pair-trade-screenerStatistical 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.
Install via ClawdBot CLI:
clawdbot install Veeramanikandanr48/pair-trade-screenerGrade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://site.financialmodelingprep.com/developer/docs/historical-price-fullAudited Apr 16, 2026 · audit v1.0
Generated Mar 1, 2026
A quantitative hedge fund screens for cointegrated pairs within the technology sector, such as software giants, to execute market-neutral trades. They use the skill to identify pairs with strong correlation and cointegration, then set up trades based on z-score deviations for mean-reversion profits.
An asset manager in the financial industry uses the skill to find pairs among regional banks or insurance stocks to hedge sector exposure. By analyzing correlation and cointegration, they construct market-neutral portfolios that reduce overall risk while capturing relative value opportunities.
A proprietary trading firm focuses on the healthcare sector, screening for cointegrated pairs among pharmaceutical or biotech stocks. They leverage the skill's backtesting and z-score calculations to generate entry/exit signals for short-term mean-reversion trades in volatile markets.
An energy-focused investment fund employs the skill to identify pairs within the energy sector, such as oil and gas companies, for statistical arbitrage. They use cointegration testing and spread analysis to build market-neutral strategies that profit from price divergences independent of broader market trends.
A retail trading desk uses the skill to screen for pairs in the consumer discretionary sector, like automotive or retail stocks, for mean-reversion opportunities. They apply correlation stability checks and half-life calculations to optimize trade timing and manage risk in dynamic market conditions.
A hedge fund specializing in statistical arbitrage uses this skill to systematically identify and trade cointegrated pairs. They generate revenue through market-neutral strategies that capture small, consistent profits from mean-reversion, with fees based on assets under management and performance.
A proprietary trading firm employs the skill to develop automated pair trading algorithms for in-house capital. Revenue is derived from direct trading profits by executing high-frequency or short-term mean-reversion trades based on the skill's signals and backtesting insights.
An asset management firm integrates this skill into advisory services for institutional clients seeking market-neutral exposure. They offer customized pair trading strategies and portfolio construction, generating revenue through consulting fees and a share of client investment returns.
💬 Integration Tip
Ensure access to reliable historical price data APIs and implement robust data validation to handle missing values, as the skill relies on clean data for accurate correlation and cointegration analysis.
Scored Apr 19, 2026
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