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Backtesting.py Review

Backtesting.py is a Python framework designed for evaluating trading strategies using historical data and providing interactive visualizations for performance analysis.

shipped Sep 15, 2026researchfree
Domain rating94Monthly visits338/mo
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Backtesting.py — product screenshot

Why it matters

1Open-source Python library under the AGPL-3.0 license.
2Features a built-in SAMBO optimizer for testing hundreds of strategy variants.
3Integrates with technical analysis libraries such as TA-Lib, Tulip, and pandas-ta.
4Supports backtesting for forex, crypto, stocks, and futures candlestick data.

Specs

GitHub

View Repository →

API Available

Yes, public API

overview

What is Backtesting.py?

Backtesting.py is a Python framework tool developed by Kernc that enables quantitative analysts and traders to evaluate trading strategies using historical data. It allows users to test the viability of mechanical or algorithmic trading systems before live deployment, providing interactive visualizations to aid in the analysis of strategy performance. The framework requires Python coding for strategy creation, offering flexibility for users comfortable with programming. It integrates with technical analysis libraries and focuses on research, providing a clean API for backtesting without direct live broker connections.

features

Key Features of Backtesting.py

Backtesting.py offers a comprehensive set of features for the development and analysis of trading strategies. Its architecture is built upon established Python libraries, ensuring both performance and ease of use for developers.

  • Interactive visualizations using Bokeh for detailed analysis of trades and equity curves.
  • Integration with technical analysis libraries including TA-Lib, Tulip, and pandas-ta.
  • Built-in SAMBO optimizer for efficient parameter optimization and heatmap generation.
  • High-level API supporting concepts like market timing, swing trading, money management, stop-loss/take-profit, and leverage.
  • Support for vectorized backtesting, contributing to fast simulation speeds.
  • Composable strategies with predefined utilities for streamlined development.
  • Compatibility with historical candlestick data (OHLCV) for forex, crypto, stocks, and futures.
  • Small, clean API designed for ease of understanding and rapid strategy implementation.
  • Blazing fast performance, leveraging Pandas, NumPy, and Bokeh for core operations.

use cases

Who Should Use Backtesting.py?

Backtesting.py is primarily designed for individuals and professionals involved in algorithmic trading and quantitative finance who require a robust, flexible, and free tool for strategy validation.

  • Strategy Developers: For testing the viability of new mechanical or algorithmic trading systems using historical data before live deployment.
  • Quantitative Analysts: For in-depth research and analysis of strategy performance, including metrics like total return, maximum drawdown, and Sharpe ratio.
  • Python Programmers: Users comfortable with Python coding who seek a flexible framework for creating and refining custom trading strategies.
  • Financial Researchers: For educational purposes and experimenting with backtesting concepts due to its user-friendly API and clear documentation.
  • Algorithmic Traders: For optimizing existing strategies and identifying optimal parameters through its built-in SAMBO optimizer.

how to use

How to Use Backtesting.py

To begin using Backtesting.py, users typically install the library via pip and then define their trading strategy within a Python script. The process involves loading historical data, instantiating the Backtest object, and running the backtest.

  • 1Install the library using pip install backtesting.
  • 2Import necessary modules: from backtesting import Backtest, Strategy and from backtesting.lib import crossover.
  • 3Load historical financial data, typically in OHLCV (Open, High, Low, Close, Volume) format, often using Pandas DataFrames.
  • 4Define a custom trading strategy by subclassing Strategy and implementing init() and next() methods.
  • 5Instantiate Backtest with the loaded data and the defined strategy, e.g., bt = Backtest(GOOG, SmaCross, commission=.002, exclusive_orders=True).
  • 6Execute the backtest using bt.run() and analyze the results, including performance statistics and interactive plots via bt.plot().

pricing

Backtesting.py Pricing & Plans

Backtesting.py is an open-source project distributed under the AGPL-3.0 license, making it entirely free to use. There are no paid tiers, subscriptions, or usage-based fees associated with the core library.

  • Free: Full access to all features and capabilities of the Backtesting.py library.

Pros

  • +Open-source and completely free under the AGPL-3.0 license.
  • +User-friendly API, making it accessible for beginners in algorithmic trading.
  • +Fast simulation speeds due to its vectorized approach and reliance on Pandas and NumPy.
  • +Integrated interactive visualizations (Bokeh) for clear analysis of trading results.
  • +Built-in SAMBO optimizer for efficient parameter tuning and heatmap generation.
  • +Active maintenance and recent updates, including fractional trading and improved modeling.

Cons

  • −Primarily focused on single-asset backtesting, lacking native multi-asset portfolio support.
  • −Offers basic order modeling without advanced features like complex slippage or volume constraints.
  • −May require workarounds or be less suitable for highly complex, multi-layered trading strategies.
  • −Some users suggest documentation could be more extensive for advanced customization.
  • −Charting customization options are somewhat limited compared to dedicated plotting libraries.

Similar Tools

Backtesting.py vs Competitors

Backtesting.py occupies a niche as a lightweight, fast, and user-friendly backtesting framework, often recommended for beginners in Python-based algorithmic trading. It differentiates itself through its simplicity and interactive visualizations.

1
backtraderOn Stork

A feature-rich Python framework for backtesting and trading, allowing users to focus on writing reusable trading strategies, indicators, and analyzers instead of building infrastructure.

Backtrader is more comprehensive and offers a wider range of features, including various order types and data feeds, compared to Backtesting.py. However, its extensive API can lead to a steeper learning curve for beginners.

2
PyAlgoTrade↗

An event-driven Python algorithmic trading library focused on backtesting, paper-trading, and live-trading with minimal effort.

PyAlgoTrade provides a straightforward, event-driven approach to backtesting, similar to Backtesting.py's focus on simplicity. It might offer less sophisticated built-in interactive visualizations than Backtesting.py.

3
VectorBTOn Stork

Focuses on high-performance, vectorized backtesting, enabling the testing of thousands of configurations simultaneously using NumPy and Numba.

VectorBT offers significantly faster backtesting for large datasets and complex parameter sweeps due to its vectorized approach, which differs from Backtesting.py's more sequential processing. The free version is in maintenance mode, with active development in the paid Pro version.

See every Backtesting.py alternative, compared→

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