mean-reversion

Mean-reversion trading strategies use Z-score thresholds and Bollinger bands to identify when asset prices deviate from their statistical equilibrium, triggering automated entries and exits. You must define rolling statistics and clear execution rules for robust signal generation.

The Ornstein-Uhlenbeck process is used in pairs trading to statistically model the mean-reverting behavior of a spread between two closely related assets. It helps calculate half-life estimation, allowing you to predict how quickly a spread returns to equilibrium for signal generation.

Yes, you can backtest a spread trading strategy and estimate half-life to evaluate performance and enforce risk controls. The framework applies statistical modeling to validate reversion speed and guard against regime changes before deploying configurable thresholds.

This mean-reversion skill supports single instruments, spreads, and pairs trading. It designs strategies and generates execution signals across these configurations by applying Z-score, Bollinger bands, and Ornstein-Uhlenbeck models to identify equilibrium deviations.

You should avoid using a mean-reversion strategy during market regime changes where prices trend strongly instead of reverting. The skill includes risk controls and backtesting to detect these limitations, preventing automated entries when statistical reversion properties break down.

The best way to automate pair-trade entries is to configure mean-reversion thresholds on liquid assets and run a backtest first. You apply Z-score or Bollinger band limits to a spread, observe reversion signals, and deploy using the safe execution framework.