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quantstats

Backtesting & trading frameworks★ 7,693 GitHub starsPython⏱ Last commit 14 days ago

Portfolio analytics and performance tear sheets for quants

What it is

A Python library for portfolio profiling that gives quants and portfolio managers in-depth performance and risk metrics. It provides three modules: stats for metrics such as Sharpe ratio, volatility and win rate; plots for visualizing performance, drawdowns and rolling statistics; and reports for generating metrics tear sheets, batch plots and full HTML reports, plus built-in Monte Carlo simulations. It works well for systematic and algorithmic strategies with regular rebalancing, since it analyzes return series rather than discrete trades. Metrics like win rate and payoff ratio are period-based, so they may differ from trade-level statistics for discretionary traders with multi-day trades.

At a glance

RecommendedOur rating, based on popularity, maintenance and how ready it is for real use.

Best forProfessional quants
Used forData analysis, Strategy research
MarketsMulti-market
StackPython
Learning curveEasy to start
Practical valueHigh practical value
CostFree and open source
HardwareNo GPU needed
MaintenanceLast commit 14 days ago

GitHub stars, last 30 days

Daily snapshots since 2026-09-12 (up to 30 days): +67 over the period, now 7,693. Gaps mean no snapshot was taken that day.

In the author's words

Portfolio analytics for quants, written in Python.

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