AI Trading Agent for Mean Reversion

Mean reversion needs strict band and z-score discipline and counter-trend nerve that humans struggle with. Buying a stock that just dropped 5% takes psychological fortitude. An agent enters without hesitation when the z-score hits the threshold. It also exits at the mean without greed for more. Crypto bot platforms focus on grid and DCA bots that are mechanically different from mean reversion. They execute repetitive buy orders at intervals, not z-score based entries at extremes. An agent that handles true mean reversion across markets occupies an open position in the competitive landscape.

I tested a mean reversion strategy on SPY using Pineify. The logic: buy when SPY closes 2 standard deviations below its 20-period moving average on the 1-hour chart. Sell when it returns to the moving average. I described this to Pineify and the generated Pine Script compiled cleanly. The optimizer tested 180 combinations of lookback period and standard deviation multiplier. The best result used a 14-period lookback and 1.8 standard deviations, not the standard 20 and 2. That combination produced 35 trades with a profit factor of 1.7. The revelation was that 2 standard deviations triggered too rarely on SPY, especially during low VIX regimes. Dropping to 1.8 increased the trade count by 60% without significantly reducing the win rate. I am planning to test this with the agent when it launches.