Backtesting.py — AI Agent Framework: Live Stats & TrendScore

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: Jun 21, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As seen on GitHub, 'Backtesting.py' is described as a 'robust workflow for medium- and long-term stock/index strategies' that can 'backtest, evaluate and then generate recommendations' - this sentiment is echoed on Dev.to where users discuss its applications, for instance, 'I built a version manager for llama.cpp using nothing but vibe coding' is not directly related but shows the overall excitement around such projects.

Pros & Cons

What People Love

Customizable, Powerful backtesting capabilities, Ease of integration with other tools

Common Complaints

Performance issues, Steep learning curve

Biggest Positive: Powerful Backtesting

Biggest Negative: Performance Issues

Why Backtesting.py Stands Out

Backtesting.py takes a unique technical approach by providing a simple, well-documented API and a built-in optimizer, which makes it easier to create and test complex trading strategies. Its blazing fast execution and indicator-library-agnostic design also make it an attractive choice for users. Additionally, Backtesting.py's focus on detailed results and interactive visualizations sets it apart from other backtesting frameworks. This project has solved the problem of creating a user-friendly yet powerful backtesting tool that can handle complex strategies and provide actionable insights.

Built With

Build a research agent that reads 50 papers and writes a literature review — Backtesting.py chains search, extraction, and synthesis agents automatically, Build a trading simulator that generates realistic market data — Backtesting.py allows users to create custom indicators and strategies, Build a portfolio optimization tool that maximizes returns while minimizing risk — Backtesting.py includes a built-in optimizer, Build a data analytics platform that provides interactive visualizations of financial data — Backtesting.py supports detailed results and interactive visualizations, Build a machine learning model that predicts stock prices based on historical data — Backtesting.py supports any financial instrument with candlestick data

Getting Started

  1. $ pip install backtesting
  2. Import the necessary libraries: from backtesting import Backtest, Strategy from backtesting.lib import crossover from backtesting.test import SMA, GOOG
  3. Create a new strategy class that inherits from backtesting.Strategy: class SmaCross(Strategy): def init(self): price = self.data.Close self.ma1 = self.I(SMA, price, 10) self.ma2 = self.I(SMA, price, 20)
  4. Initialize a Backtest object with the strategy and data: bt = Backtest(GOOG, SmaCross, commission=.002, exclusive_orders=True)
  5. Run the backtest and plot the results: stats = bt.run() 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

Competitive with other backtesting frameworks like Backtrader and PyAlgoTrade