PCA Factor Decomposition - Quant Researcher Interview Question

Problem Description

Principal Component Analysis (PCA) is a statistical technique used in quantitative finance to identify independent drivers of asset returns and reduce dimensionality. By decomposing the covariance matrix of returns into eigenvectors and eigenvalues, analysts can extract latent risk factors and construct orthogonal factor portfolios. This process is fundamental for risk management, signal processing, and feature extraction in high-dimensional financial datasets. Task Implement a function solutio