Stock Correlation

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.

| User Request | Route To | Examples |

|---|---|---|

| Single ticker, wants to find related stocks | Sub-Skill A: Co-movement Discovery | "what correlates with NVDA", "find stocks related to AMD", "sympathy plays for TSLA" |

| Two or more specific tickers, wants relationship details | Sub-Skill B: Return Correlation | "correlation between AMD and NVDA", "how do LITE and COHR move together", "compare AAPL vs MSFT" |

| Group of tickers, wants structure/grouping | Sub-Skill C: Sector Clustering | "correlation matrix for FAANG", "cluster these semiconductor stocks", "sector peers for AMD" |

| Wants time-varying or conditional correlation | Sub-Skill D: Realized Correlation | "rolling correlation AMD NVDA", "when NVDA drops what else drops", "how has correlation changed" |

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

| Parameter | Default |

|---|---|

| Lookback period | 1y (1 year) |

| Data interval | 1d (daily) |

| Correlation method | Pearson |

| Minimum correlation threshold | 0.60 |

| Number of results | Top 10 |

| Return type | Daily log returns |

| Rolling window | 60 trading days |

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:

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

| Rank | Ticker | Company | Correlation | Why linked |

|---|---|---|---|---|

| 1 | AMD | Advanced Micro Devices | 0.82 | Same industry — GPU/CPU |

| 2 | AVGO | Broadcom | 0.78 | AI infrastructure peer |

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:

| Metric | Value |

|---|---|

| Pearson Correlation | 0.82 |

| Beta (B vs A) | 1.15 |

| R-squared | 0.67 |

| Rolling Corr (60d avg) | 0.80 |

| Rolling Corr Range | [0.55, 0.94] |

| Rolling Corr Std Dev | 0.08 |

| Spread Z-Score (current) | +1.2 |

| Observations | 250 |

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

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

def sector_clustering(tickers, period="1y"):
    data = yf.download(tickers, period=period, auto_adjust=True, progress=False)

    # yf.download returns MultiIndex (Price, Ticker) columns
    closes = data["Close"].dropna(axis=1, thresh=max(60, len(data) // 2))
    returns = np.log(closes / closes.shift(1)).dropna()
    corr_matrix = returns.corr()

    # Hierarchical clustering order
    from scipy.cluster.hierarchy import linkage, leaves_list
    from scipy.spatial.distance import squareform

    dist_matrix = 1 - corr_matrix.abs()
    np.fill_diagonal(dist_matrix.values, 0)
    condensed = squareform(dist_matrix)
    linkage_matrix = linkage(condensed, method="ward")
    order = leaves_list(linkage_matrix)
    ordered_tickers = [corr_matrix.columns[i] for i in order]

    # Reorder matrix
    clustered = corr_matrix.loc[ordered_tickers, ordered_tickers]

    return clustered, returns

Note: if scipy is not available, fall back to sorting by average correlation instead of hierarchical clustering.

C2: Present results

• Full correlation matrix — formatted as a table. For more than 8 tickers, show as a heatmap description or highlight only the strongest/weakest pairs.

• Identified clusters — group tickers that have high intra-group correlation:
• Cluster 1: [NVDA, AMD, AVGO] — avg intra-correlation 0.82
• Cluster 2: [AAPL, MSFT] — avg intra-correlation 0.75

• Outliers — tickers with low average correlation to the group (potential diversifiers).

• Strongest pairs — top 5 highest-correlation pairs in the matrix.

• Weakest pairs — top 5 lowest/negative-correlation pairs (hedging candidates).

Sub-Skill D: Realized Correlation

Goal: Show how correlation changes over time and under different market conditions.

D1: Rolling correlation

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

def realized_correlation(ticker_a, ticker_b, period="2y", windows=[20, 60, 120]):
    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()

    rolling = {}
    for w in windows:
        rolling[f"{w}d"] = returns[ticker_a].rolling(w).corr(returns[ticker_b])

    return rolling, returns

D2: Regime-conditional correlation

def regime_correlation(returns, ticker_a, ticker_b, condition_ticker=None):
    """Compare correlation across up/down/volatile regimes."""
    if condition_ticker is None:
        condition_ticker = ticker_a

    ret = returns[condition_ticker]

    regimes = {
        "All Days": pd.Series(True, index=returns.index),
        "Up Days (target > 0)": ret > 0,
        "Down Days (target < 0)": ret < 0,
        "High Vol (top 25%)": ret.abs() > ret.abs().quantile(0.75),
        "Low Vol (bottom 25%)": ret.abs() < ret.abs().quantile(0.25),
        "Large Drawdown (< -2%)": ret < -0.02,
    }

    results = {}
    for name, mask in regimes.items():
        subset = returns[mask]
        if len(subset) >= 20:
            results[name] = {
                "correlation": round(subset[ticker_a].corr(subset[ticker_b]), 4),
                "days": int(mask.sum()),
            }

    return results

D3: Present results

• Rolling correlation summary table:

| Window | Current | Mean | Min | Max | Std |

|---|---|---|---|---|---|

| 20-day | 0.88 | 0.76 | 0.32 | 0.95 | 0.12 |

| 60-day | 0.82 | 0.78 | 0.55 | 0.92 | 0.08 |

| 120-day | 0.80 | 0.79 | 0.68 | 0.88 | 0.05 |

• Regime correlation table:

| Regime | Correlation | Days |

|---|---|---|

| All Days | 0.82 | 250 |

| Up Days | 0.75 | 132 |

| Down Days | 0.87 | 118 |

| High Vol (top 25%) | 0.90 | 63 |

| Large Drawdown (< -2%) | 0.93 | 28 |

• Key insight: Highlight whether correlation increases during sell-offs (very common — "correlations go to 1 in a crisis"). This is critical for risk management.

• Trend: Is correlation trending higher or lower recently vs. its historical average?

Step 3: Respond to the User

After running the appropriate sub-skill, present results clearly:

Always include

• The lookback period and data interval used
• The number of observations (trading days)
• Any tickers dropped due to insufficient data

Always caveat

• Correlation is not causation — co-movement does not imply a causal link
• Past correlation does not guarantee future correlation — regimes shift
• Short lookback windows produce noisy estimates; longer windows smooth but may miss regime changes

Practical applications (mention when relevant)

• Sympathy plays: Stocks likely to follow a peer's earnings/news move
• Pair trading: High-correlation pairs where the spread has diverged from its mean
• Portfolio diversification: Finding low-correlation assets to reduce risk
• Hedging: Identifying inversely correlated instruments
• Sector rotation: Understanding which sectors move together
• Risk management: Correlation spikes during stress — diversification may fail when needed most

Important: Never recommend specific trades. Present data and let the user draw conclusions.

Reference Files

• references/sector_universes.md — Dynamic peer universe construction using yfinance Screener API

Read the reference file when you need to build a peer universe for a given ticker.