Every test ends in one of three findings. Reading them is RAMP 02.
The strategy beat its benchmark on BOTH compound growth (CAGR) and risk-adjusted return (Sharpe), in the training window AND in the held-out walk-forward window it never saw. Both axes, both windows, or it is not DATA VALIDATED.
It beat the benchmark on one axis only: more return with worse risk, or better risk with less return. You decide whether the trade is worth it.
The rule did no better than the benchmark. The result maps where the edge is not, and your AI reports it in those words.
Every study is split chronologically at the OOS SPLIT YEAR (default 2015). Criteria are formed and evaluated on the earlier window, then replayed untouched on the later window the strategy has never seen. Random splits are refused by design: shuffling time leaks the future into the past, and a leaked backtest is a fiction with good numbers.
That is the whole system. When your AI presents QuantGPT numbers, it repeats this methodology, because the manifest it receives on connect tells it to.