394de7d434
Per Class A audit ranking, the highest-suspected-impact fix for the months-long WR-stuck-at-46-48% plateau across 11 superprojects. Hardcoded REWARD_POS_CAP=+5.0f / REWARD_NEG_CAP=-10.0f (state_layout.cuh:266-267) was structurally clipping the upper tail of realized alpha. Controller signals (sharpe EMA, var_q, q_gap) all derive from this CAPPED buffer, so the controller cannot select for trades it cannot see. Selectivity gradient evaporates — small wins clip to +5 alongside large wins also clipping to +5. The state_layout comment lines 261-264 explicitly deferred Phase 2 (ISV-driven) "IF Phase 1 validation reveals adaptive need". Phase 1 has been running 11 SPs without budging WR — adaptive need revealed. Architecture: - 2 new ISV slots [452..454): REWARD_POS_CAP_ADAPTIVE, REWARD_NEG_CAP_ADAPTIVE. - Producer kernel reward_cap_update_kernel.cu: block-tree-reduce Welford `mean + Z_99 × sigma` p99 estimator over winning realized returns + conservative `max(p99, max_win)` takeover, × 1.5 safety factor → POS cap. NEG cap = -2 × POS cap (preserves Kahneman 2:1 asymmetry per pearl_audit_unboundedness_for_implicit_asymmetry — asymmetry stays, but moved from hardcoded scalar to producer-time multiplier; single source of truth, no consumer applies the 2× ratio itself). - Pearl-A first-observation bootstrap from sentinel (5.0 / -10.0, matching pre-P0-A hardcoded values for bit-identical cold-start). Welford EMA α=0.01 thereafter (slow blend — reward distribution is the foundation of training and shouldn't move fast). - Bounds: POS in [1, 50], NEG in [-100, -2] (Category-1 dimensional safety per feedback_isv_for_adaptive_bounds, NOT tuning). - 3 consumer sites migrated atomically per feedback_no_partial_refactor: experience_kernels.cu:3112-3114 (segment_complete cap), compute_sp15_final_reward_kernel.cu:163 (Stage 4 helper invocation), sp15_reward_axis_helpers.cuh:211 (sp15_apply_sp12_cap device fn signature change to take isv ptr). - Cold-start fallback: when ISV slot at sentinel OR outside [REWARD_POS_CAP_MIN_BOUND=1, REWARD_POS_CAP_MAX_BOUND=50], consumers fall back to original macros (still defined in state_layout.cuh). - 2 new device-ptr accessors on the experience collector (step_ret_per_sample_dev_ptr, trade_close_per_sample_dev_ptr) — reuses existing per-sample buffers; no new buffer allocated. - Per-epoch boundary launch (cold path) in training_loop.rs alongside launch_aux_horizon_chain. - Reset registry entries + dispatch arms in reset_named_state per the C.10 lesson (missing dispatch causes runtime crash). - Layout fingerprint seed updated: ISV_TOTAL_DIM 452→454 + AUX_PRED_HORIZON_BARS=450 + AVG_WIN_HOLD_TIME_BARS=451 (previously missing from seed) + REWARD_POS_CAP_ADAPTIVE=452 + REWARD_NEG_CAP_ADAPTIVE=453. Per feedback_isv_for_adaptive_bounds: every adaptive bound in ISV. Verification: - cargo check -p ml --tests --all-targets: clean (19 pre-existing warnings, 0 new). - sp14_isv_slots tests: 8/8 pass (4 layout + 4 fits-within). - sp14_oracle_tests with --features cuda --ignored: 8/8 pass (4 existing q_disagreement/dir_concat + 4 new P0-A tests covering Pearl-A bootstrap, no-winning-trades preservation, bounds clamping to [1,50], Welford α=0.01 EMA blend). - sp15_phase1_oracle_tests with --features cuda --ignored: 36/36 pass (no regression from sp15_apply_sp12_cap signature change). Cumulative WR-plateau fix series: - Class C bug 1 (8f218cab2): replay buffer intent→realized. - Class A P0-B (8f218cab2): Kelly warmup floor wiring. - Class A P0-C (316db416b): MIN_HOLD_TARGET adaptive. - Class A P0-A (this commit): REWARD_POS/NEG_CAP adaptive — restores upper-tail alpha to the controller's view. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;