correlation-analysis

Cointegration testing for pairs trading applies Engle-Granger and Johansen tests to two asset price series to identify long-term equilibrium relationships. Half-life estimation gauges spread reversion speed, enabling robust candidate pool construction for systematic trade signal generation.

Calculating dynamic hedge ratios uses the Kalman filter to adjust exposure continuously based on changing asset co-movements. This approach supplements static correlation metrics like Pearson, Spearman, and Kendall by providing adaptive position sizing for pair-trading signal generation.

Generating pair-trading signals from correlation analysis involves computing Pearson, Spearman, or Kendall metrics across asset price series, then applying cointegration tests to confirm relationships. This skill outputs modular position signals for automated trading strategies directly from the identified co-movements.

Using statsmodels for Johansen cointegration tests in multi-asset portfolios allows simultaneous identification of multiple cointegrating vectors. This skill leverages statsmodels alongside numpy and scipy to process multi-asset price series for sector clustering and cross-market linkage analysis.

Discovering co-movements across assets for sector clustering requires computing correlation matrices and cross-market linkage metrics. This skill processes multi-asset price inputs using pandas and seaborn to visualize return-correlation networks and identify clusters of assets exhibiting similar historical behavior.

Pair trading strategies fail when high correlation exists without cointegration because the spread between assets is non-stationary. This skill addresses the issue by applying Engle-Granger tests and half-life estimation to verify that asset pairs possess true mean-reverting equilibrium relationships before generating signals.