f64eeb64da
C.4 timing bonus (experience_kernels.cu) and D.4b regime penalty multiplied by the trade-cumulative |reward| / |final_pnl| as an unbounded multiplicand. Across trades this compounds — bonus values inflate as Q values inflate, then bonus rewards inflate Q values further (Bellman bootstraps off shaped reward). Per pearl_one_unbounded_signal_per_reward + feedback_isv_for_adaptive_bounds: replace |reward|/|final_pnl| with ISV[Q_DIR_ABS_REF_INDEX]-bounded variant. The unbounded multiplicand becomes the direction-branch Q-scale EMA (already-tracked, gradient-decoupled), not the trade-cumulative shaping output. Cap is adaptive (matches Q magnitude as it evolves) and breaks the multi-trade compounding loop. Discovered during Plan C Phase 2 smoke diagnosis (researcher report 2026-04-29 a25f669e9df953174).a52d99613baseline also hits bonus=235 — pre-existing structural pathology, not Plan-C-specific. Other shaping sites (D.4a persist, B.2 novelty, D.4c stable) already have all factors bounded — no fix needed. Surgical change set: - experience_env_step gains final `int q_dir_abs_ref_idx` param. - C.4 (line 2521): pnl_unit = |final_pnl|/max(ISV[21], eps); pnl_capped = min(pnl_unit, 1) * ISV[21]. - D.4b (line 2581): same pattern, reward_unit/reward_capped. - gpu_experience_collector.rs:3800 wires Q_DIR_ABS_REF_INDEX as i32. No new ISV slot, no new producer kernel, no layout-fingerprint shift — ISV[21] already produced by q_stats_kernel.cu since Plan 1. Predicted impact: - bonus EMA drops O(100-256) -> O(0.05-2.0) - rc[5] -> Bellman-target optimism loop broken - Plan C smoke: Q-drift kill at F0 ep2 likely no longer fires -a52d99613baseline: same effect; validates pre-existing fix 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;