How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Common financial risk and performance metrics. Used by zipline and pyfolio.
The main features of quantopian/empyrical are: Financial Analytics, Portfolio Optimization, Quantitative Research Tools.
Projects with overlapping indexed features include: quantopian/pyfolio — Portfolio and risk analytics in Python. jankrepl/deepdow. quantopian/alphalens — Alphalens is a quantitative alpha factor analysis library designed to measure the predictive power of financial… dcajasn/riskfolio-lib — Riskfolio-Lib is a Python portfolio optimization library and convex risk management tool. It provides a framework for… robertmartin8/pyportfolioopt — PyPortfolioOpt is a comprehensive portfolio optimization library for Python that provides a full suite of methods for… ssantoshp/empyrial — An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎.
Portfolio and risk analytics in Python
Riskfolio-Lib is a Python portfolio optimization library and convex risk management tool. It provides a framework for calculating optimal asset allocations using convex risk measures and mathematical programming solvers, supporting linear, quadratic, and semidefinite programming. The library features a hierarchical risk parity framework and financial asset clustering tools to group similar instruments and improve diversification. It includes a portfolio backtesting engine for simulating investment strategies using historical data and cross-validation. The system covers a broad range of quant
Alphalens is a quantitative alpha factor analysis library designed to measure the predictive power of financial factors. It serves as a computational toolset for processing financial time series and calculating performance metrics to evaluate quantitative trading hypotheses. The library distinguishes itself through the use of quantile-based data binning to analyze return distributions across different factor strength levels. It aligns historical alpha signals with forward-looking price changes to isolate predictive effects and transforms these metrics into heatmaps and time-series charts for
