🔎 📈 🐍 💰 Backtest trading strategies in Python.
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Updated
Aug 5, 2026 - Python
🔎 📈 🐍 💰 Backtest trading strategies in Python.
Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books(Level-2 and Level-3), with real-world crypto trading examples for Binance and Bybit
QuantStart.com - QSTrader backtesting simulation engine.
Open-source Rust framework for building event-driven live-trading & backtesting systems
An extension for Nautilus Trader
Backtesting & live trading engine built for AI agents. Zipline rebuilt on Polars with a native MCP server for Claude, Cursor & Codex. Stocks, ETFs, futures.
Extremely fast quantitative factor cleaning and backtesting library, based on Polars
A stock backtesting engine written in Java. And a pairs trading (cointegration) strategy implementation using a bayesian kalman filter model
SimTradeLab is an open-source backtesting framework inspired by PTrade’s event-driven architecture. It features a lightweight, modular design and full syntax compatibility, enabling seamless strategy development and validation.
Option and stock backtester / live trader
Pine Script-compatible Python runtime for indicators and strategies, validated against TradingView on real published scripts
Quantitative systematic trading strategy development and backtesting in Julia
Backtesting framework for modern option strategies
Hybrid Event-driven and Vectorized Strategy Backtesting Library
Sub-microsecond bare-metal execution engine with deterministic replay, lock-free order path, and hardware-timestamped latency measurement.
Python 开源量化框架: 覆盖“研究、回测、交易”全生命周期,构建从零到实盘的工业级量化闭环工作流。
Python based open source quantitative trading platform development framework
High performance, low-latency backtesting engine for testing quantitative trading strategies on historical and live data in Rust
AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.
Run Pine Script® indicators and strategies from your terminal. Point it at any .pine file and get JSON output — live market data or your own dataset, no project setup. Built on PineTS.
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