Backtesting.py — AI Agent Review & Live Stats

Live GitHub stats, community sentiment, and trend data for Backtesting.py. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Dec 20, 2025 • Sentiment updated: Mar 17, 2026

GitHub Statistics

Community Sentiment

Community Buzz: The project has a strong focus on algo-trading and backtesting, indicating a dedicated community of developers and traders who are actively contributing to and using the framework.

Why Backtesting.py Stands Out

Backtesting.py stands out from alternative backtesting frameworks due to its blazing fast execution, built-in optimizer, and library of composable base strategies and utilities. Its simple, well-documented API makes it easy to define and test trading strategies, and its support for any financial instrument with candlestick data makes it highly versatile. Additionally, its detailed results and interactive visualizations provide a comprehensive understanding of strategy performance. By leveraging these features, users can quickly and accurately evaluate and refine their trading strategies.

Built With

Build a trading strategy backtester — backtesting.py enables it by providing a simple, well-documented API for defining and testing trading strategies, Build a custom technical indicator library — backtesting.py allows it by being indicator-library-agnostic, supporting any financial instrument with candlestick data, Build an automated trading system — backtesting.py facilitates it by providing a built-in optimizer and detailed results for strategy evaluation, Build a portfolio optimization tool — backtesting.py enables it by supporting the backtesting of multiple strategies and providing interactive visualizations, Build a risk management system — backtesting.py allows it by providing metrics such as exposure time, equity final, and drawdown duration

Getting Started

  1. Install backtesting.py using pip: $ pip install backtesting
  2. Import the necessary modules: from backtesting import Backtest, Strategy
  3. Define a trading strategy by creating a class that inherits from Strategy: class SmaCross(Strategy):
  4. Configure the backtest by specifying the data, strategy, and other parameters: bt = Backtest(GOOG, SmaCross, commission=.002, exclusive_orders=True)
  5. Try running the backtest and plotting the results to verify it works: stats = bt.run() and bt.plot()

About

🔎 📈 🐍 💰 Backtest trading strategies in Python.

Official site: https://kernc.github.io/backtesting.py/

Category & Tags

Category: trading

Tags: algo-trading, algorithmic-trading, backtesting, backtesting-engine, backtesting-frameworks, backtesting-trading-strategies, finance, financial-markets, forex, forex-trading, framework, hacktoberfest, investing, investment, investment-strategies, stocks, trading, trading-algorithms, trading-simulator, trading-strategies

Market Context

This project is relevant to the finance and investing market, providing a framework for traders and developers to test and optimize their strategies.