The model had Omega>1 (profitable trade selection) but MaxDD 41-50%
(catastrophic drawdown timing), causing negative total returns despite
winning trades. Root cause: zero reward gradient between 0% and 25% DD.
The only drawdown consequence was the hard capital floor at 25% which
terminates the episode with reward=-10.
Fix: compute_drawdown_penalty() in trade_physics.cuh — smooth linear
ramp from 0 at dd_threshold (2%) to -5.0 at the capital floor (25%).
Applied every step, not just at trade exit, so the model learns to
reduce position size DURING drawdowns.
- Added compute_drawdown() and compute_drawdown_penalty() to trade_physics.cuh
- Wired dd_threshold and w_dd from config through to CUDA kernel
- Added to all 3 TOML profiles (smoketest, localdev, production)
Early results: MaxDD dropped from 88.9% → 36.6% by epoch 3.
Q-values went negative in drawdown states — the model is learning.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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