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correlation-analysis

Analyze market data for correlation, cointegration, and cross-market linkages using statistical tests.

2|Updated May 13, 2026
Search Tags:#market analysis#risk management#cointegration#correlation analysis#pairs trading
One-click install
npx skills add https://github.com/thanhtai040805/AI_Invest --skill correlation-analysis-thanhtai040805
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: correlation-analysis
Source: https://github.com/thanhtai040805/AI_Invest/tree/main/ai-engine/app/domain/services/quant/skills_data/correlation-analysis
Command: npx skills add https://github.com/thanhtai040805/AI_Invest --skill correlation-analysis-thanhtai040805

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, statsmodels, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides in-depth correlation and cointegration analysis, uncovering hidden patterns in market data for informed decision-making.

Core Features & Use Cases

  • Co-Movement Discovery: Identify highly correlated assets for pairs trading and substitute identification.
  • Deep Return-Correlation Analysis: Perform a comprehensive analysis including Pearson, Spearman, and Kendall correlations, Beta/R², rolling correlation, and spread Z-Score.
  • Sector Clustering: Run hierarchical clustering on correlation matrices to discover sector structures and portfolio diversification.
  • Realized Correlation: Compute rolling correlation and analyze conditional correlation by market regime.
  • Cointegration Analysis: Use Engle-Granger and Johansen tests to assess long-run equilibrium relationships.
  • Cross-Market Linkage Analysis: Analyze cross-market correlations and lead-lag relationships between different markets.
  • Pair-Trading Signal Generation: Generate signals for pair trading based on correlation and cointegration analysis.

Quick Start

Activate the correlation-analysis skill to perform a full correlation analysis on the 'market_data.csv' file.

Frequently Asked Questions about correlation-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I test for cointegration between two assets for pairs trading?▼

Cointegration testing for pairs trading uses Engle-Granger and Johansen tests to assess long-run equilibrium relationships between assets. This identifies statistically valid pairs whose price spreads mean-revert over time.

What is the best way to calculate rolling correlation across market data in Python?▼

Rolling correlation across market data is calculated using pandas and numpy to compute moving window Pearson or Spearman coefficients. This reveals how asset co-movement dynamics evolve, allowing analysis of conditional correlation by market regime.

Does this correlation analysis approach use Pearson, Spearman, and Kendall methods?▼

Yes, deep return-correlation analysis uses Pearson, Spearman, and Kendall correlation methods alongside Beta, R-squared, and spread Z-Score calculations to provide a comprehensive statistical view of asset relationships.

Can I use hierarchical clustering on a correlation matrix to find sector structures?▼

Yes, hierarchical clustering runs on correlation matrices to discover sector structures. Grouping assets by their return correlations helps identify natural market clusters and optimize portfolio diversification.

How do I analyze cross-market linkages and lead-lag relationships?▼

Cross-market linkage analysis examines correlations and lead-lag relationships between different markets using statsmodels. This uncovers how price movements in one market statistically predict or follow movements in another.

Do I need statsmodels and scipy to run Johansen tests for market analysis?▼

Yes, running Johansen tests for market analysis requires statsmodels and scipy. These dependencies provide the underlying statistical methods needed for cointegration tests and data manipulation alongside pandas and numpy.

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