Enumerates every term that contributes to training or validation reward,
classifies each as P&L-aligned (keeper) or behavioral (remover), with
source line references into experience_kernels.cu and backtest_env_kernel.cu.
Findings:
- 7 P&L-aligned terms to preserve in the unified env (core return,
tx_cost, drawdown, inventory, churn, capital floor, + vol normalization)
- 8 active behavioral training-only terms to delete:
* order_credit_weight (rewards theoretical limit-order savings,
double-counts actual tx_cost)
* risk_efficiency_weight (double-counts drawdown asymmetrically)
* urgency_credit_weight (redundant with core return)
* kelly_sizing_weight (rewards matching a formula, not outcomes)
* micro_reward_scale (OFI-momentum signal follower)
* commitment_lambda (triple-counts churn)
* reward_noise_scale (belongs at gradient level, not reward level)
* position_entropy_weight (changes what "optimal" means)
- 4 dormant/dead terms to clean up (w_dsr, exit_timing, ofi_reward,
opp_cost_scale — all plumbing with no kernel effect)
Biggest offenders by magnitude: urgency_credit and risk_efficiency can
dominate the core signal on a single positioned bar.
Phase 2 (unified_env_kernel implementation) can now proceed with a
concrete deletion list, not a judgment call.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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