Validation: Walk-Forward, Monte Carlo, Overfitting

Testing whether a backtest result is likely to survive new data — out-of-sample periods, walk-forward analysis, Monte Carlo resampling and overfitting checks.

What you will be able to do

Split data into in-sample and out-of-sample periods, and respect the split.
Run a walk-forward analysis and interpret only its unseen segments.
Use Monte Carlo resampling to estimate the range of drawdowns and outcomes.
Recognise overfitting from parameter searches, and reduce it.

Planned lessons

1
In-sample and out-of-sample
Holding data back, and why you only get to use it once.
Coming next
2
Walk-forward analysis
Re-fitting on rolling windows and judging the strategy only on the segments it never saw.
Coming next
3
Monte Carlo resampling
Shuffling and bootstrapping trades to see the spread of paths a strategy could have taken.
Coming next
4
Parameter sensitivity and overfitting
Heatmaps, robust plateaus, and counting how many variants you really tried.
Coming next
5
Regimes and stress periods
Checking behaviour across trending, ranging and high-volatility stretches of data.
Coming next