★ 20 Ways to Generate Stock Investment Profits with AI - 6. Rigorous Backtesting for AI Strategies

 

6. Rigorous Backtesting for AI Strategies

Introduction
A backtest validates your AI-driven ideas—but improper methodology can lead to “overfitted” strategies that fail in live trading.

Common Pitfalls

• Look-ahead Bias: Using future data inadvertently in feature creation.

• Survivorship Bias: Excluding delisted or bankrupt companies from your dataset.

• Overfitting: Tuning too many hyperparameters to historical noise.

Best Practices

1. Data Hygiene: Freeze your universe at each point in time; include delisted stocks from CRSP.

2. Walk-Forward Testing: Retrain your model every quarter on rolling windows, then test on the next period.

3. Transaction Costs: Model realistic slippage and commissions (e.g., 0.02% per trade).

4. Out-of-Sample Holdout: Reserve the latest 20% of data for final validation only.

5. Robustness Checks: Stress-test under high-vol regimes (2008, 2020) via Monte Carlo simulations.

Case Insight
A momentum strategy showing 18% annual returns in-sample fell to 4% after accounting for realistic costs and walk-forward testing—highlighting the need for rigorous validation.

Conclusion
Meticulous backtesting separates robust, deployable models from historical curiosities—protecting your capital when you go live.