A Coding Implementation to Portfolio Optimization with skfolio for Building Testing, Tuning, and Comparing Modern Investment Strategies

The tutorial shows how to use skfolio to backtest, tune, and compare modern portfolio strategies on S&P 500 data in Python.

It builds a workflow that loads S&P 500 prices, converts them to returns, and splits data chronologically for realistic evaluation.

The article compares baseline, optimization-based, hierarchical, robust, and factor-aware methods, including EqualWeighted, InverseVolatility, MeanRisk, RiskBudgeting, HRP, NCO, Black-Litterman, and Factor Model approaches.

It also demonstrates validation and hyperparameter tuning with tools like scikit-learn and GridSearchCV to control risk and improve model selection.