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Replay recorded AI decisions from decision_records under alternative risk-control and throttle parameters, then search the 15-dim space with Optuna multivariate TPE. - extract.py: parse decision cycles, intents (incl. throttled ones), 15m OHLCV and per-cycle prices out of input prompts into CSVs - simulate.py: replay engine mirroring auto_trader_throttle.go semantics (margin-based PnL%% thresholds, intra-candle SL/TP trigger orders, opens/hour + reentry + margin gates); live-config replay reproduces the real account curve (-83%% sim vs -85%% actual) - search.py: TPE search (train/holdout split) + baselines + importances Findings: train-window optima do not survive holdout (overfit); only 2/800 trials are positive across all 3 time folds. Dominant lever is min_confidence (importance 0.79) — the edge problem is decision quality, not risk parameters.