Agent skill

pair-trade-screener

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 opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.

BaggaT236github.com/BaggaT236GitHub ↗
claude-codeships scriptsMIT
Install
npx skills add BaggaT236/AI-Trading-Skills --skill pair-trade-screener --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 8
SKILL.md size: 18 KB
Bundled scripts: yes
Path: skills/pair-trade-screener/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 118
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Analysis Workflow
  4. Step 1: Define Pair Universe
  5. Step 2: Retrieve Historical Price Data
  6. Step 3: Calculate Correlation and Beta
  7. Step 4: Cointegration Testing
  8. Step 5: Spread Analysis and Z-Score Calculation
  9. Step 6: Generate Entry/Exit Recommendations
  10. Step 7: Position Sizing and Risk Management
  11. Step 8: Generate Pair Analysis Report
  12. Quality Standards
  13. Statistical Rigor
  14. Practical Considerations
Ships with 7 files
  • README.md
  • references/cointegration_guide.md
  • references/methodology.md
  • scripts/analyze_spread.py
  • scripts/find_pairs.py
  • scripts/tests/conftest.py
  • scripts/tests/test_find_pairs_screener.py
Commands it runs
python scripts/fetch_price_data.py --sector Technology --lookback 730
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 \
Analyze specific pair
python scripts/analyze_spread.py --stock-a AAPL --stock-b MSFT
Custom lookback period
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About this skill
What does the pair-trade-screener skill do?

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 opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.

How do I install it?

Run `npx skills add BaggaT236/AI-Trading-Skills --skill pair-trade-screener --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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