skfolio: Portfolio Optimization in Python

TL;DR

skfolio is an open-source Python library that integrates traditional and modern portfolio optimization techniques with machine learning workflows, enhancing reproducibility and transparency in quantitative finance.

Contribution

It introduces a unified, scikit-learn-compatible framework for diverse portfolio strategies, combining statistical rigor with practical implementation in Python.

Findings

Supports classical and modern optimization methods

Provides advanced cross-validation for financial data

Facilitates reproducible machine learning workflows in finance

Abstract

Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open-source Python library for portfolio construction and risk management that seamlessly integrates with the scikit-learn ecosystem. skfolio provides a unified framework for diverse allocation strategies, from classical mean-variance optimization to modern clustering-based methods, state-of-the-art financial estimators with native interfaces, and advanced cross-validation techniques tailored for financial time series. By adhering to scikit-learn's fit-predict-transform paradigm, the library enables researchers and practitioners to leverage machine learning workflows for portfolio optimization, promoting reproducibility and transparency in quantitative finance.