Managing AI Strategy Tuning, Failure, and Live Trading Risks
Summary
This note offers practical guidance for developing and operating AI-driven equity strategies. It recommends judging possible strategy failure by comparing live maximum drawdown with the largest drawdown observed in a long backtest that spans a full bull and bear cycle. A larger live drawdown may indicate failure or overfitting; paper trading before deployment and rotating among strategies are suggested responses.
For tuning, the note advises holding some variables fixed while changing others, since changing factors, training periods, or backtest windows together can make results difficult to interpret. For live execution, it flags slippage when capital is large and notes that opening-auction orders may help small-capital short-term strategies obtain an opening fill, though buying at 9:30 may yield an unfavorable price. It also advises screening out stocks that may become specially treated during the annual-report season, using prior-year losses as an example. The guidance is brief and provides no measured performance evidence or detailed validation procedure; its drawdown rule is a warning signal rather than a definitive test.
Key ideas
- Compare live drawdown with the worst drawdown in a long backtest as a warning of possible strategy failure or overfitting.
- Paper trade before deploying a strategy with real capital.
- Change a limited set of tuning variables at a time to make results easier to interpret.
- Account for slippage and opening-price tradeoffs when planning live execution.
- Screen for potential special-treatment stocks during concentrated annual-report season.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.