Stock Correlation Analysis Skill

Finds and analyzes correlated stocks using historical price data from Yahoo Finance via yfinance. Routes to specialized sub-skills based on user intent.

Important: This is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.

Step 1: Ensure Dependencies Are Available

Current environment status:

!`python3 -c "import yfinance, pandas, numpy; print(f'yfinance={yfinance.__version__} pandas={pandas.__version__} numpy={numpy.__version__}')" 2>/dev/null || echo "DEPS_MISSING"`

If DEPS_MISSING, install required packages before running any code:

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance", "pandas", "numpy"])

If all dependencies are already installed, skip the install step and proceed directly.

Step 2: Route to the Correct Sub-Skill

Classify the user's request and jump to the matching sub-skill section below.

If ambiguous, default to Sub-Skill A (Co-movement Discovery) for single tickers, or Sub-Skill B (Return Correlation) for two tickers.

Defaults for all sub-skills

Sub-Skill A: Co-movement Discovery

Goal: Given a single ticker, find stocks that move with it.

A1: Build the peer universe

You need 15-30 candidates. Do not use hardcoded ticker lists — build the universe dynamically at runtime. See references/sector_universes.md for the full implementation. The approach:

1. Screen same-industry stocks using yf.screen() + yf.EquityQuery to find stocks in the same industry as the target
2. Broaden to sector if the industry screen returns fewer than 10 peers
3. Add thematic/adjacent industries — read the target's longBusinessSummary and screen 1-2 related industries (e.g., a semiconductor company → also screen semiconductor equipment)
4. Combine, deduplicate, remove target ticker

A2: Compute correlations

import yfinance as yf
import pandas as pd
import numpy as np

def discover_comovement(target_ticker, peer_tickers, period="1y"):
    all_tickers = [target_ticker] + [t for t in peer_tickers if t != target_ticker]
    data = yf.download(all_tickers, period=period, auto_adjust=True, progress=False)

    # Extract close prices — yf.download returns MultiIndex (Price, Ticker) columns
    closes = data["Close"].dropna(axis=1, thresh=max(60, len(data) // 2))

    # Log returns
    returns = np.log(closes / closes.shift(1)).dropna()
    corr_series = returns.corr()[target_ticker].drop(target_ticker, errors="ignore")

    # Rank by absolute correlation
    ranked = corr_series.abs().sort_values(ascending=False)

    result = pd.DataFrame({
        "Ticker": ranked.index,
        "Correlation": [round(corr_series[t], 4) for t in ranked.index],
    })
    return result, returns

A3: Present results

Show a ranked table with company names and sectors (fetch via yf.Ticker(t).info.get("shortName")):

Include:

• Top 10 positively correlated stocks
• Any notable negatively correlated stocks (potential hedges)
• Brief explanation of why each might be linked (sector, supply chain, customer overlap)

Sub-Skill B: Return Correlation

Goal: Deep-dive into the relationship between two (or a few) specific tickers.

B1: Download and compute

import yfinance as yf
import pandas as pd
import numpy as np

def return_correlation(ticker_a, ticker_b, period="1y"):
    data = yf.download([ticker_a, ticker_b], period=period, auto_adjust=True, progress=False)
    closes = data["Close"][[ticker_a, ticker_b]].dropna()

    returns = np.log(closes / closes.shift(1)).dropna()
    corr = returns[ticker_a].corr(returns[ticker_b])

    # Beta: how much does B move per unit move of A
    cov_matrix = returns.cov()
    beta = cov_matrix.loc[ticker_b, ticker_a] / cov_matrix.loc[ticker_a, ticker_a]

    # R-squared
    r_squared = corr ** 2

    # Rolling 60-day correlation for stability
    rolling_corr = returns[ticker_a].rolling(60).corr(returns[ticker_b])

    # Spread (log price ratio) for mean-reversion
    spread = np.log(closes[ticker_a] / closes[ticker_b])
    spread_z = (spread - spread.mean()) / spread.std()

    return {
        "correlation": round(corr, 4),
        "beta": round(beta, 4),
        "r_squared": round(r_squared, 4),
        "rolling_corr_mean": round(rolling_corr.mean(), 4),
        "rolling_corr_std": round(rolling_corr.std(), 4),
        "rolling_corr_min": round(rolling_corr.min(), 4),
        "rolling_corr_max": round(rolling_corr.max(), 4),
        "spread_z_current": round(spread_z.iloc[-1], 4),
        "observations": len(returns),
    }

B2: Present results

Show a summary card:

Interpretation guide:

• Correlation > 0.80: Strong co-movement — these stocks are tightly linked
• Correlation 0.50–0.80: Moderate — shared sector drivers but independent factors too
• Correlation < 0.50: Weak — limited co-movement despite possible sector overlap
• High rolling std: Unstable relationship — correlation varies significantly over time
• Spread Z > |2|: Unusual divergence from historical relationship

Sub-Skill C: Sector Clustering

Goal: Given a group of tickers, show the full correlation structure and identify clusters.

C1: Build the correlation matrix

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