skills.rest
OpenSucker
OpenSuckerOFFICIAL
Explore all skills by this author →
GitHub

correlation-analysis

Analyze financial asset correlations and cointegration for pairs trading.

15|2|Updated May 1, 2026
Search Tags:#market analysis#correlation#cointegration#pair trading#sector clustering
One-click install
npx skills add https://github.com/OpenSucker/OpenSucker --skill correlation-analysis-opensucker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: correlation-analysis
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/correlation-analysis
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill correlation-analysis-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, statsmodels.

What problem does it solve?

This Skill enables comprehensive analysis of asset co-movement, cointegration, and sector clustering, helping users uncover long-term and short-term relationships between financial instruments.

Core Features & Use Cases

  • Correlation Discovery: Identify assets with high or inverse correlations for pairs trading or diversification.
  • Cointegration Testing: Assess whether assets share a long-term equilibrium relationship for reliable spread trading.
  • Sector Clustering: Group assets based on correlation patterns to understand sector influences.
  • Use Case: An analyst can detect pairs with strong cointegration and stable half-life for executing statistical arbitrage strategies.

Quick Start

Run the correlation analysis on your asset returns data to find long-term cointegrated pairs and sector groupings.

Frequently Asked Questions about correlation-analysis

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

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

To test cointegration for pairs trading, you assess whether assets share a long-term equilibrium relationship using statistical tests. This identifies reliable spread trading opportunities by finding pairs with stable half-life dynamics.

What is the difference between correlation and cointegration in market analysis?▼

Correlation measures short-term asset return co-movement, while cointegration identifies a long-term equilibrium relationship. Correlation aids portfolio diversification, whereas cointegration is essential for statistical arbitrage strategies.

Can I use pandas and statsmodels to perform sector clustering on asset returns?▼

Yes, you can use pandas and statsmodels to group assets based on correlation patterns. This hierarchical sector clustering reveals underlying market dynamics and sector influences affecting financial instruments.

What's the best way to find assets with inverse correlations for portfolio diversification?▼

The best way to find inverse correlations for portfolio diversification is to run correlation discovery on asset returns data. This identifies assets with inverse relationships to minimize overall portfolio risk.

Do I need numpy and scipy to analyze dynamic relationships in financial markets?▼

Yes, you need numpy and scipy alongside pandas and statsmodels to execute comprehensive analyses. These dependencies support the statistical measures required to assess dynamic market relationships accurately.

Related Skills

Explore More Finance & Accounting Skills→
StarlitnightlyStarlitnightly
1.2k

data-transform

Transform raw tabular data into cleaned, structured datasets using pandas and numpy.

Community
Advanced
brainbytes-devbrainbytes-dev
10

stat-arb

Construct market-neutral statistical arbitrage portfolios using PCA-based return decomposition.

Community
Advanced
mahmoud20138mahmoud20138
10

cross-asset-arbitrage-engine

Detect cross-asset arbitrage opportunities using cointegration tests and z-score spread signals.

Community
Advanced
gracefullightgracefullight

scikit-learn

Guide machine learning model building with scikit-learn preprocessing and evaluation.

Community
Advanced