walk-forward-runnerlisted

# Walk-Forward Runner Use this skill when a time-series model or rule-based strategy needs chronological validation without leakage. It gives the user rolling train/validation windows and a simple rank-divergence verdict for parameter robustness. ## When to invoke - User says: "walk forward validation" / "time series cross validation" / "no leakage split" - Code in the conversation uses: timestamped rows, rolling windows, chronological model evaluation, or parameter sweeps. ## When NOT to invoke - The dataset is too short for multiple folds. - The task is ordinary shuffled cross-validation on independent rows. ## Concrete example User input: ```text I tested lookback values on 3 years of daily rows. Flag which settings are overfit. ``` Output: ```text Param value Train rank Val rank Verdict lookback=20 1 1 STRONG ROBUST lookback=50 2 6 OVERFIT (good train, bad val) lookback=10 5 3 weak both ``` Code: ```python from datetime import timedelta # Copy assets/walk_forward.py into your project, then: from walk_forward import walk_forward_folds, verdict folds = list(walk_forward_folds(rows["timestamp"], timedelta(days=365), timedelta(days=90), timedelta(days=30))) label = verdict(train_rank=2, val_rank=6, n=6) ``` ## Pattern to apply 1. Sort rows by timestamp before splitting. 2. Roll fixed train and validation windows forward by a step size. 3. Assert validation starts at or after train end.