stock-correlation
Analyze how stocks move together using Yahoo Finance price history (yfinance): find correlated peers for a ticker, measure correlation, beta, and spread between specific tickers, cluster a group into a correlation matrix, and track rolling or regime-dependent correlation. Use this skill whenever the user asks what moves with a stock, what else drops when it drops, related tickers or sympathy plays, sector or supply-chain peers, pair trading or hedging pairs, beta or relative performance, correlation matrices, co-movement, or rolling/realized correlation — including well-known pairs like AMD/NVDA, GOOGL/AVGO, or LITE/COHR. With a single ticker, assume the user wants its correlated peers.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/himself65/finance-skills --skill stock-correlation- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads stock-correlation automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Stock Correlation Analysis Skill
If your host has not already executed the shell probes below (for example, in Codex), run the commands inside the ! wrappers with its shell tool before choosing a setup path. Use the actual output; if shell access is unavailable, report that limitation.
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 "exec('try:\n import yfinance, pandas, numpy\n print(f\'yfinance={yfinance.__version__} pandas={pandas.__version__} numpy={numpy.__version__}\')\nexcept Exception:\n print(\'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.EquityQueryto 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
longBusinessSummaryand 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")). Values below are illustrative:
| 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 (illustrative values):
| 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 (illustrative values here and in the regime 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
Present the data and let the user draw conclusions; don't recommend specific trades.
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.
FAQ
What does the stock-correlation skill do?
Analyze how stocks move together using Yahoo Finance price history (yfinance): find correlated peers for a ticker, measure correlation, beta, and spread between specific tickers, cluster a group into a correlation matrix, and track rolling or regime-dependent correlation. Use this skill whenever the user asks what moves with a stock, what else drops when it drops, related tickers or sympathy plays, sector or supply-chain peers, pair trading or hedging pairs, beta or relative performance, correlation matrices, co-movement, or rolling/realized correlation — including well-known pairs like AMD/NVDA, GOOGL/AVGO, or LITE/COHR. With a single ticker, assume the user wants its correlated peers.
How do I install stock-correlation?
Run npx skills add https://github.com/himself65/finance-skills --skill stock-correlation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/stock-correlation/ (personal) or .claude/skills/stock-correlation/ (project).
Which AI agents can use stock-correlation?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is stock-correlation safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains stock-correlation?
It is published by himself65 in himself65/finance-skills. It has 3,487 recorded installs.
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