8f218cab24
Two independent bugs surfaced by Class C (frame-shift) + Class A (hardcoded bounds) audits, both implicated in the months-long WR-stuck-at-46-48% plateau across 11 superprojects. ## Bug 1: Replay buffer Sutton's deadly triad experience_kernels.cu:2275 was writing the original INTENT action to out_actions, but the reward in the replay buffer was computed from the REALIZED position (post-enforcement: Kelly cap, capital floor, trail-stop, broker cap can clamp Long→Flat). Replay buffer stored (s, intent, r_realized, s'). Q(s, Long) was therefore trained against r(s, Flat) whenever env clamped the intent. This explains the train_active_frac=0.40 vs val_active_frac=0.05 gap: train measures intent (40% Long/Short), eval measures realized (5% Long/Short). The 8× gap is env physics draining intent. Fix: after unified_env_step_core resolves actual_dir_core/actual_mag_core, overwrite out_actions[out_off] with the realized action (same encoding as backtest_env_kernel.cu:323-330, which has been doing it correctly all along). Order/urgency preserved from intent. ## Bug 2: Kelly cap update kernel ignored existing ISV warmup floor kelly_cap_update_kernel.cu:53 hardcoded the kelly_f floor at 0.0f. Cold path (per-epoch boundary). Per project_magnitude_eval_collapse_kelly_capped, this collapses kelly_cap to 0 → max position pinned to Quarter for cold start. The val-mag pathology. The warmup floor producer (ISV[KELLY_WARMUP_FLOOR_INDEX=330], SP9 Fix 37) was already populated and consumed by the per-step path at trade_physics.cuh:377-384, but this cold-path kernel never read it. Partial wiring. Fix: replace fmaxf(kelly_f, 0.0f) with fmaxf(kelly_f, isv[330]). One-line change. ## Predicted effect - train_active_frac and val_active_frac should converge (Bug 1 inflated train by counting overridden intents) - Magnitude distribution should escape Quarter-only (Bug 2 was pinning it) - WR ceiling at 46-48% may finally move (Bug 1 broke Bellman consistency; Bug 2 prevented edge realization) Falsification: 5-epoch L40S smoke. If unmoved by ep5, the plateau is deeper still (Class A P0-A REWARD_POS_CAP/NEG_CAP next). 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;