Market Correlation Matrix Analyzer

Description

Real-time correlation analysis between assets for portfolio optimization and risk management.

Features

• Rolling Correlation - 20/50/100 day rolling windows
• Cross-Asset Analysis - Stocks vs Crypto vs Forex vs Commodities
• Regime Detection - Detect correlation regime changes (risk-on/risk-off)
• Heatmap Visualization - Visual correlation matrices
• Diversification Scorer - Portfolio diversification quality score
• Beta Calculation - Asset beta to benchmark (SPY, BTC)
• Pair Clustering - K-means clustering of correlated assets
• Stress Testing - Correlation behavior during market stress

Use Cases

1. Portfolio Construction - Find uncorrelated assets for diversification
2. Risk Management - Monitor correlation breakdown during crises
3. Pairs Trading - Identify highly correlated pairs for spread trading
4. Regime Trading - Adapt strategy based on correlation regimes
5. Sector Rotation - Track sector correlations for rotation strategies

Commands

analyze correlation BTC ETH GOLD SPY --window 30d
correlation heatmap my-portfolio
diversification score my-portfolio
find uncorrelated BTC --min-assets 5
correlation regime --detect
stress test my-portfolio --scenario 2020-covid
beta calculate NVDA --benchmark SPY
cluster assets --method kmeans --k 5

Parameters

Parameter	Type	Default	Description
window	string	30d	Rolling window for correlation
method	string	pearson	pearson, spearman, or kendall
min_correlation	float	0.7	Minimum correlation for pairs
max_correlation	float	0.3	Maximum for diversification
regime_threshold	float	0.2	Change threshold for regime detection
benchmark	string	SPY	Benchmark for beta calculation

Outputs

• Correlation matrix (JSON/CSV/visual heatmap)
• Diversification score (0-100)
• Beta values per asset
• Regime change alerts
• Cluster assignments
• Portfolio optimization suggestions

Technical Implementation

• Uses Pandas for correlation calculations
• NumPy for matrix operations
• SciPy for clustering algorithms
• Matplotlib/Plotly for visualizations
• Real-time updates via WebSocket data feeds

Risk Considerations

• Correlations change over time (non-stationary)
• Past correlations don't guarantee future behavior
• Crisis correlations typically spike (diversification fails when needed most)
• Use multiple timeframes for robust analysis