Backtesting is the step that validates whether an AI trading strategy can survive real market conditions. Done poorly, it creates false confidence and hidden risk.
Key Backtesting Principles
- Clean data: remove lookahead bias and align timestamps precisely.
- Out-of-sample testing: validate on data the model never saw during training.
- Walk-forward analysis: re-train and re-test the model over rolling windows.
Avoiding Common Pitfalls
The three biggest mistakes are: using future data in feature construction, ignoring transaction costs, and failing to test over different volatility regimes.
How We Use It
In MicroStocks, every AI signal is backtested with a conservative execution model, including slippage estimates for low-liquidity names and monthly rebalancing scenarios for longer-term signals.