pair-trading

To automate pair trading signals, this Skill calculates the Z-score of the price ratio between two correlated instruments to identify mean reversion opportunities. You configure the lookback period and Z-score thresholds to generate buy and sell signals.

A Z-score threshold in mean reversion trading defines the statistical deviation required to trigger entry or exit signals for a price ratio. Setting these thresholds allows you to manage risk by specifying when the spread between two instruments diverges enough to trade.

You calculate price ratios for a pair trading strategy using Python by processing historical price data with pandas and numpy. The Skill computes the ratio between two instruments and monitors its deviation from the historical mean to signal trades.

Yes, you can apply this pair trading approach to both stocks and cryptocurrencies. The Skill supports any two correlated financial instruments, requiring only price data and configuration parameters to generate mean reversion signals.

Yes, you need pandas and numpy installed to run Z-score calculations for pair trading. These dependencies handle the numerical processing required to compute price ratios, calculate statistical deviations, and generate trading signals.

To start mean reversion trading, you must specify at least two correlated instruments, a lookback period for the historical mean, and Z-score thresholds for entry and exit points. These parameters drive the automated signal generation.