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arXiv · 2507.04176

skfolio: Portfolio Optimization in Python

Carlo Nicolini·Matteo Manzi·Hugo Delatte
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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.

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Carlo Nicolini, Matteo Manzi, Hugo Delatte. 2025-07-05. skfolio: Portfolio Optimization in Python. https://arxiv.org/abs/2507.04176

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