{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Computes financial return and risk metrics\u2014including drawdown, alpha/beta, Value at Risk, Sharpe and Sortino ratios\u2014from return arrays or pandas Series, with support for rolling window calculations and optional data fetching from Yahoo Finance or Fama-French sources.","skillfed_tags":["quantitative-finance","portfolio-analysis","risk-metrics"],"use_cases":["Calculate max drawdown and other risk metrics from a backtest return stream to evaluate strategy performance.","Compute alpha and beta against a benchmark to measure active management skill and systematic risk exposure.","Generate rolling Sharpe or Sortino ratios to track risk-adjusted returns over time windows.","Fetch historical S&P 500 or other asset returns from Yahoo Finance for comparative analysis.","Access Fama-French risk factors for multi-factor performance attribution studies."],"what_it_does":"Empyrical-reloaded is a quantitative finance metrics library that calculates standard performance and risk statistics from return data. It accepts numpy arrays or pandas Series and computes metrics ranging from simple statistics (max drawdown, volatility) to advanced measures (alpha, beta, Value at Risk, Sharpe and Sortino ratios). The library also supports rolling-window calculations to track metrics over time, and includes utility functions to fetch historical price data from Yahoo Finance or Fama-French risk factors.\n\nThe package is designed for portfolio analysis, backtesting workflows, and financial research. Its main dependencies are numpy, pandas, scipy, and bottleneck, making it lightweight for a quantitative finance tool. It runs on Python 3.10\u20133.13 and is permissively licensed under Apache 2.0, so it can be used in commercial and proprietary projects. The aging maintenance status (last release 439 days ago) means new features are unlikely, but the codebase is stable and the repository remains active.","worth_installing":"Yes, if you need standard quantitative finance metrics and accept aging maintenance. The library is stable, dependency-light, and permissively licensed. Install it for portfolio analysis, backtesting, or academic finance work. Avoid it if you require active development, cutting-edge risk models, or real-time data integration beyond Yahoo Finance."},"id":"empyrical-reloaded","links":{"html":"https://skillfed.io/packages/empyrical-reloaded","md":"https://skillfed.io/packages/empyrical-reloaded.md","pypi":"https://pypi.org/project/empyrical-reloaded/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-06-01","license_spdx":null,"license_treatment":"permissive","name":"empyrical-reloaded","python_support":"supports_current","summary":"empyrical computes performance and risk statistics commonly used in quantitative finance"},"popularity":{"monthly_downloads":161706,"position":10624,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.12"}