Walk-Forward Validation AI Prompt

Design and execute a walk-forward validation framework to assess strategy robustness out-of-sample.

Strategy: {{strategy}}
Total data period: {{period}}
Parameters to optimize: {{parameters}}

1. Walk-forward validation framework:
   - Training window: {{training_length}} months (used for parameter optimization)
   - Test window: {{test_length}} months (out-of-sample evaluation)
   - Step: {{step_size}} months (how often to re-optimize)
   - Total OOS periods: (total_months - training_months) / step_size

   Process for each fold:
   1. Train: optimize parameters on training window to maximize {{objective}} (e.g. Sharpe)
   2. Freeze: lock the optimal parameters from the training window
   3. Test: evaluate the frozen strategy on the next test window
   4. Step: advance both windows by the step size
   5. Repeat until the end of data

2. Walk-forward variants:
   - Anchored (expanding window): training window grows over time. More data but may include stale regimes.
   - Rolling (fixed window): training window moves with a fixed length. Adapts to regime changes but discards old data.
   - Recommendation: compare both; if they diverge significantly, parameters are regime-dependent.

3. Concatenated OOS performance:
   - Concatenate all test period results into a single OOS return series
   - This is the most realistic performance estimate: uses only OOS data
   - Report: Sharpe, Calmar, max drawdown, win rate, and turnover on the OOS series

4. In-sample vs out-of-sample performance ratio:
   - IS Sharpe / OOS Sharpe: if > 2, significant overfitting
   - Minimum OOS Sharpe ≥ 50% of IS Sharpe: rough guideline for acceptable overfitting
   - If OOS performance is dramatically worse: the strategy is overfit, not robust

5. Parameter stability analysis:
   - Plot the optimal parameter value chosen at each training step over time
   - Are optimal parameters stable across windows or do they oscillate?
   - High instability → the strategy is sensitive to parameter choice → not robust
   - A strategy with robust parameters will show similar optimal values across training windows

6. Number of OOS periods required:
   - Need at least 30 OOS periods (folds) for statistical inference on OOS performance
   - With 30 periods at monthly frequency: 2.5 years of OOS data
   - With 3-month test windows: need 7.5 years of OOS data — this is a significant requirement

Return: walk-forward performance table (IS vs OOS per fold), concatenated OOS Sharpe and drawdown, parameter stability plots, and overfitting assessment.