stat-arb

You can build a market-neutral statistical arbitrage portfolio by running PCA on a return matrix to decompose common factors, computing residual signals, and applying constraint-based optimization for market, sector, and factor neutrality before sizing to target gross exposure.

PCA-based return decomposition separates common systematic factors from asset-specific residuals in a return matrix, isolating the residual alpha signal used for mean-reversion trading in statistical arbitrage.

Factor and sector neutrality are enforced through constraint-based optimization during portfolio construction, ensuring residual signals are orthogonal to market, sector, and PCA-identified common risk factors.

Yes, this PCA-based statistical arbitrage framework is designed for liquid equity universes with daily rebalancing, handling the full workflow from universe definition to sized portfolio output.

The best way to calculate residual signals is to perform PCA decomposition on a constructed return matrix, extract the orthogonal residuals, and use those residuals as mean-reversion signals within a market-neutral portfolio.