QuantStats

QuantStats is a Python library for portfolio analytics. It calculates performance and risk metrics such as Sharpe and Sortino ratios, drawdowns, volatility and value at risk, draws the standard performance charts, and builds complete HTML tear-sheet reports from a single line of code. Analysts, students, educators and independent and professional investors use it to understand how a strategy or portfolio really performed.

As of October 2026: about 7,700 GitHub stars, 1,260 forks, 36 contributors, 82 releases on PyPI since May 2019, and about 563,000 PyPI downloads in the last month. GitHub reports about 1,470 repositories and 90 packages that depend on it.

People make real decisions based on these numbers, so correctness matters more than features. Recent fixes, such as cross-column contamination in multi-asset inputs and rolling alpha calculations, show how subtle these bugs can be.

What the funding pays for (plan: quantstats-maintenance, USD 50,000 per year):
- Regular paid maintainer time to review and land or close the 21 open pull requests and 5 open issues, then keep the queue short.
- Correctness: a numeric test suite that checks every metric against reference implementations and published formulas.
- Compatibility with current and upcoming pandas, NumPy, matplotlib and Python releases, tested in CI.
- Security and supply chain: PyPI trusted publishing, build provenance, dependency scanning, and a security policy.
- Documentation for every metric, with its formula, assumptions and an example.
- Regular releases with changelogs.