pair-trading

Mean reversion trading signals are generated by computing the price ratio and Z-score of two correlated instruments. When the spread Z-score crosses configurable entry thresholds, it triggers opposing long-short positions that close as the ratio normalizes.

Yes, you can calculate the Z-score spread for crypto pairs using pandas. The workflow computes the rolling mean and standard deviation of the price ratio for aligned OHLCV data to signal divergence and convergence events for assets like BTC-USDT and ETH-USDT.

The best way to set entry and exit thresholds is to tune the entry_z and exit_z parameters. This balances responsiveness and noise filtration, triggering opposite positions when the Z-score crosses bands like ±2 and closing them when it reverts within 0.5.

Yes, this pair trading approach requires aligned OHLCV data for exactly two symbols. Ensuring timestamp alignment is a necessary prerequisite to accurately calculate the spread ratio, rolling statistics, and Z-score for mean-reversion signals.

You hedge risk by taking opposing directional exposure on two correlated instruments based on their price ratio. When the Z-score deviates from the mean, you open opposite long-short positions to capture the convergence when the spread normalizes.

A pair trading Z-score strategy generates noisy signals when the lookback window is too short or entry thresholds are too narrow. Adjusting the rolling lookback parameters and widening the entry_z band filters market noise and improves signal reliability.