stock-correlation

仓库: himself65/finance-skills
安装量: 65
排名: #11586

安装

npx skills add https://github.com/himself65/finance-skills --skill 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
Before running any code, install required packages if needed:
import
subprocess
,
sys
subprocess
.
check_call
(
[
sys
.
executable
,
"-m"
,
"pip"
,
"install"
,
"-q"
,
"yfinance"
,
"pandas"
,
"numpy"
]
)
Always include this at the top of your script.
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.
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