Statistical 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 opportunitie

Pair Trade Screener Overview This 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: Identify pairs of stocks with high correlation and similar sector/industry exposure Test for cointegration (long term statistical relationship) Calculate spread z scores to identify mean reversion opportunities Generate entry/exit signals based on statistical thresholds Provide position sizing for market neutral exposure Key Advantages: Market neutral: Profits in up, down, or sideways markets Risk management: Limited exposure to broad market movements Statistical foundation: Data driven, not discretionary Diversification: Uncorrelated to traditional long only strategies When to Use This Skill Use this skill when: User asks for "pair trading opportunities" User wants "market neutral strategies" User requests "statistical arbitrage screening" User asks "which stocks move together?" User wants to hedge sector exposure User requests mean reversion trade ideas User asks about relative value trading Example user requests: "Find pair trading opportunities in the tech sector" "Which stocks are cointegrated?" "Screen for statistical arbitrage opportunities" "Find mean reversion pairs" "What are good market neutral trades right now?" Prerequisites Python 3.9 or newer An FMP API key with access to the company screener and historical price endpoints statsmodels =0.14,<0.15 for ADF and autoregression calculations Set the API key without placing it on the command line or in a committed file: Run the scripts from the repository root with the statistical dependency isolated to the command: Analysis Workflow Step 1: Define Pair Universe Objective: Establish the pool of stocks to analyze for pair relationships. Option A: Sector Based Screening (Recommended) Select a specific sector to screen: Technology Financials Healthcare Consumer Discretionary Industrials Energy Materials Consumer Staples Utilities Real Estate Communication Services Option B: Custom Stock List User provides specific tickers to analyze: Option C: Industry Specific Narrow focus to specific industry within sector: Example: "Software" within Technology sector Example: "Regional Banks" within Financials Filtering Criteria: Minimum market cap: $2B (mid cap and above) Minimum average volume: 1M shares/day (liquidity requirement) Active trading: No delisted or inactive stocks Same exchange preference: Avoid cross exchange complications Step 2: Retrieve Historical Price Data Objective: Fetch price history for correlation and cointegration analysis. Data Requirements: Timeframe: 2 years (minimum 252 trading days) Frequency: Daily closing prices Adjustments: Adjusted for splits and dividends Clean data: No gaps or missing values FMP API Endpoint: Data Validation: Verify consistent date ranges across all symbols Remove stocks with 10% missing data Fill minor gaps with forward fill method Log data quality issues Script Execution: Step 3: Calculate Correlation and Beta Objective: Identify candidate pairs with strong linear relationships. Correlation Analysis: For each pair of stocks (i, j) in the universe: 1. Calculate Pearson correlation coefficient (ρ) 2. Calculate rolling correlation (90 day window) for stability check 3. Filter pairs with ρ = 0.70 (strong positive correlation) Correlation Interpretation: ρ = 0.90: Very strong correlation (best candidates) ρ 0.70 0.90: Strong correlation (good candidates) ρ 0.50 0.70: Moderate correlation (marginal) ρ < 0.50: Weak correlation (exclude) Beta Calculation: For each candidate pair (Stock A, Stock B): Beta indicates the hedge ratio: Beta = 1.0: Equal dollar amounts Beta = 1.5: $1.50 of B for every $1.00 of A Beta = 0.8: $0.80 of B for every $1.00 of A Correlation Stability Check: Calculate correlation over multiple periods (6mo, 1yr, 2yr) Require correlation to be stable (not deteriorating) Flag pairs where recent correlation < historical correlation by 0.15 Step 4: Cointegration Testing Objective: Statistically validate long term equilibrium relationship. Why Cointegration Matters: Correlation measures short term co movement Cointegration proves long term equilibrium relationship Cointegrated pairs mean revert predictably Non cointegrated pairs may diverge permanently Augmented Dickey Fuller (ADF) Test: For each correlated pair: 1. Calculate spread: Spread = Price A (Beta × Price B) 2. Run ADF test on spread series 3. Check p value: p < 0.05 indicates cointegration (reject null hypothesis of unit root) 4. Extract ADF statistic for strength ranking Cointegration Interpretation: p value < 0.01: Very strong cointegration (★★★) p value 0.01 0.05: Moderate cointegration (★★) p value 0.05: No cointegration (exclude) Half Life Calculation: Estimate mean reversion speed: Half life < 30 days: Fast mean reversion (good for short term trading) Half life 30 60 days: Moderate speed (standard) Half life 60 days: Slow mean reversion (long holding periods) Python Implementation: Step 5: Spread Analysis and Z Score Calculation Objective: Quantify current spread deviation from equilibrium. Spread Calculation: Two common methods: Method 1: Price Difference (Additive) Best for: Stocks with similar price levels Method 2: Price Ratio (Multiplicative) Best for: Stocks with different price levels, easier interpretation Z Score Calculation: Measures how many standard deviations spread is from its mean: Z Score Interpretation: Z +2.0: Stock A expensive relative to B (short A, long B) Z +1.5: Moderately expensive (watch for entry) Z 1.5 to +1.5: Normal range (no trade) Z < 1.5: Moderately cheap (watch for entry) Z < 2.0: Stock A cheap relative to B (long A, short B) Historical Spread Analysis: Calculate mean and std dev over 90 day rolling window Plot historical z score distribution Identify maximum historical z score deviations Check for structural breaks (spread regime change) Step 6: Generate Entry/Exit Recommendations Objective: Provide actionable trading signals with clear rules. Entry Conditions: Conservative Approach (Z ≥ ±2.0): Aggressive Approach (Z ≥ ±1.5): Lower threshold for more frequent trades Higher win rate but smaller avg profit per trade Requires tighter risk management Exit Conditions: Primary Exit: Mean Reversion (Z = 0) Secondary Exit: Partial Profit Take Stop Loss: Time Based Exit: Step 7: Position Sizing and Risk Management Objective: Determine dollar amounts for market neutral exposure. Market Neutral Sizing: For a pair (Stock A, Stock B) with beta = β: Equal Dollar Exposure: Position Sizing Considerations: Total pair allocation: 10 20% of portfolio per pair Maximum pairs: 5 8 active pairs for diversification Correlation across pairs: Avoid highly correlated pairs Risk Metrics: Maximum loss per pair: 2 3% of total portfolio Stop loss trigger: Z score ±3.0 or 5% loss on spread Portfolio level risk: Sum of all pair risks ≤ 10% Step 8: Generate Pair Analysis Report Objective: Create structured markdown report with findings and recommendations. Report Sections: 1. Executive Summary Total pairs analyzed Number of cointegrated pairs found Top 5 opportunities ranked by statistical strength 2. Cointegrated Pairs Table Pair name (Stock A / Stock B) Correlation coefficient Cointegration p value Current z score Trade signal (Long/Short/None) Half life 3. Detailed Analysis (Top 10 Pairs) Pair description Statistical metrics Current spread position Entry/exit recommendations Position sizing Risk assessment 4. Spread Charts (Text Based) Historical z score plot (ASCII art) Entry/exit levels marked Current position indicator 5. Risk Warnings Pairs with deteriorating correlation Structural breaks detected Low liquidity warnings File Naming Convention: Example: pair trade analysis Technology 2025 11 08.md Output find pairs.py creates the requested parent directory and writes one JSON object with metadata and pairs keys. Each pair includes the correlation, hedge ratio, ADF result, half life, current z score, signal, and generation timestamp. Progress and a ranked summary are written to stdout. File system errors produce a concise stderr message and a nonzero exit. analyze spread.py writes a single pair statistical report to stdout and does not create files. Both commands reject invalid or non finite thresholds, insufficient lookback windows, and duplicate symbols before making API requests. Missing statsmodels produces an install command on stderr without a traceback. Quality Standards Statistical Rigor Minimum Requirements for Valid Pair: ✓ Correlation ≥ 0.70 over 2 year period ✓ Cointegration p value < 0.05 (ADF test) ✓ Spread stationarity confirmed ✓ Half life < 90 days ✓ No structural breaks in recent 6 months Red Flags (Exclude Pair): Correlation dropped 0.20 in recent 6 months Cointegration p value 0.05 Half life increasing over time (mean reversion weakening) Significant corporate events (merger, spin off, bankruptcy risk) Liquidity concerns (avg volume < 500K shares/day) Practical Considerations Transaction Costs: Assume 0.1% round trip cost per leg Total cost per pair = 0.4% (entry + exit, both legs) Minimum z score threshold should exceed transaction costs Short Selling: Verify stock is shortable (not hard to borrow) Factor in short interest costs (borrow fees) Monitor short squeeze risk Execution: Enter/exit both legs simultaneously (avoid leg risk) Use limit orders to control slippage Pre locate shorts before entry Available Scripts scripts/find pairs.py Purpose: Screen for cointegrated pairs within a sector or custom list. Usage: Parameters: 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: pair analysis.json ) api key : FMP API key (or set FMP API KEY env var) Output: scripts/analyze spread.py Purpose: Analyze a specific pair's spread behavior and generate trading signals. Usage: Parameters: 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 key Output: Current spread analysis Z score calculation Entry/exit recommendations Position sizing Historical z score chart (text) Reference Documentation references/methodology.md Comprehensive guide to statistical arbitrage and pair trading: Pair Selection Criteria : How to identify good pair candidates Statistical Tests : Correlation, cointegration, stationarity Spread Construction : Price difference vs price ratio approaches Mean Reversion : Half life calculation and interpretation Risk Management : Position sizing, stop losses, diversification Common Pitfalls : Survivorship bias, look ahead bias, overfitting references/coin

pair-trade-screener