Crypto Correlation Analysis System Development
Correlation analysis in crypto is a critical tool for portfolio management, arbitrage pair search and understanding market structure. System automatically tracks correlations between hundreds of assets and identifies significant changes.
Pearson correlation: linear dependency between returns of two assets. Most common.
Spearman rank correlation: less sensitive to outliers. Preferable for crypto returns with fat tails.
DCC-GARCH (Dynamic Conditional Correlation): accounts for temporal dependence of volatility. More accurate for short-term correlations.
Practical applications:
- Portfolio diversification: select assets with low mutual correlations
- Stat arb pairs: pairs with stable correlation > 0.85 are candidates for cointegration check
- Risk management: if all positions correlated > 0.7 — portfolio actually not diversified
Correlation regime detection: Normal market correlation 0.4–0.6. Crisis mode > 0.8. On entering crisis mode system recommends position reduction.
Stack: Python (pandas, scipy, arch for DCC-GARCH), PostgreSQL for matrix storage, React + D3.js for interactive heatmaps. Data updates daily, realtime rolling correlation every hour.