A-grade bottleneck, use `web_search` / `screen_market` to find listed companies.\n\n### Initial Screen\n\n| Criterion | Requirement |\n|-----------|-------------|\n| Listing status | Listed (A/HK/US/JP/TW/EU) |\n| Bottleneck revenue share | >30% of revenue from the bottleneck link |\n| Market cap | Prefer <$10B (large caps already priced) |\n| Liquidity | Average daily turnover >$1M |\n\n### Valuation Gate (mandatory, never skip)\n\n**A real bottleneck ≠ an investment opportunity.** For every company, compute PE/PB/ROE/FCF yield with `financial_rigor` (`command=verify_valuation`), and run `financial_rigor` (`command=three_scenario`) for scenario valuation:\n\n- **Red light** (any one → signal strength capped at ★★, flag \"valuation stretched\"): market cap >20% of TAM; PS>30x with revenue growth <100%; market cap >10× 5-year optimistic revenue forecast; stock doubled within 60 days of a follow-on offering.\n- **Yellow light** (needs extra justification, else downgrade): loss-making + PS>15x; PS >5× a profitable peer; PE>80x (compute PEG).\n- **Green light** (bonus): PS<10x with revenue growing; PE<30x with a moat (flag \"margin of safety\").\n\n**Sanity check (mandatory)**: with `financial_rigor` (`command=three_scenario`), answer — \"buying at current market cap, if the most optimistic scenario fully plays out and I exit at 25× PE in 10 years, what's the annualized return?\" <10%/yr → flag \"no margin of safety at current price\".\n\n## Step 5: Cross-Validation — Don't Trust a Single Story\n\n### Positive checks\n\n| Check | Question |\n|-------|----------|\n| Customer validation | Have top customers signed/imported? (check announcements, customer filings) |\n| Revenue validation | Is the bottleneck already showing in revenue growth? (last 2-3 quarters) |\n| Price validation | Is the product raising price? (industry quotes, analyst reports) |\n| Capacity validation | Is capacity really tight? (lead times, customer complaints) |\n| Capital validation | Is there expansion capex? (company guidance) |\n\nUse `get_financial_statements` / `get_stock_news` / `web_search`.\n\n### Reverse checks (Munger inversion)\n\n- Why don't smart people buy this stock?\n- Can the bottleneck be bypassed? Alternative routes?\n- Can China / other players quickly replicate capacity?\n- If end-demand drops 50%, what happens to this company?\n- Has management diluted at highs before?\n- What growth assumption does the current valuation imply?\n\n## Step 6: Output — Bottleneck Opportunity Board\n\n### Ranking Table\n\n| Rank | Company | Ticker | Mkt Cap | Revenue | PS | PE | Bottleneck link | Grade | Share | Growth | Signal | Valuation |\n|------|---------|--------|---------|---------|-----|-----|-----------------|-------|-------|--------|--------|-----------|\n\n**Market cap, revenue, PS, PE are mandatory** — never skip with \"TBD\". If financials can't be obtained, signal strength ≤★★.\n\nSignal strength (valuation gate directly affects):\n- ★★★★★ multi-cross-validated + customers imported + revenue confirmed + valuation green\n- ★★★★ most checks pass + valuation green/yellow (with explanation)\n- ★★★ logic holds but parts unverified + valuation yellow acceptable\n- ★★ early signal, or logic holds but valuation red\n- ★ pure concept, unverified\n\nAfter drafting, run `report_audit` (`command=extract` → verify each point → `command=verdict`) as a quality gate to ensure no hallucinated numbers.\n\n### One-Pager Template\n\n```\n🎯 {Company} ({Ticker}) — {one-line bottleneck positioning}\n\nWhy it's a bottleneck: (2-3 sentences)\nWhy this company: (2-3 sentences)\n\nCatalyst timeline:\n- Near-term (1-3m): [earnings / capacity / customer win]\n- Mid-term (3-12m): [industry trend / expansion node]\n\nKey risks: 1. 