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: Apr 7, 2026

GitHub Statistics

Community Sentiment

Community Buzz: Reddit users praise backtesting.py for its ease of use, as one user stated 'I love prototyping strategies on TV then do more rigorous backtesting and validation with out of sample data in python' from Reddit. Additionally, a Twitter user mentioned 'the system takes that pine script and converts it into pure python code using libraries like pandas ta and backtesting py' from X/Twitter

Pros & Cons

What People Love

Ease of use, Flexibility in backtesting strategies, Compatibility with other libraries like pandas and vectorbt, Reddit users praise its ability to do walk forward optimization

Common Complaints

Technical issues, Limited documentation

Biggest Positive: Easy to use

Biggest Negative: Technical 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

Backtesting.py is a popular Python library for backtesting trading strategies, competing with other libraries such as Backtrader and Pineify