correlation-analysislisted

# Correlation Analysis Cross-asset correlation analysis for diversification assessment, risk management, pairs trading signal generation, and portfolio construction. ## Why Correlation Matters Correlation measures how assets move together. In crypto markets this is critical for: - **Diversification**: holding correlated assets provides no diversification benefit — you are effectively holding one concentrated position - **Risk management**: portfolio risk depends on the correlation structure, not just individual asset volatility - **Pairs trading**: highly correlated assets that temporarily diverge create mean-reversion opportunities - **Portfolio construction**: optimal allocation requires accurate correlation estimates - **Crash protection**: understanding tail dependence reveals whether assets crash together ## Correlation Methods ### Pearson Correlation Linear correlation assuming normality. Most common but least robust for crypto. ```python import pandas as pd import numpy as np # Always compute on returns, never on prices returns_a = prices_a.pct_change().dropna() returns_b = prices_b.pct_change().dropna() pearson_corr = returns_a.corr(returns_b) # default is Pearson ``` - **Range**: -1 (perfect inverse) to +1 (perfect co-movement) - **Assumes**: linear relationship, normally distributed returns, no outliers - **Limitation**: crypto returns are heavy-tailed — Pearson underestimates extreme co-movement ### Spearman Rank Correlation Converts values to ranks,