freqtrade-strategies Documentation

Technical Documentation: freqtrade/freqtrade-strategies

ℹ️ Provenance: Multi-Tier Ingestion · CodeWiki Reference

Overview (`README.md`) Copy Section

Freqtrade strategies

This Git repo contains free buy/sell strategies for Freqtrade.

All strategies should work with a freqtrade version of 2022.4 or newer.

Disclaimer Copy Chapter

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 Copy Chapter

• Free trading strategies
• Contribute
• FAQ
  • What is Freqtrade?
  • What includes these strategies?
  • How to install a strategy?
  • How to test a strategy?
  • How to create/optimize a strategy?

Free trading strategies Copy Chapter

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 Copy Chapter

Feel free to send your strategies, comments, optimizations and pull requests via an
Issue ticket or as a Pull request enhancing this repository.

FAQ Copy Chapter

What is Freqtrade? Copy Section

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? Copy Section

Each Strategies includes:

• Minimal ROI: Minimal ROI optimized for the strategy.
• Stoploss: Optimal stoploss.
• Buy signals: Result from Hyperopt or based on existing trading strategies.
• Sell signals: Result from Hyperopt or based on existing trading strategies.
• 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? Copy Section

First you need a working Freqtrade.

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
2. Copy the strategy file
3. Paste it into your user_data/strategies folder
4. Run the bot with the parameter --strategy <STRATEGY CLASS NAME> (ex: freqtrade trade --strategy Strategy001)

More information about backtesting and strategy customization.

How to test a strategy? Copy Section

Let assume you have selected the strategy strategy001.py:

Simple backtesting

freqtrade backtesting --strategy Strategy001

Refresh your test data

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 for more information.

Overview (`user_data/strategies/futures/Readme.md`) Copy Section

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`) Copy Section

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

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