advanced-math-trading/portfolio-factors

Factor models decompose portfolio risk and returns into systematic exposures. This Skill solves portfolio construction by computing factor loadings, applying mean-variance optimization to generate efficient weights, and optionally incorporating Black-Litterman priors to blend market views with historical data.

Mean-variance optimization maximizes risk-adjusted returns using historical covariance; Black-Litterman extends it by incorporating investor views as priors, reducing estimation error and producing more stable, implementable allocations.

Yes. This Skill supports turnover constraints to limit rebalancing costs and cardinality constraints to cap the number of holdings, making optimization output realistic for live trading and operational workflows.

Factor data must be structured as factor loadings and returns aligned with assets. This Skill accepts numpy and pandas inputs, so ensure your factor matrix and asset returns are formatted as numeric arrays or DataFrames before optimization.

Factor models replace asset-by-asset covariance estimation with cross-sectional factor exposures and factor covariance, requiring fewer parameters and reducing noise—especially valuable when assets outnumber historical observations.

Violations like non-normal returns or regime shifts degrade optimization stability. This Skill documents edge cases; use robustness checks, stress testing, and constraint tightening to validate outputs under realistic market conditions.