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.
