quantstats
Portfolio analytics for quants
Decision gist · record as of 2026-08-14
Yes. QuantStats is actively maintained, has no known vulnerabilities, uses permissive licensing, and installs with low friction. It is well-suited if you need to compute standard portfolio metrics and generate tearsheets. Be aware that it operates on return periods, not discrete trades—if you trade multi-day positions, period-based metrics like win rate will differ from trade-level statistics. Verify that its metric definitions align with your risk model before relying on it for critical decisions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10.
- yfinance dependency needs internet access to download market data.
- Low friction installation with a pure-Python wheel.
License · maintenance · safety
Apache-2.0 (permissive) — Distributed under Apache-2.0 (permissive). You can use, modify, and distribute QuantStats freely in commercial and private projects with minimal restrictions.
last release 2026-01-13 (213 days) · last repo commit 2026-07-20 · 7,547 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 315,897 downloads/mo, #7,682 on PyPI
Alternatives
Verify before relying
pip install quantstats
import quantstats as qs
stock = qs.utils.download_returns('META')
sharpe = qs.stats.sharpe(stock)
qs.plots.snapshot(stock, title='Performance', show=True)
qs.reports.html(stock, 'SPY')- Whether Monte Carlo simulations (bust_probability, goal_probability) are suitable for your risk model assumptions.
- How period-based metrics (win rate, consecutive wins) map to your specific trading workflow if you use multi-day trades.
- Performance characteristics with very large return series or high-frequency data.
What it is and what it does
QuantStats is a Python library for analyzing and visualizing portfolio returns. It calculates standard performance metrics like Sharpe ratio, Sortino ratio, maximum drawdown, volatility, and win rate from a time series of returns, then visualizes them via matplotlib and seaborn plots or generates interactive HTML tearsheets for comparison against benchmarks. The library operates on return periods (daily, weekly, monthly) rather than discrete trades, making it well-suited for systematic strategies with regular rebalancing.
The package wraps eight core dependencies—numpy, pandas, scipy, matplotlib, seaborn, tabulate, yfinance, and python-dateutil—to provide three main modules: `stats` for metric calculation, `plots` for visualization, and `reports` for batch analysis and tearsheet generation. It includes Monte Carlo simulation for probabilistic risk analysis and can extend pandas DataFrames with convenience methods like `.sharpe()` and `.plot_snapshot()`.
Use it for
- Calculate and compare Sharpe ratio, Sortino ratio, and other risk-adjusted returns for a strategy against a benchmark.
- Generate an HTML tearsheet showing monthly returns, drawdowns, rolling volatility, and performance metrics for stakeholder reporting.
- Run Monte Carlo simulations to estimate bust probability and goal probability for a portfolio under different market scenarios.
- Visualize daily returns, drawdown periods, and rolling statistics to identify performance patterns and risk periods.
- Analyze win rate, consecutive wins/losses, and profit factor for systematic trading strategies on a period-by-period basis.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
QuantStats is actively maintained, has no known vulnerabilities, uses permissive licensing, and installs with low friction. It is well-suited if you need to compute standard portfolio metrics and generate tearsheets. Be aware that it operates on return periods, not discrete trades—if you trade multi-day positions, period-based metrics like win rate will differ from trade-level statistics. Verify that its metric definitions align with your risk model before relying on it for critical decisions.
Install
quantstats on PyPI
Before you install
Low friction installation with a pure-Python wheel. Actively maintained with recent commits and a large community (7547 stars). Requires Python >= 3.10 and eight runtime dependencies including numpy, pandas, scipy, matplotlib, and yfinance—all stable, widely-used libraries.
Requires Python >= 3.10. yfinance dependency needs internet access to download market data.
License in practice
Distributed under Apache-2.0 (permissive). You can use, modify, and distribute QuantStats freely in commercial and private projects with minimal restrictions.
Quickstart
pip install quantstats
import quantstats as qs
stock = qs.utils.download_returns('META')
sharpe = qs.stats.sharpe(stock)
qs.plots.snapshot(stock, title='Performance', show=True)
qs.reports.html(stock, 'SPY')Verify before relying
- Whether Monte Carlo simulations (bust_probability, goal_probability) are suitable for your risk model assumptions.
- How period-based metrics (win rate, consecutive wins) map to your specific trading workflow if you use multi-day trades.
- Performance characteristics with very large return series or high-frequency data.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesmatplotlibnumpypandaspython-dateutilscipyseaborntabulateyfinance |
| Maintenance | Actively maintained 213 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 315,897 / month, #7,682 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Office/Business :: FinancialTopic :: Office/Business :: Financial :: InvestmentTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: MathematicsTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: quantstats-0.0.81-py3-none-any.whl
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