pair-trade-screener
Pair Trade Screener detects statistical arbitrage opportunities by identifying cointegrated stock pairs within sectors and analyzing their spread behavior. The skill uses correlation analysis, cointegration testing, and z-score calculations to generate market-neutral trading signals based on mean-reversion patterns. Designed for investors seeking uncorrelated trading strategies that profit regardless of overall market direction.
Pair Trade Screener identifies cointegrated stock pairs for statistical arbitrage and market-neutral trading strategies.
AI-generated summary based on this skill's SKILL.md
Install
tradermonty/claude-trading-skills/pair-trade-screener · repository language: Python
git clone https://github.com/tradermonty/claude-trading-skills
cp -r claude-trading-skills/skills/pair-trade-screener ~/.claude/skills/pair-trade-screenerFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How does pair-trade-screener find cointegrated stock pairs?
Pair Trade Screener identifies cointegrated stock pairs by applying statistical cointegration testing across sector universes. The skill analyzes long-term equilibrium relationships between stocks to isolate pairs that move together systematically, enabling detection of statistical arbitrage opportunities where temporary spread deviations can be exploited for mean-reversion trades.
What are mean reversion pair trades and entry/exit signals?
Pair Trade Screener analyzes pair spread behavior using z-score calculations to generate mean-reversion entry and exit signals. When spreads deviate significantly from their historical mean, the skill identifies overbought or oversold conditions. Traders enter when z-scores exceed thresholds and exit as spreads revert to equilibrium, capturing profits from temporary mispricings.
Can pair-trade-screener screen sectors for market-neutral opportunities?
Yes, Pair Trade Screener screens sectors for market-neutral pair trading opportunities by identifying cointegrated pairs within sector universes. This sector-focused approach isolates pairs with strong fundamental linkages, reducing idiosyncratic risk and improving signal reliability for constructing hedged portfolios that profit independent of overall market direction.
How does pair-trade-screener calculate hedge ratios and position sizing?
Pair Trade Screener calculates hedge ratios and position sizing for pair trades by quantifying the optimal long-short weighting between cointegrated stocks. These calculations ensure balanced exposure management, allowing traders to construct properly hedged positions that neutralize systematic market risk while maintaining exposure to relative value opportunities.
What makes pair-trade-screener useful for statistical arbitrage?
Pair Trade Screener combines correlation analysis, cointegration testing, and z-score calculations to detect statistical arbitrage opportunities systematically. By identifying stocks that move together and analyzing spread deviations, the skill generates market-neutral trading signals uncorrelated with overall market direction—ideal for investors seeking diversified, uncorrelated return streams.
Which stocks move together according to pair-trade-screener?
Pair Trade Screener identifies which stocks move together by testing cointegration relationships across sector universes. The skill detects pairs with strong long-term equilibrium linkages—typically competitors or companies in similar industries—revealing which stocks exhibit synchronized price behavior suitable for relative value trading strategies.
SKILL.md
rendered from the published skill — quoted content, verbatim
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 -
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File tree — 8 files
skills/pair-trade-screener/README.md
skills/pair-trade-screener/SKILL.md
skills/pair-trade-screener/references/cointegration_guide.md
skills/pair-trade-screener/references/methodology.md
skills/pair-trade-screener/scripts/analyze_spread.py
skills/pair-trade-screener/scripts/find_pairs.py
skills/pair-trade-screener/scripts/tests/conftest.py
skills/pair-trade-screener/scripts/tests/test_find_pairs_screener.py