backtesting-tools

Python backtester for intraday gold (XAUUSD) strategies: minute-by-minute simulation with real spread, conservative fills, and a built-in look-ahead test that proves results don’t use future prices. Includes honest research results.

Free and source-available, self-hostable trading workspace: data, backtest, trading journal, news aggregator, rich-doc editor, portfolio, 20+ modules with a built-in MCP server and AI agent.

Search moving-average crossover grids exactly, find top in-sample systems, and measure winner confidence despite correlated testing.

Official Python backtesting library for PolyOrderbooks historical Polymarket order-book data.

Runnable strategy backtests on PolyOrderbooks historical Polymarket order books.

Free market data samples and reproducible Python examples for backtesting and quantitative research.

Backtest any MT5 indicator through Claude AI — MCP server that reads real indicator signals and replays them on live broker data

How many trades should you backtest? The sample-size math behind the interactive tool at thebacktestinglab.com: win-rate bands, required trades for a target precision, and chart geometry. Framework-free TypeScript, no dependencies.

Fast exact grid search and backtesting of moving-average crossover strategies, with vectorized evaluation and multiple-testing-aware Sharpe diagnostics.

QuantJourney Backtester

Automated trading bot that opens and closes positions in real time.

Temporal partitioning and backtesting for time-correlated datasets

An extension for Nautilus Trader

Free backtesting app https://strategytune.com

wip backtesting framework for my personal use

🎸 Projects in this repository cover various trading concepts, utilize multiple backtesting libraries, and employ several ways to validate testing.

tradingview python automated strategy engine

Lightweight Python Market-data CLI to normalize & store top-of-book, then replay with timing for execution backtests

Lookback - C++ Backtesting Library

Fast exact finite-sample back-testing for Value-at-Risk (VaR) models in R.