Backtest Framework

This Python backtesting framework allows you to evaluate trading strategies on historical market data. It supports stop-loss (SL), take-profit (TP), trailing stop (TS), and various risk management features. You can backtest both long and short strategies with configurable settings for transaction costs and balance management.

Features Backtest long and short trading strategies Configurable stop-loss (SL) types: ATR-based, standard deviation (SD)-based, and percentage-based Take-profit and trailing stop functionality Risk management through percentage-based position sizing Transaction costs handling Equity curve and drawdown tracking Detailed trade history, including entry and exit points, stop-loss updates, and trade returns

License

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means.

In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit of the public at large and to the detriment of our heirs and successors. We intend this dedication to be an overt act of relinquishment in perpetuity of all present and future rights to this software under copyright law.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

For more information, please refer to https://unlicense.org

Installation

Install cerebro_backtester with pip

  pip install cerebro-backtester

Usage/Examples

Example 1: Simple Long Strategy with ATR-based Stop Loss

import pandas as pd
from backtest import Backtest

# Example historical data
data = pd.DataFrame({
    'Close': [100, 102, 101, 104, 106],
    'High': [101, 103, 102, 105, 107],
    'Low': [99, 100, 100, 103, 105],
    'buy_signal': [1, 0, 0, 1, 0],
    'sell_signal': [0, 0, 0, 0, 1]
})

# Configure and run the backtest
backtest = Backtest(
    info=data,
    side='long',
    SL_type='atr',
    SL=2,  # 2x ATR for stop-loss
    SL_spike_out=True,
    TP=3,  # 3x ATR for take-profit
    TS=True  # Enable trailing stop
)
backtest.run()

# Analyze results
print(backtest.info[['equity_curve', 'drawdown', 'trade_return_hist']])

Example 2: Short Strategy with Standard Deviation-based Stop Loss

import pandas as pd
from backtest import Backtest

# Example historical data
data = pd.DataFrame({
    'Close': [100, 102, 101, 98, 96],
    'High': [101, 103, 102, 100, 97],
    'Low': [99, 100, 100, 95, 94],
    'buy_signal': [0, 0, 0, 1, 0],
    'sell_signal': [1, 0, 1, 0, 0]
})

# Configure and run the backtest
backtest = Backtest(
    info=data,
    side='short',
    SL_type='sd',
    SL=1,  # 1x standard deviation for stop-loss
    SL_spike_out=False,
    TP=2,  # 2x standard deviation for take-profit
    TS=False  # Disable trailing stop
)
backtest.run()

# Analyze results
print(backtest.info[['equity_curve', 'drawdown', 'trade_return_hist']])

Authors

Nathan Schmidt is a programmer with years of experience in software development. Specializing in innovative programming solutions, Nathan has a commitment to open-source development and community collaboration. Known for his passion for clean code and efficiency, Nathan continues to contribute to the field of software engineering with a focus on impactful, real-world applications.

Support

For support, email nathan.schmidt.ns89@gmail.com or raise a Github issue.