pair-trade-screenerStatistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.
Install via ClawdBot CLI:
clawdbot install Veeramanikandanr48/pair-trade-screenerThis skill identifies and analyzes statistical arbitrage opportunities through pair trading. Pair trading is a market-neutral strategy that profits from the relative price movements of two correlated securities, regardless of overall market direction. The skill uses rigorous statistical methods including correlation analysis and cointegration testing to find robust trading pairs.
Core Methodology:
Key Advantages:
Use this skill when:
Example user requests:
Objective: Establish the pool of stocks to analyze for pair relationships.
Option A: Sector-Based Screening (Recommended)
Select a specific sector to screen:
Option B: Custom Stock List
User provides specific tickers to analyze:
Example: ["AAPL", "MSFT", "GOOGL", "META", "NVDA"]Option C: Industry-Specific
Narrow focus to specific industry within sector:
Filtering Criteria:
Objective: Fetch price history for correlation and cointegration analysis.
Data Requirements:
FMP API Endpoint:
GET /v3/historical-price-full/{symbol}?apikey=YOUR_API_KEYData Validation:
Script Execution:
python scripts/fetch_price_data.py --sector Technology --lookback 730Objective: Identify candidate pairs with strong linear relationships.
Correlation Analysis:
For each pair of stocks (i, j) in the universe:
Correlation Interpretation:
Beta Calculation:
For each candidate pair (Stock A, Stock B):
Beta = Covariance(A, B) / Variance(B)Beta indicates the hedge ratio:
Correlation Stability Check:
Objective: Statistically validate long-term equilibrium relationship.
Why Cointegration Matters:
Augmented Dickey-Fuller (ADF) Test:
For each correlated pair:
Spread = Price_A - (Beta Ć Price_B)Cointegration Interpretation:
Half-Life Calculation:
Estimate mean-reversion speed:
Half-Life = -log(2) / log(mean_reversion_coefficient)Python Implementation:
from statsmodels.tsa.stattools import adfuller
# Calculate spread
spread = price_a - (beta * price_b)
# ADF test
result = adfuller(spread)
adf_stat = result[0]
p_value = result[1]
# Interpret
is_cointegrated = p_value < 0.05Objective: Quantify current spread deviation from equilibrium.
Spread Calculation:
Two common methods:
Method 1: Price Difference (Additive)
Spread = Price_A - (Beta Ć Price_B)Best for: Stocks with similar price levels
Method 2: Price Ratio (Multiplicative)
Spread = Price_A / Price_BBest for: Stocks with different price levels, easier interpretation
Z-Score Calculation:
Measures how many standard deviations spread is from its mean:
Z-Score = (Current_Spread - Mean_Spread) / Std_Dev_SpreadZ-Score Interpretation:
Historical Spread Analysis:
Objective: Provide actionable trading signals with clear rules.
Entry Conditions:
Conservative Approach (Z ā„ Ā±2.0):
LONG Signal:
- Z-score < -2.0 (spread 2+ std devs below mean)
- Spread is mean-reverting (cointegration p < 0.05)
- Half-life < 60 days
ā Action: Buy Stock A, Short Stock B (hedge ratio = beta)
SHORT Signal:
- Z-score > +2.0 (spread 2+ std devs above mean)
- Spread is mean-reverting (cointegration p < 0.05)
- Half-life < 60 days
ā Action: Short Stock A, Buy Stock B (hedge ratio = beta)Aggressive Approach (Z ā„ Ā±1.5):
Exit Conditions:
Primary Exit: Mean Reversion (Z = 0)
Exit when spread returns to mean (z-score crosses 0)
ā Close both legs simultaneouslySecondary Exit: Partial Profit Take
Exit 50% when z-score reaches Ā±1.0
Exit remaining 50% at z-score = 0Stop Loss:
Exit if z-score extends beyond Ā±3.0 (extreme divergence)
Risk: Possible structural break in relationshipTime-Based Exit:
Exit after 90 days if no mean-reversion
Prevents holding broken pairs indefinitelyObjective: Determine dollar amounts for market-neutral exposure.
Market Neutral Sizing:
For a pair (Stock A, Stock B) with beta = Ī²:
Equal Dollar Exposure:
If portfolio size = $10,000 allocated to this pair:
- Long $5,000 of Stock A
- Short $5,000 Ć Ī² of Stock B
Example (Ī² = 1.2):
- Long $5,000 Stock A
- Short $6,000 Stock B
ā Market neutral, beta = 0Position Sizing Considerations:
Risk Metrics:
Objective: Create structured markdown report with findings and recommendations.
Report Sections:
File Naming Convention:
pair_trade_analysis_[SECTOR]_[YYYY-MM-DD].mdExample: pair_trade_analysis_Technology_2025-11-08.md
Minimum Requirements for Valid Pair:
Red Flags (Exclude Pair):
Transaction Costs:
Short Selling:
Execution:
Purpose: Screen for cointegrated pairs within a sector or custom list.
