Pair Trade Screener

Statistical arbitrage tool for identifying and analyzing pair trading opportunities using cointegration testing and mean-reversion analysis.

Overview

The Pair Trade Screener finds statistically significant pair trading opportunities by:

• Testing for cointegration (long-term equilibrium relationships)
• Calculating hedge ratios (beta values)
• Measuring mean-reversion speed (half-life)
• Generating entry/exit signals based on z-score thresholds

Market Neutral Strategy: Profit from relative price movements regardless of overall market direction.

Features

✅ Sector-wide screening - Analyze all stocks in a sector ✅ Custom pair analysis - Test specific stock combinations ✅ Statistical rigor - Cointegration tests (ADF), correlation analysis ✅ Mean-reversion metrics - Half-life calculation, z-score tracking ✅ Trade signals - Automatic entry/exit recommendations ✅ FMP API integration - Free tier sufficient for screening ✅ JSON output - Structured results for further analysis

Installation

Prerequisites

• Python 3.8+
• FMP API key (free tier: 250 requests/day)

Install Dependencies

pip install pandas numpy scipy statsmodels requests

Get FMP API Key

1. Visit: https://financialmodelingprep.com/developer/docs
2. Sign up for free account
3. Copy your API key
4. Set environment variable:

export FMP_API_KEY="your_key_here"

Or add to ~/.bashrc / ~/.zshrc for persistence.

Usage

Quick Start

# Screen Technology sector for pairs
python scripts/find_pairs.py --sector Technology

# Analyze specific pair
python scripts/analyze_spread.py --stock-a AAPL --stock-b MSFT

Screening for Pairs

Sector-Based Screening:

# Screen entire sector
python scripts/find_pairs.py --sector Financials

# Adjust correlation threshold
python scripts/find_pairs.py --sector Energy --min-correlation 0.75

# Longer lookback period
python scripts/find_pairs.py --sector Healthcare --lookback-days 1095

Custom Stock List:

# Test specific stocks
python scripts/find_pairs.py --symbols AAPL,MSFT,GOOGL,META,NVDA

# Tech giants pair screening
python scripts/find_pairs.py --symbols JPM,BAC,WFC,C,GS,MS

Full Options:

python scripts/find_pairs.py \
  --sector Technology \
  --min-correlation 0.70 \
  --min-market-cap 10000000000 \
  --lookback-days 730 \
  --output tech_pairs.json \
  --api-key YOUR_KEY

Analyzing Individual Pairs

Basic Analysis:

python scripts/analyze_spread.py --stock-a AAPL --stock-b MSFT

Custom Parameters:

python scripts/analyze_spread.py \
  --stock-a JPM \
  --stock-b BAC \
  --lookback-days 365 \
  --entry-zscore 2.0 \
  --exit-zscore 0.5 \
  --api-key YOUR_KEY

Example Output

Pair Screening Results

PAIR TRADING SCREEN SUMMARY
==========================================================================

Total pairs analyzed: 45
Cointegrated pairs: 12
Pairs with trade signals: 5

==========================================================================
ACTIVE TRADE SIGNALS
==========================================================================

Pair: XOM/CVX
  Signal: LONG
  Z-Score: -2.35
  Correlation: 0.9421
  P-Value: 0.0012
  Half-Life: 28.3 days
  Strength: ★★★

Individual Pair Analysis

PAIR TRADE ANALYSIS: AAPL / MSFT
==========================================================================

[ PAIR STATISTICS ]
  Correlation: 0.8732
  Hedge Ratio (Beta): 1.1523
  Data Points: 365

[ COINTEGRATION TEST ]
  ADF Statistic: -3.8542
  P-value: 0.0028
  Result: ✅ COINTEGRATED (p < 0.05)
  Strength: ★★★ Very Strong

[ MEAN REVERSION ]
  Half-Life: 42.1 days
  Speed: Moderate (suitable for pair trading)

[ Z-SCORE ]
  Current Z-Score: -2.13
  Historical Range: [-3.45, 3.12]

[ TRADE SIGNAL ]
  Signal: 🔺 LONG SPREAD
  Action: Long AAPL, Short MSFT
  Rationale: Z-score = -2.13 → AAPL cheap relative to MSFT

[ POSITION SIZING ]
  Example Allocation: $10,000
  LONG AAPL: $5,000 (27 shares @ $185.50)
  SHORT MSFT: $5,762 (14 shares @ $411.25)

  Exit Conditions:
    - Primary: Z-score crosses 0 (mean reversion)
    - Stop Loss: Z-score > ±3.0
    - Time-based: No reversion after 90 days

Understanding the Metrics

Correlation

• Range: -1 to +1
• Threshold: ≥ 0.70 required
• Interpretation: Higher = stronger co-movement

Cointegration P-Value

• Range: 0 to 1
• Threshold: < 0.05 required (statistically significant)
• Interpretation: Lower = stronger cointegration
  • p < 0.01: ★★★ Very strong
  • p 0.01-0.05: ★★ Moderate
  • p > 0.05: ☆ Not cointegrated (reject)

