correlation-analysis

To find correlated assets for pair trading, scan a universe of return series to identify highly correlated candidates and filter them with cointegration tests to validate relationship stability for reliable signals.

Generate pair-trade signals by computing Z-scores from the spread of cointegrated asset return series, producing entry and exit triggers for automated trading strategies based on statistical mean reversion.

Detect market co-movement and cointegration using Pearson, Spearman, and Kendall correlation methods alongside rolling correlations and hedge ratios to evaluate the stability of asset relationships for diversification.

Yes, analyzing asset correlations requires Python pandas, NumPy, SciPy, and statsmodels to compute statistical metrics, with optional matplotlib for visualizing rolling correlations and pair-trade signal outputs.

Yes, you can compute dynamic hedge ratios using a Kalman filter for portfolio diversification, adjusting ratios continuously over time to maintain optimal asset allocation and stable co-movement in changing markets.

Avoid using correlation analysis for trading strategies when asset return series show no statistical cointegration, as unstable relationships lead to misguided pair-trading signals and unexpected portfolio diversification losses.