2.\n\nKey data: market cap / revenue / PS / PE / growth / bottleneck revenue share\nMargin of safety: 10y 25× PE exit method, annualized return XX%. Conclusion: yes/no.\n\nCross-validation status: ✅ customer / ✅ revenue / ⚠️ valuation stretched / ❌ unverified\n\nConclusion: deep research / watchlist / skip\n```\n\nSave with `write_file` to the reports directory (e.g. `reports/bottleneck-map/{trend}-bottleneck-{YYYYMMDD}.md`).\n\n## Step 7: Inventory Update — Maintain the Bottleneck Map\n\nOn each run: ① re-check identified bottlenecks (new suppliers? capacity expanded? substitute breakthrough?); ② scan new bottlenecks (`web_search` last 7 days supply chain / shortage / bottleneck news); ③ update grades (upgrade/downgrade/relieve).\n\n## AI Research Bias Self-Check\n\n| Bias | Symptom | Counter |\n|------|---------|---------|\n| Leader-bias | Search dominated by large caps | Deliberately search small-cap suppliers, add \"small cap\" |\n| English-bias | Miss JP/KR/TW players | Must search JP/KR/TW market suppliers |\n| Narrative-bias | Drawn to \"AI concept\" labels | Look only at actual supply-chain position, not market labels |\n| Confirmation-bias | After finding a bottleneck, only seek positive evidence | Force Step 5 reverse checks |\n| Recency-bias | Rely on stale info | Prefer last 30 days of data |\n\n## Core Principles (highest priority)\n\n1. **Don't ask the AI to recommend stocks — ask it to decompose supply chains.** The question matters more than the answer.\n2. **Physical first** — only links that need real physical product/material/equipment.\n3. **Layer 2 and Layer 3** — don't chase already-priced leaders.\n4. **Cross-validate** — every conclusion needs ≥2 independent sources.\n5. **Be honest about uncertainty** — if data is missing, say so; don't fill with speculation.\n6. **Bottlenecks are temporary** — every bottleneck gets resolved; the key is timing the window.\n7. **Small cap ≠ good opportunity** — a small cap can also be a bad company; it must pass financial quality.\n8. **A real bottleneck ≠ an investment** — at PS>30x or still loss-making, the current price is not a buy. **Valuation is a hard gate that cannot be overridden by bottleneck purity, signal strength, or narrative appeal.** Better to miss a bottleneck stock that ran than buy a loss-making company at 100× sales.\n\n\n---\nname: candlestick\ndescription: Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores.\ncategory: strategy\n---\n# Candlestick Pattern Recognition\n\n## Purpose\n\nIdentifies 15 classic candlestick patterns and generates trading signals:\n\n### Single-Candle Patterns (5)\n| Pattern | Signal | Description |\n|------|------|------|\n| Hammer | Bullish | Long lower shadow with a small body at the top |\n| Inverted Hammer | Bullish | Long upper shadow with a small body at the bottom |\n| Shooting Star | Bearish | Long upper shadow with a small body at the bottom (appears after an uptrend) |\n| Doji | Neutral | Open and close are nearly equal |\n| Spinning Top | Neutral | Small body with roughly equal upper and lower shadows |\n\n### Double-Candle Patterns (5)\n| Pattern | Signal |\n|------|------|\n| Bullish Engulfing | Bullish |\n| Bearish Engulfing | Bearish |\n| Bullish Harami | Bullish |\n| Bearish Harami | Bearish |\n| Piercing Line | Bullish |\n| Dark Cloud Cover | Bearish |\n\n### Triple-Candle Patterns (4)\n| Pattern | Signal |\n|------|------|\n| Morning Star | Bullish |\n| Evening Star | Bearish |\n| Three White Soldiers | Bullish |\n| Three Black Crows | Bearish |\n\n## Signal Logic\n\nBullish patterns score +1, bearish patterns score -1. Go long when the total score is > 0, go short when it is < 0, and stand aside when it equals 0.