pair-trade-screener

To find cointegrated stock pairs for statistical arbitrage, you can screen specific sectors or custom stock lists using automated cointegration tests. This process identifies statistically sound, market-neutral trading opportunities without requiring manual research.

Pair trading is a market-neutral strategy that identifies correlated stocks, calculates their spread, and expects mean reversion. Analysis uses z-scores and half-life calculations to generate entry and exit signals when the spread deviates from its historical mean.

You generate entry and exit signals for a market-neutral strategy by calculating z-scores and hedge ratios from cointegrated stock pairs. These metrics indicate when the spread between pairs reaches extreme deviations, signaling optimal trade execution points.

Yes, you can use Python and FMP API for pair trading backtesting. The workflow utilizes Python libraries like pandas, numpy, scipy, and statsmodels to fetch data via FMP API, perform spread backtesting, and validate statistical arbitrage strategies.

Pair trading screening works effectively with both custom stock lists and specific sectors. You can input a custom ticker list or target a specific sector like technology, and the screener will automatically detect cointegrated pairs and analyze their relationships.

Validating a pair trading strategy requires the Augmented Dickey-Fuller (ADF) test for cointegration, correlation analysis, and half-life of mean reversion calculations. These statistical tests confirm the long-term relationship between stocks and the viability of the spread.