https://medium.com/@tiwarilaxuu/list-of-backtesting-library-2191832c00ed
There are several popular libraries for back-testing trading strategies in Python, each with its own strengths and capabilities. Some of the most widely used libraries include:
This is a popular and flexible library for back-testing trading strategies. It supports multiple data sources, indicators, and strategy development. https://www.backtrader.com/
This is another well-known library that provides a simple and event-driven back-testing framework for trading strategies. https://gbeced.github.io/pyalgotrade/
Developed by Quantopian, Zipline is a powerful library that allows for back-testing and live-trading algorithmic strategies. It is widely used in the quantitative finance community. https://zipline.ml4trading.io/
This is an open-source algorithmic trading platform that provides a powerful back-testing engine and supports multiple asset classes. https://www.quantconnect.com/
This is a flexible back-testing library that allows for easy manipulation of data and strategy development. https://pmorissette.github.io/bt/
FreqTrade is a free and open-source trading bot platform with support for backtesting, paper trading, and live trading on multiple exchanges. It’s highly customizable and suitable for traders seeking full control over their trading strategies. FreqTrade’s active community and continuous development make it a popular choice among enthusiasts. https://www.freqtrade.io/en/latest/backtesting/
Pinkfish is a user-friendly backtesting library that emphasizes simplicity and practicality. It offers features for position sizing, risk management, and portfolio optimization, empowering traders to build robust and well-managed portfolios. Pinkfish’s intuitive interface makes it accessible to traders of all skill levels. https://github.com/fja05680/pinkfish
Systematic Trading in python from book Systematic Trading by Rob Carver. https://github.com/robcarver17/pysystemtrade
Quantiacs is a platform tailored for backtesting and live trading of quantitative trading strategies. It offers historical market data and provides a platform for developing and deploying algorithms. Quantiacs’ focus on quantitative strategies makes it a valuable resource for traders seeking data-driven approaches to trading. https://quantiacs.com/
A extendable, replaceable Python algorithmic backtest & trading framework supporting multiple securities. https://github.com/ricequant/rqalpha When choosing a library, it’s important to consider factors such as the specific requirements of your trading strategy, the ease of use, and the community support behind the library. Each of these libraries has its own strengths and weaknesses, so it’s worth exploring them to find the best fit for your needs.
The most honest approach to developing trading models is to create your own tool that addresses real issues such as liquidity, costs, and distribution. Many quantitative models fail because their creators misinterpret the results and fall victim to confirmation bias. Often, they rely on out-of-sample data that yields impressive returns and high r-squared values, leading them to invest based on flawed assumptions.
To ensure proper back-testing and avoid falling into the trap of overconfidence, it’s essential to develop your own tool. This approach fosters honesty in the development process and helps mitigate the “God complex” that many quants struggle with. By crafting a tool tailored to your specific needs, you can accurately assess the performance and viability of your trading strategies.
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