Time-series shuffle breaks temporal order
An AI built this stock-signal backtest validation using k-fold CV. Backtest Sharpe is excellent; live trading loses money. Explain the leakage the shuffle introduces.
Implement
walk_forward_folds(n_rows: int, n_splits: int, embargo: int) → list[list[int]]Examples
in
[10,2,0]out[[0,4,4,7],[0,7,7,10]]in
[10,2,2]out[[0,2,4,7],[0,5,7,10]]What a strong answer looks like
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
0:00 of about 20 min
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.