is an open-source Python library that is designed to try helping in the process of backtesting trading strategies. Backtesting is a technique where a strategy is tested on historical data, to see how it would have performed. This is an essential step for traders and quants in developing and refining trading algorithms. tries to aim to simplify this process by providing a concise and straightforward API to work with. It tries to enable rapid prototyping of trading strategies and comes with handy visualization tools that try to provide insights into how a strategy performed over time. By employing a vectorized approach, it tries to offer a performance advantage over traditional iterative backtesting methods.

This library is well-suited for anyone looking to design and test quantitative trading strategies without delving into complicated infrastructural details. With minimal configuration, one can run backtests on historical price data, optimize strategies, and assess their risk and performance characteristics.


  • Simple API: is designed with a clear and straightforward trading API, trying to make it easy for users to create and test trading strategies without hassle.
  • Vectorized Backtesting: Unlike conventional methods, this library tries to employ a vectorized approach for backtesting in Python, speeding up calculations by operating on whole data arrays at once.
  • Visualization Tools: Built-in tools try to provide visual representations of various performance aspects, such as equity curves, drawdowns, and trades, trying to enable a more intuitive understanding of a strategy’s behavior.
  • Extensive Metrics: A wide range of metrics is available for analyzing a strategy’s performance, including essential statistics like Sharpe ratio, annual returns, and maximum drawdowns.
  • Custom Indicators and Strategies: Users have the flexibility to define custom trading rules and indicators, trying to allow for more complex and tailored trading strategies.
  • Optimization Tools: The library tries to provide optimization features to fine-tune strategy parameters, trying to aid in the search for optimal performance settings.
  • Ease of Use: With minimal configuration and code, users can start backtesting strategies, making it an accessible tool for both beginners and experienced traders.



Install the library via pip:

pip install backtesting.

Strategy Definition

Define your trading strategy by subclassing the Strategy class and implementing your logic in the init and next methods.

Running the Backtest

Use the Backtest class to run the backtest on historical data. You can set parameters like initial capital, commission, and slippage.

Visualization and Metrics

After running the backtest, you can utilize built-in methods to visualize results and gather performance metrics.


The library tries to allow optimization of strategy parameters using methods like optimize, helping you find the best configuration for your strategy.


From backtesting import Backtest, Strategy

class MyStrategy(Strategy):
def init(self):
# Define indicators here
def next(self):
# Define strategy logic here

bt = Backtest(data, MyStrategy, cash=10000)
stats =


  • Simplicity Over Complexity: While its straightforward and simple API is an advantage for quick prototyping, it might not be suitable for highly complex or advanced trading strategies.
  • Lack of Support for Some Asset Types: primarily tries to focus on traditional financial instruments like stocks and commodities. Support for more complex derivatives or asset types may be lacking.
  • Limited Risk Management Tools: While the library tries to provide basic metrics for evaluating a strategy, it might not include more advanced risk management or portfolio optimization features found in some professional-level tools.
  • Scalability Concerns: The library is optimized for ease of use and quick development, but it might not be suitable for large-scale, high-frequency trading simulations where ultra-high performance is required.
  • Lack of Live Trading Integration: is designed for backtesting on historical data, and as of the last update, there is no built-in support for live trading or seamless integration with live trading platforms.
  • Limited Documentation and Community Support: Though sufficient for most common use cases, the documentation and community support might be limited compared to other more extensive frameworks, possibly hindering the development of more sophisticated strategies.

Final Thoughts

In conclusion, tries to stand as a user-friendly and efficient tool for backtesting trading strategies in the Python programming environment. With its clear API, vectorized calculations, and built-in visualization and metrics tools, it tries to democratize access to backtesting, trying to enable both novice and experienced traders to develop and refine trading algorithms.

While perfect for simple to moderate strategies and rapid prototyping, it does have some limitations. These include restricted support for highly complex strategies, certain asset types, advanced risk management, and large-scale simulations.

Nonetheless, the library’s ease of use, flexibility, and speed make it an asset for many in the trading community. Whether you’re a hobbyist experimenting with trading ideas or a professional quant, backtesting.pytries to provide a straightforward path to testing and analyzing trading strategies without delving into cumbersome details. For many users, its strengths will outweigh its limitations, making it a go-to tool in the algorithmic trading toolkit.

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