# correlation-analysis

Computes correlations between assets: rolling windows, hierarchical clustering, tail dependence. Useful for diversification checks and pairs trading.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 464 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/correlation-analysis/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/correlation-analysis

## What it does

The skill hands your agent ready Python recipes for cross-asset correlation work. Three methods are covered: Pearson, Spearman and Kendall Tau. For crypto the author leans toward Spearman and Kendall, since returns are heavy-tailed and plain Pearson understates joint moves during a selloff.

Rolling correlation comes in 20, 60 and 120 day windows, plus an EWMA version that reacts faster to recent data. The matrix section walks through eigenvalue decomposition: the first component is the market factor, and once it explains more than 60% of variance everything moves together. Hierarchical clustering runs through `scipy`, and minimum variance weights come from the inverse covariance matrix.

Tail dependence gets its own block — the share of cases where a second asset drops below its quantile at the same time as the first. That is where diversification quietly breaks: a 0.6 correlation between two altcoins can pair with 0.8 tail dependence, so the protection vanishes exactly when you need it. The code needs `pandas`, `numpy` and `scipy`, and correlations are computed on returns rather than prices.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill correlation-analysis -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/correlation-analysis/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/correlation-analysis/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill correlation-analysis -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/correlation-analysis/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill correlation-analysis -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/correlation-analysis/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills correlation-analysis
```

2. **Manually: put the files in place**
- File: `.github/skills/correlation-analysis/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill correlation-analysis -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/correlation-analysis
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/correlation-analysis/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill correlation-analysis -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/correlation-analysis/SKILL.md

## When to use it
- check whether a basket of coins actually diversifies anything
- plot a rolling correlation and spot a regime shift
- group assets into clusters by how they move together
- derive minimum variance weights from the covariance matrix
- test whether two assets crash at the same time in the tail

## Example prompts
- Compute the 60-day rolling correlation between BTC and ETH and show where it shifted most
- Build a Spearman correlation matrix for BTC, ETH, SOL and AVAX, then decompose it into eigenvalues
- Estimate tail dependence between ETH and SOL at the 0.05 quantile — do they fall together?

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE eed75f051a3adcaa
````text
---
name: correlation-analysis
description: Cross-asset correlation analysis including rolling correlation, hierarchical clustering, tail dependence, and regime-dependent correlation
---

# 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, then computes Pearson on ranks. Captures monotonic (not just linear) relationships.

```python
spearman_corr = returns_a.corr(returns_b, method='spearman')
```

- More robust to outliers and non-linear relationships
- Better for crypto due to heavy-tailed return distributions
- Slightly lower power than Pearson when normality holds

### Kendall Tau Correlation

Counts concordant vs discordant pairs. Most robust to outliers.

```python
kendall_corr = returns_a.corr(returns_b, method='kendall')
```

- Most robust to outliers of the three methods
- Computationally slower on large datasets
````
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