{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/13"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Financial","url":"https://skillfed.io/packages/category/office-business-financial"},{"label":"Investment","url":"https://skillfed.io/packages/category/office-business-financial-investment"}],"enrichment":{"capability":"QuantStats computes portfolio performance metrics (Sharpe ratio, volatility, drawdown, win rate) and generates visualizations and HTML tearsheets for analyzing investment returns.","skillfed_tags":["portfolio-analysis","quantitative-finance","risk-metrics"],"use_cases":["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."],"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.\n\nThe package wraps eight core dependencies\u2014numpy, pandas, scipy, matplotlib, seaborn, tabulate, yfinance, and python-dateutil\u2014to 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()`.","worth_installing":"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\u2014if 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."},"id":"quantstats","links":{"html":"https://skillfed.io/packages/quantstats","md":"https://skillfed.io/packages/quantstats.md","pypi":"https://pypi.org/project/quantstats/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-13","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"quantstats","python_support":"supports_current","summary":"Portfolio analytics for quants"},"popularity":{"monthly_downloads":315897,"position":7682,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.81"}