# Technical Documentation: freqtrade/freqtrade-strategies > ℹ️ **Provenance:** Multi-Tier Ingestion · [CodeWiki Reference](https://codewiki.google/github.com/freqtrade/freqtrade-strategies) ### Overview (`README.md`) # Freqtrade strategies This Git repo contains free buy/sell strategies for [Freqtrade](https://github.com/freqtrade/freqtrade). All strategies should work with a freqtrade version of 2022.4 or newer. ## Disclaimer These strategies are for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS. Always start by testing strategies with a backtesting then run the trading bot in Dry-run. Do not engage money before you understand how it works and what profit/loss you should expect. We strongly recommend you to have coding and Python knowledge. Do not hesitate to read the source code and understand the mechanism of this bot. ## Table of Content - [Free trading strategies](#free-trading-strategies) - [Contribute](#share-your-own-strategies-and-contribute-to-this-repo) - [FAQ](#faq) - [What is Freqtrade?](#what-is-freqtrade) - [What includes these strategies?](#what-includes-these-strategies) - [How to install a strategy?](#how-to-install-a-strategy) - [How to test a strategy?](#how-to-test-a-strategy) - [How to create/optimize a strategy?](https://www.freqtrade.io/en/latest/strategy-customization/) ## Free trading strategies Strategies from this repo are free to use, though they are provided as-is and without any warranty. They also mostly should serve as a starting point for your own strategies, not as "ready to use" strategies. Feel free to use and/or update them to your likings. Some may only work in specific market conditions, while others are more "general purpose" strategies. It's noteworthy that further optimization to the exchange and Pairs used will usually result in better outcomes. Please keep in mind, results will heavily depend on the pairs, timeframe and timerange used to backtest - so please run your own backtests that mirror your usecase, to evaluate each strategy for yourself. The results above should serve as a general outline to demonstrate the number of trades to expect. Actual performance will be different based on various factors. ## Share your own strategies and contribute to this repo Feel free to send your strategies, comments, optimizations and pull requests via an [Issue ticket](https://github.com/freqtrade/freqtrade-strategies/issues/new) or as a [Pull request](https://github.com/freqtrade/freqtrade-strategies/pulls) enhancing this repository. ## FAQ ### What is Freqtrade? [Freqtrade](https://github.com/freqtrade/freqtrade) Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning. ### What includes these strategies? Each Strategies includes: - [x] **Minimal ROI**: Minimal ROI optimized for the strategy. - [x] **Stoploss**: Optimal stoploss. - [x] **Buy signals**: Result from Hyperopt or based on existing trading strategies. - [x] **Sell signals**: Result from Hyperopt or based on existing trading strategies. - [x] **Indicators**: Includes the indicators required to run the strategy. Best backtest multiple strategies with the exchange and pairs you're interested in, and fine tune the strategy to the markets you're trading. ### How to install a strategy? First you need a [working Freqtrade](https://freqtrade.io). Once you have the bot on the right version, follow this steps: 1. Select the strategy you want. All strategies of the repo are into [user_data/strategies](https://github.com/freqtrade/freqtrade-strategies/tree/main/user_data/strategies) 2. Copy the strategy file 3. Paste it into your `user_data/strategies` folder 4. Run the bot with the parameter `--strategy ` (ex: `freqtrade trade --strategy Strategy001`) More information [about backtesting](https://www.freqtrade.io/en/latest/backtesting/) and [strategy customization](https://www.freqtrade.io/en/latest/strategy-customization/). ### How to test a strategy? Let assume you have selected the strategy `strategy001.py`: #### Simple backtesting ```bash freqtrade backtesting --strategy Strategy001 ``` #### Refresh your test data ```bash freqtrade download-data --days 100 ``` *Note:* Generally, it's recommended to use static backtest data (from a defined period of time) for comparable results. Please check out the [official backtesting documentation](https://www.freqtrade.io/en/latest/backtesting/) for more information. --- ### Overview (`user_data/strategies/futures/Readme.md`) # all strategies are tested against this config. Tests only done on binance futures ``` /* Detailed source-code truncated for AI context efficiency. */ ``` --- ### Overview (`user_data/strategies/lookahead_bias/readme.md`) Warning, Strategies in this folder do have a lookahead bias. Please see these as practice to see if you can spot the lookahead bias.
Expand for spoilers / solution Please Click on each strategy to see details of the mistakes.
DevilStra `normalize()` uses `.min()` and `.max()`. This uses the full dataframe, not just past data.
GodStraNew `normalize()` uses `.min()` and `.max()`. This uses the full dataframe, not just past data.
Zeus uses `.min()` and `.max()` to normalize `trend_ichimoku_base` as well as `trend_kst_diff`.
wtc ``` python min_max_scaler = preprocessing.MinMaxScaler() x_scaled = min_max_scaler.fit_transform(x) ``` Using a MinMaxScaler will automatically take the absolute maximum and minimum of a series.
--- --- METRICS --- - Files Extracted: 3 - Estimated Token Budget: ~1466 tokens - Canonical Reference: https://codewiki.google/github.com/freqtrade/freqtrade-strategies