\n\n## Parameters\n\n| Parameter | Default | Description |\n|------|--------|------|\n| body_pct | 0.1 | Threshold for body-to-range ratio in a doji |\n| shadow_ratio | 2.0 | Ratio of shadow length to body length |\n\n## Dependencies\n\n```bash\npip install pandas numpy requests\n```\n\n## Signal Convention\n\n- `1` = long, `-1` = short, `0` = stand aside\n\n\n---\nname: ccxt\ncategory: data-source\ndescription: CCXT unified crypto exchange library (100+ exchanges). Free public market data. Fallback when OKX is unavailable.\n---\n\n## Overview\n\nCCXT is a unified cryptocurrency exchange trading library supporting 100+ exchanges including Binance, Bybit, OKX, Coinbase, Kraken, and more. Public market data (OHLCV, tickers, order books) requires no API key.\n\n- GitHub: https://github.com/ccxt/ccxt (35k+ stars)\n- Install: `pip install ccxt`\n\n## Quick Start\n\n```python\nimport ccxt\n\nexchange = ccxt.binance({\"enableRateLimit\": True})\n\n# Fetch daily OHLCV\nohlcv = exchange.fetch_ohlcv(\"BTC/USDT\", \"1d\", limit=100)\n# Returns: [[timestamp, open, high, low, close, volume], ...]\n\n# Fetch ticker\nticker = exchange.fetch_ticker(\"ETH/USDT\")\nprint(f\"ETH price: {ticker['last']}\")\n```\n\n## Key Methods\n\n| Method | Description | Returns |\n|--------|-------------|---------|\n| `fetch_ohlcv(symbol, timeframe, since, limit)` | Historical candles | `[[ts, o, h, l, c, v], ...]` |\n| `fetch_ticker(symbol)` | Latest quote | `{last, bid, ask, volume, ...}` |\n| `fetch_tickers(symbols)` | Batch quotes | `{symbol: ticker}` |\n| `fetch_order_book(symbol, limit)` | Order book | `{bids, asks, timestamp}` |\n| `fetch_trades(symbol, since, limit)` | Recent trades | `[{price, amount, side, timestamp}, ...]` |\n\n## Timeframes\n\n`1m`, `3m`, `5m`, `15m`, `30m`, `1h`, `2h`, `4h`, `6h`, `12h`, `1d`, `1w`, `1M`\n\nNote: not all exchanges support all timeframes. Use `exchange.timeframes` to check.\n\n## Symbol Format\n\nCCXT uses slash format: `BTC/USDT`, `ETH/BTC`, `SOL/USDT`\n\nThe project's DataLoader automatically converts `BTC-USDT` (hyphen) to `BTC/USDT` (slash).\n\n## Exchange Selection\n\nSet via environment variable: `CCXT_EXCHANGE=binance` (default)\n\nPopular exchanges: `binance`, `bybit`, `okx`, `coinbase`, `kraken`, `bitget`, `gate`\n\n## Built-in Loader\n\nThe project has a built-in CCXT DataLoader at `backtest/loaders/ccxt_loader.py`. It serves as a fallback when the OKX loader is unavailable.\n\n## Pagination\n\nFor long history, CCXT paginates via the `since` parameter (millisecond timestamp). The built-in loader handles this automatically (up to 200 pages).\n\n\n# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/一买一卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/一买一卖.md)\n\n\n# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/三买三卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/三买三卖.md)\n\n\n# 买卖点 Skill\n\nPath: `agent/src/skills/chanlun/references/买卖点/二买二卖.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/买卖点/二买二卖.md)\n\n\n# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/中枢.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/中枢.md)\n\n\n# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/分型.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/分型.md)\n\n\n# 核心概念 Skill\n\nPath: `agent/src/skills/chanlun/references/核心概念/笔.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/chanlun/references/核心概念/笔.md)\n\n\n---\nname: chanlun\ndescription: 基于缠论（缠中说禅）的形态识别引擎，使用czsc库自动检测K线分型、笔、中枢，并生成一买/一卖/二买/二卖/三买/三卖等买卖点信号。支持多周期分析和形态分类（3/5/7/9/11笔形态）。\ncategory: strategy\n---\n# 缠论形态识别\n\n## 用途\n\n基于**缠中说禅**理论的价格形态识别。缠论是一套完全基于价格结构的技术分析方法，核心链路：\n\n```\n原始K线 → 去包含处理 → 分型识别 → 笔检测 → 中枢构建 → 买卖点判定\n```\n\n适用于任何有 OHLCV 数据的市场（A股、加密货币、期货等）。