Half-Life

• Meaning: Time for spread to revert halfway to mean
• Fast: < 30 days (ideal for short-term trading)
• Moderate: 30-60 days (standard pair trading)
• Slow: > 60 days (long-term positions)

Z-Score

• Calculation: (Current Spread - Mean) / Std Dev
• Entry Signals:
  • Z > +2.0: Short spread (Short A, Long B)
  • Z < -2.0: Long spread (Long A, Short B)
• Exit: Z crosses 0 (mean reversion)
• Stop: |Z| > 3.0 (extreme divergence)

Hedge Ratio (Beta)

• Meaning: Dollar amount of Stock B per $1 of Stock A
• Example: Beta = 1.2 → Short $1,200 of B for every $1,000 long in A
• Purpose: Market-neutral positioning (net beta ≈ 0)

Common Workflows

1. Weekly Pair Screening

# Monday: Screen for new opportunities
python scripts/find_pairs.py --sector Technology --output tech_pairs.json

# Review top pairs in JSON output
cat tech_pairs.json | jq '.pairs[] | select(.signal != "NONE")'

# Detailed analysis on top candidates
python scripts/analyze_spread.py --stock-a AAPL --stock-b MSFT

2. Sector Rotation Pairs

# Screen multiple sectors
for sector in Technology Financials Healthcare Energy; do
  python scripts/find_pairs.py --sector $sector --output ${sector}_pairs.json
  sleep 5
done

# Find pairs with strongest signals
cat *_pairs.json | jq '.pairs[] | select(.current_zscore | . > 2 or . < -2)'

3. Monitor Existing Pairs

# Update z-scores for current positions
python scripts/analyze_spread.py --stock-a XOM --stock-b CVX
python scripts/analyze_spread.py --stock-a JPM --stock-b BAC
python scripts/analyze_spread.py --stock-a GOOGL --stock-b META

API Usage & Rate Limits

Free Tier:

• 250 API requests/day
• ~2 requests per stock for price data
• Can screen ~60 stocks/day (= 1,770 pairs)

Screening Costs:

Sector screening (30 stocks):
  - Fetch 30 stock prices = 30 requests
  - Analyze 435 pairs (30 choose 2) = 0 additional requests
  - Total: 30 requests

Individual pair analysis:
  - Fetch 2 stock prices = 2 requests

Tips:

• Run sector screens once/week (not daily)
• Cache results in JSON files
• Monitor specific pairs daily (2 requests each)
• Upgrade to paid plan if screening multiple sectors daily

Interpretation Guide

When to Trade

✅ Strong Pair (Enter):

• Correlation > 0.80
• P-value < 0.03
• Half-life 20-60 days
• |Z-score| > 2.0
• Economic linkage (same sector/industry)

⚠️ Marginal Pair (Caution):

• Correlation 0.70-0.80
• P-value 0.03-0.05
• Half-life > 60 days
• |Z-score| 1.5-2.0

❌ Weak Pair (Avoid):

• Correlation < 0.70
• P-value > 0.05
• Half-life > 90 days or undefined
• No economic linkage

Exit Conditions

Primary Exit:

• Z-score crosses 0 (spread reverts to mean)
• Close both legs simultaneously

Stop Loss:

• |Z-score| > 3.0 (extreme divergence, possible structural break)
• -5% loss on spread
• Exit immediately

Time-Based:

• No mean reversion after 90 days (or 3× half-life)
• Free capital for better opportunities

Troubleshooting

No pairs found

Solutions:

• Lower --min-correlation to 0.65
• Expand stock universe (try different sector)
• Increase --lookback-days to 1095 (3 years)

API rate limit exceeded

Solutions:

• Wait 24 hours (free tier resets daily)
• Cache screening results (JSON files)
• Upgrade to paid plan ($14/mo Starter tier)

All z-scores near zero

Normal: Pairs in equilibrium, no trade signals Action: Check back later or expand universe

Pair correlation broke down

Causes: Corporate events, M&A, business model changes Detection: Recent correlation << historical correlation Action: Exit pair, remove from watchlist

Integration with Other Skills

Backtest Expert:

• Test pair trading strategies historically
• Optimize entry/exit thresholds
• Validate robustness

Sector Analyst:

• Identify sectors in rotation
• Screen for pairs within leading sectors

Technical Analyst:

• Confirm individual stock trends
• Check support/resistance before entry

Portfolio Manager:

• Track multiple pair positions
• Monitor overall market-neutral exposure

Resources

Documentation:

• references/methodology.md - Statistical arbitrage theory
• references/cointegration_guide.md - Cointegration testing guide

FMP API:

• API Docs: https://financialmodelingprep.com/developer/docs
• Historical Price API: /v3/historical-price-full/{symbol}
• Stock Screener API: /v3/stock-screener

Academic Papers:

• Engle & Granger (1987): "Co-Integration and Error Correction"
• Gatev et al. (2006): "Pairs Trading: Performance of a Relative-Value Arbitrage Rule"

License

Educational and research use. Trade at your own risk. Past performance does not guarantee future results.

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Version: 1.0 Last Updated: 2025-11-08 Dependencies: Python 3.8+, pandas, numpy, scipy, statsmodels, requests API: FMP API (free tier sufficient)