The experience collector used raw_next (NEXT bar's close) for P&L and
equity, creating a 1-bar misalignment between actions and rewards.
The model couldn't learn which actions produce which outcomes.
val_Sharpe=24 (backtest uses raw_close correctly) but training Sharpe=0
(experience collector used raw_next, shifting reward by 1 bar).
Fix: ALL portfolio computations use raw_close only. raw_next removed
from reward path entirely. Rewards are per-trade only (position change
= realized P&L, holding = tiny cost, flat = zero).
This is the root cause of training Sharpe being capped at ~0 across
ALL runs regardless of model complexity or component changes.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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