\n\n## 核心概念\n\n| 概念 | 说明 | 详细文档 |\n| --- | --- | --- |\n| 分型（FX） | 顶分型：中间K线最高；底分型：中间K线最低 | [分型](references/核心概念/分型.md) |\n| 笔（BI） | 相邻顶底分型之间的一段走势，最小单元 | [笔](references/核心概念/笔.md) |\n| 中枢（ZS） | 至少3笔构成的价格重叠区域，趋势的核心 | [中枢](references/核心概念/中枢.md) |\n\n## 买卖点体系\n\n| 买卖点 | 含义 | 详细文档 |\n| --- | --- | --- |\n| 一买/一卖 | 趋势结束后的第一个反转信号（背驰点） | [一买一卖](references/买卖点/一买一卖.md) |\n| 二买/二卖 | 一买/一卖后回调不破底/顶的确认信号 | [二买二卖](references/买卖点/二买二卖.md) |\n| 三买/三卖 | 中枢上移/下移后回调不进入前中枢的信号 | [三买三卖](references/买卖点/三买三卖.md) |\n\n## 依赖安装\n\n```bash\npip install czsc requests pandas\n```\n\n## 快速上手\n\n```python\nfrom czsc import CZSC, RawBar, Freq\nfrom datetime import datetime\n\n# 准备 RawBar 列表（需按时间正序排列）\nbars = [\n RawBar(symbol=\"BTC-USDT\", id=0, dt=datetime(2026,1,1),\n freq=Freq.D, open=70000, close=71000,\n high=72000, low=69000, vol=1000, amount=71000000),\n # ... 更多K线\n]\n\n# 创建分析器，自动检测分型/笔/中枢\nc = CZSC(bars)\n\n# 访问结果\nprint(c.bi_list) # 已完成的笔\nprint(c.bars_ubi) # 未完成笔中的K线\n```\n\n## 可用信号函数（czsc.signals.cxt）\n\nczsc 内置 43 个缠论信号函数，核心如下：\n\n| 函数 | 说明 | 类型 |\n| --- | --- | --- |\n| `cxt_first_buy_V221126` | 一买信号 | 买卖点 |\n| `cxt_first_sell_V221126` | 一卖信号 | 买卖点 |\n| `cxt_second_bs_V230320` | 均线辅助二买二卖 | 买卖点 |\n| `cxt_third_bs_V230318` | 均线辅助三买三卖 | 买卖点 |\n| `cxt_third_buy_V230228` | 笔三买辅助 | 买卖点 |\n| `cxt_double_zs_V230311` | 两中枢组合判断BS1 | 中枢 |\n| `cxt_three_bi_V230618` | 三笔形态分类 | 形态 |\n| `cxt_five_bi_V230619` | 五笔形态分类 | 形态 |\n| `cxt_seven_bi_V230620` | 七笔形态分类 | 形态 |\n| `cxt_nine_bi_V230621` | 九笔形态分类 | 形态 |\n| `cxt_eleven_bi_V230622` | 十一笔形态分类 | 形态 |\n| `cxt_bi_base_V230228` | BI基础信号（方向/转折） | 基础 |\n| `cxt_bi_end_V230312` | MACD辅助笔结束 | 辅助 |\n| `cxt_range_oscillation_V230620` | 区间震荡判断 | 辅助 |\n| `cxt_zhong_shu_gong_zhen_V221221` | 大小级别中枢共振 | 中枢 |\n\n## 信号约定\n\n- 信号引擎输出：`1`=做多，`-1`=做空，`0`=观望\n- 做多条件：一买信号 或 三笔向上盘背 或 五笔类一买\n- 做空条件：一卖信号 或 三笔向下盘背 或 五笔类一卖\n- 中枢辅助：价格在中枢下沿附近做多优势，上沿附近做空优势\n\n## 数据格式\n\nczsc 接受 `List[RawBar]`，每个 RawBar 包含：\n\n| 字段 | 类型 | 说明 |\n| --- | --- | --- |\n| symbol | str | 标的代码 |\n| id | int | 序号（从0开始） |\n| dt | datetime | 时间 |\n| freq | Freq | 频率：`Freq.F1/F5/F15/F30/F60/D/W/M` |\n| open | float | 开盘价 |\n| close | float | 收盘价 |\n| high | float | 最高价 |\n| low | float | 最低价 |\n| vol | float | 成交量 |\n| amount | float | 成交额 |\n\n## 实现方式\n\n使用 [czsc](https://github.com/waditu/czsc) 库（v0.9.68+），基于纯 Python 实现（可选 Rust 加速后端）。支持增量更新，适合实时分析。\n\n\n# commodity-analysis\n\nPath: `agent/src/skills/commodity-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/commodity-analysis/SKILL.md)\n\n\n# convertible-bond\n\nPath: `agent/src/skills/convertible-bond/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/convertible-bond/SKILL.md)\n\n\n# corporate-events\n\nPath: `agent/src/skills/corporate-events/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/corporate-events/SKILL.md)\n\n\n# correlation-analysis\n\nPath: `agent/src/skills/correlation-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-analysis/SKILL.md)\n\n\n# correlation-regime\n\nPath: `agent/src/skills/correlation-regime/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/correlation-regime/SKILL.md)\n\n\n# credit-analysis\n\nPath: `agent/src/skills/credit-analysis/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/credit-analysis/SKILL.md)\n\n\n# cross-market-strategy\n\nPath: `agent/src/skills/cross-market-strategy/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/cross-market-strategy/SKILL.md)\n\n\n# crypto-derivatives\n\nPath: `agent/src/skills/crypto-derivatives/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/crypto-derivatives/SKILL.md)\n\n\n# data-routing\n\nPath: `agent/src/skills/data-routing/SKILL.md`\n\n[View Raw Content on GitHub](https://raw.githubusercontent.com/HKUDS/Vibe-Trading/HEAD/agent/src/skills/data-routing/SKILL.md)\n\n\n"}