skfolio is a Python library designed for portfolio optimization and financial risk management that integrates closely with the scikit-learn ecosystem. The project provides a unified machine learning-style framework for building, validating, and comparing portfolio allocation strategies using financial data. By following the familiar scikit-learn API design, the library allows quantitative researchers and developers to apply techniques such as model selection, cross-validation, and hyperparameter tuning to portfolio construction workflows. It supports a wide range of allocation methods, from classical mean-variance optimization to modern techniques that rely on clustering, factor models, and risk-based allocations. The framework also includes tools for evaluating portfolio performance under different market conditions, enabling users to test robustness and reduce the risk of overfitting.
Features
- Portfolio optimization framework compatible with the scikit-learn API
- Tools for model selection, cross-validation, and hyperparameter tuning in finance
- Support for multiple portfolio allocation strategies and optimization methods
- Built-in utilities for backtesting and evaluating investment strategies
- Integration with Python data science libraries such as NumPy and pandas
- Visualization and analysis tools for portfolio performance and risk metrics