Factor-Based Portfolio Management in Equity Market

This code implements a full QIS strategy that combines traditional factor-based modeling with machine-learning techniques. The pipeline first estimates each asset’s exposure to multiple systematic factors through a Ridge-regularized multi-factor model, ensuring stable and robust loadings even in the presence of multicollinearity. It then integrates predictive components and model-selection mechanisms inspired by machine learning such as automatic hyperparameter tuning, cross-validation, and performance-driven optimization to generate signals and allocate weights systematically. The result is a data-driven, transparent, and fully systematic investment process that blends academic factor insights with modern machine-learning methods in line with QIS best practices.