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.
Why use this skill?
Use the Pair Trade Screener to identify cointegrated stock pairs, calculate z-scores, and automate your market-neutral trading strategies with statistical precision.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/veeramanikandanr48/pair-trade-screenerWhat This Skill Does
The pair-trade-screener is a sophisticated quantitative tool designed for OpenClaw users to identify, evaluate, and monitor statistical arbitrage opportunities. At its core, the skill performs rigorous data analysis on equity pairs by testing for cointegration—the condition where two assets move in tandem over the long term despite temporary price deviations. By calculating the spread between these assets and normalizing it via z-scores, the skill isolates moments when a pair has diverged beyond historical norms, signaling a potential mean-reversion trade.
Installation
You can install this skill directly through the OpenClaw CLI using the following command:
clawhub install openclaw/skills/skills/veeramanikandanr48/pair-trade-screener
Use Cases
This tool is specifically built for traders and analysts seeking market-neutral strategies. Use it to:
- Identify pairs within the same sector (e.g., comparing major players in Tech or Energy) to hedge directional market risk.
- Perform statistical screening to find high-probability mean-reversion candidates.
- Generate actionable entry and exit signals based on z-score thresholds (typically +/- 2 standard deviations).
- Build portfolios that are uncorrelated to the broader market, allowing for returns regardless of whether the S&P 500 is bullish or bearish.
Example Prompts
- "Find pair trading opportunities in the semiconductor industry with high cointegration scores."
- "Analyze the spread between KO and PEP; should I enter a mean-reversion trade based on the current z-score?"
- "Scan the Financials sector for statistically significant pairs that are currently trading at a 2-standard-deviation spread."
Tips & Limitations
To maximize the efficacy of your trades, consider these best practices:
- Data Integrity: The skill requires at least 252 days of historical data; avoid using symbols with significant gaps or recent IPOs.
- Execution: While the skill provides entry/exit signals, it does not execute trades; ensure you have appropriate risk management settings in your brokerage account.
- Macro Factors: Statistical arbitrage can break down during extreme market regime changes. Always verify that a pair's relationship is fundamental (e.g., similar business models) rather than just a historical statistical fluke. Use the tool as a decision-support system, not as a replacement for fundamental due diligence.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-veeramanikandanr48-pair-trade-screener": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: network-access, external-api
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