Usage:
# Sector-based screening
python scripts/find_pairs.py --sector Technology --min-correlation 0.70
# Custom stock list
python scripts/find_pairs.py --symbols AAPL,MSFT,GOOGL,META --min-correlation 0.75
# Full options
python scripts/find_pairs.py \
--sector Financials \
--min-correlation 0.70 \
--min-market-cap 2000000000 \
--lookback-days 730 \
--output pairs_analysis.jsonParameters:
--sector: Sector name (Technology, Financials, etc.)--symbols: Comma-separated list of tickers (alternative to sector)--min-correlation: Minimum correlation threshold (default: 0.70)--min-market-cap: Minimum market cap filter (default: $2B)--lookback-days: Historical data period (default: 730 days)--output: Output JSON file (default: stdout)--api-key: FMP API key (or set FMP_API_KEY env var)Output:
[
{
"pair": "AAPL/MSFT",
"stock_a": "AAPL",
"stock_b": "MSFT",
"correlation": 0.87,
"beta": 1.15,
"cointegration_pvalue": 0.012,
"adf_statistic": -3.45,
"half_life_days": 42,
"current_zscore": -2.3,
"signal": "LONG",
"strength": "Strong"
}
]Purpose: Analyze a specific pair's spread behavior and generate trading signals.
Usage:
# Analyze specific pair
python scripts/analyze_spread.py --stock-a AAPL --stock-b MSFT
# Custom lookback period
python scripts/analyze_spread.py \
--stock-a JPM \
--stock-b BAC \
--lookback-days 365 \
--entry-zscore 2.0 \
--exit-zscore 0.5Parameters:
--stock-a: First stock ticker--stock-b: Second stock ticker--lookback-days: Analysis period (default: 365)--entry-zscore: Z-score threshold for entry (default: 2.0)--exit-zscore: Z-score threshold for exit (default: 0.0)--api-key: FMP API keyOutput:
Comprehensive guide to statistical arbitrage and pair trading:
Deep dive into cointegration testing:
Sector Analyst Integration:
Technical Analyst Integration:
Backtest Expert Integration:
Market Environment Analysis Integration:
Portfolio Manager Integration:
Use Case 1: Technology Sector Pairs
User: "Find pair trading opportunities in tech stocks"
Workflow:
1. Screen Technology sector for stocks with market cap > $10B
2. Calculate all pairwise correlations
3. Filter pairs with correlation ā„ 0.75
4. Run cointegration tests
5. Identify current z-score extremes (|z| > 2.0)
6. Generate top 10 pairs reportUse Case 2: Specific Pair Analysis
User: "Analyze AAPL and MSFT as a pair trade"
Workflow:
1. Fetch 2-year price history for AAPL and MSFT
2. Calculate correlation and beta
3. Test for cointegration
4. Calculate current spread and z-score
5. Generate entry/exit recommendation
6. Provide position sizing guidanceUse Case 3: Regional Bank Pairs
User: "Screen for pairs among regional banks"
Workflow:
1. Filter Financials sector for industry = "Regional Banks"
2. Exclude banks with <$5B market cap
3. Calculate pairwise statistics
4. Rank by cointegration strength
5. Focus on pairs with half-life < 45 days
6. Report top 5 mean-reverting pairsProblem: No cointegrated pairs found
Solutions:
Problem: All z-scores near zero (no trade signals)
Solutions:
Problem: Pair correlation broke down
Solutions:
Version: 1.0
Last Updated: 2025-11-08
Dependencies: Python 3.8+, pandas, numpy, scipy, statsmodels, requests
Generated Mar 1, 2026
A quantitative hedge fund screens for cointegrated pairs within the technology sector, such as software giants, to execute market-neutral trades. They use the skill to identify pairs with strong correlation and cointegration, then set up trades based on z-score deviations for mean-reversion profits.
An asset manager in the financial industry uses the skill to find pairs among regional banks or insurance stocks to hedge sector exposure. By analyzing correlation and cointegration, they construct market-neutral portfolios that reduce overall risk while capturing relative value opportunities.
A proprietary trading firm focuses on the healthcare sector, screening for cointegrated pairs among pharmaceutical or biotech stocks. They leverage the skill's backtesting and z-score calculations to generate entry/exit signals for short-term mean-reversion trades in volatile markets.
An energy-focused investment fund employs the skill to identify pairs within the energy sector, such as oil and gas companies, for statistical arbitrage. They use cointegration testing and spread analysis to build market-neutral strategies that profit from price divergences independent of broader market trends.
A retail trading desk uses the skill to screen for pairs in the consumer discretionary sector, like automotive or retail stocks, for mean-reversion opportunities. They apply correlation stability checks and half-life calculations to optimize trade timing and manage risk in dynamic market conditions.
A hedge fund specializing in statistical arbitrage uses this skill to systematically identify and trade cointegrated pairs. They generate revenue through market-neutral strategies that capture small, consistent profits from mean-reversion, with fees based on assets under management and performance.
A proprietary trading firm employs the skill to develop automated pair trading algorithms for in-house capital. Revenue is derived from direct trading profits by executing high-frequency or short-term mean-reversion trades based on the skill's signals and backtesting insights.
An asset management firm integrates this skill into advisory services for institutional clients seeking market-neutral exposure. They offer customized pair trading strategies and portfolio construction, generating revenue through consulting fees and a share of client investment returns.
š¬ Integration Tip
Ensure access to reliable historical price data APIs and implement robust data validation to handle missing values, as the skill relies on clean data for accurate correlation and cointegration analysis.
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