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foxhunt/docs/superpowers
jgrusewski 3f3d32d5ba spec: trade-level reward attribution + exploration risk budget + homeostatic regularization
Two fundamental fixes for training Sharpe breakthrough:
1. Trade-level rewards: replace per-bar noise (SNR=0.01) with trade
   P&L attribution (SNR=0.1-0.5). C51 atoms model trade outcome
   distributions, not random walk noise.
2. Exploration risk budget: protection stack (CVaR, epistemic gate,
   commitment, DSR) scaled by iqn_readiness². Loose during exploration,
   tight when converged. Model can discover edges before being punished.

Also: homeostatic regularization spec (unified adaptive penalties).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-16 01:24:03 +02:00
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