Backtesting.py

The smallest complete step from an idea to a measured backtest. A strategy is a Strategy subclass with init and next, placing buy and sell orders against simulated fills with commission; the framework returns trade-level results and a statistics block. It is deliberately indicator-library-agnostic — bring whatever you already use — and ships composable base strategies in backtesting/lib.py, a parameter optimizer built on SAMBO, and interactive Bokeh charts. Any instrument with OHLC(V) candles works, and sample data ships inside the package as backtesting/test/ (GOOG.csv, EURUSD.csv, BTCUSD.csv), so a first end-to-end run needs no data source, account or API key.

The returned stats cover Sharpe, Sortino and Calmar ratios, max drawdown, alpha and beta, profit factor, SQN and the Kelly criterion — and, usefully, “Buy & Hold Return [%]” sits in the same block as the strategy’s own return, so the comparison that decides whether a strategy was worth running is present by default rather than something you remember to compute.

The license is AGPL-3.0, in a file named LICENSE.md rather than the usual LICENSE. That is network copyleft: embedding this in a hosted backtesting service triggers source-disclosure obligations toward that service’s users. It is a single-maintainer project, funded through GitHub Sponsors and actively developed — the current version is 0.6.6, published to PyPI on 2026-07-22, with versioning by git tag rather than GitHub release objects.