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Difficulty: Medium
Category: machine_learning
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Topics: pca, factor-model, covariance, eigendecomposition, risk-decomposition, machine-learning
Principal Component Analysis (PCA) on asset return covariances is a standard technique for uncovering latent risk factors that drive portfolio variance. Systematic trading desks use PCA to monitor factor exposures, detect crowding, and attribute portfolio risk to its underlying systematic drivers. This process involves an eigendecomposition of the covariance matrix to extract principal components and their corresponding explained variance. Task Implement the function solution(returns_matrix: li
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