pair-trade-screener

Name: pair-trade-screener
Availability: InStock
Author: fabio29t

Screen stock pairs for cointegration and generate market-neutral trade signals.

2|Updated Mar 4, 2026

Search Tags:#stock screening #cointegration #mean reversion #statistical arbitrage #pair trading #market neutral

Ready to run

npx skills add https://github.com/Fabio29T/Trading-Skills --skill pair-trade-screener-fabio29t

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pair-trade-screener
Source: https://github.com/Fabio29T/Trading-Skills/tree/main/skills/pair-trade-screener
Command: npx skills add https://github.com/Fabio29T/Trading-Skills --skill pair-trade-screener-fabio29t

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, statsmodels, requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of identifying statistically sound pair trading opportunities, enabling market-neutral strategies that profit regardless of market direction.

Core Features & Use Cases

Automated Pair Discovery: Screens sectors or custom lists for cointegrated stock pairs.
Statistical Analysis: Performs correlation, cointegration (ADF test), and mean-reversion (half-life) analysis.
Trade Signal Generation: Provides clear entry/exit recommendations based on z-score thresholds.
Use Case: A trader wants to implement a market-neutral strategy and asks the Skill to find pairs in the technology sector that are currently mispriced and likely to revert to their mean.

Quick Start

Use the pair-trade-screener skill to find pair trading opportunities in the technology sector.

Frequently Asked Questions about pair-trade-screener

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I find cointegrated stock pairs for statistical arbitrage?▼

To find cointegrated stock pairs for statistical arbitrage, you can screen specified sectors or custom stock lists to identify statistically significant relationships. This process calculates cointegration p-values and correlation to filter pairs suitable for market-neutral trading strategies.

What is the best way to generate mean reversion trade signals for a pair trading strategy?▼

Mean reversion trade signals are generated by calculating the current z-score of the pair's price spread. The screening process evaluates half-life and applies z-score thresholds to provide clear entry and exit recommendations for market-neutral trades.

Do I need a specific data provider to run a cointegration analysis on stock pairs?▼

Cointegration analysis requires historical price data, which can be retrieved via Financial Modeling Prep API integration. You need an FMP API key to automate data retrieval for calculating statistical metrics like beta and half-life.

How does statistical arbitrage screening handle market direction volatility?▼

Statistical arbitrage screening identifies market-neutral pairs, meaning the strategy profits from the relative price movement between two cointegrated stocks. This market-neutral approach ensures profitability regardless of overall market direction.

What statistical tests are used to identify profitable pairs trading opportunities?▼

Pairs trading opportunities are identified using correlation analysis, the Augmented Dickey-Fuller ADF test for cointegration, and half-life calculations. These statistical tests confirm that the stock pair's price spread exhibits mean-reverting behavior.

Can I use a custom stock list instead of a predefined sector for pair screening?▼

Yes, pair screening supports custom stock lists as well as predefined sectors. You can provide a custom list of tickers to identify cointegrated pairs and generate z-score trade signals tailored to your specific portfolio.