296ba92282
Step 3 of β migration: collector now runs aux_next_bar_forward on rollout state every step. Label producer (new thin variant aux_sign_label_per_step_kernel) derives sign(price[t+1] - price[t]) per env using bar = episode_starts[ep] + t. Aux predictions feed the EGF kernel chain (step 4), NOT the Q-head's input (rollout Q-head still sees raw h_s2, dir_qaux_concat_ptr remains 0u64). Placement: AFTER captured forward graph, BEFORE expected_q kernel. Same-stream serial ordering reads exp_h_s2_f32 populated by forward_online_f32 inside the captured graph. Cold-start gated on trainer_params_ptr != 0 to skip the test-scaffold path where the trainer hasn't wired its params yet. Files added: aux_sign_label_per_step_kernel.cu (66 lines). Files modified: build.rs (+8 lines, register cubin), gpu_experience_collector.rs (+106 lines: struct field, cubin static, load in new(), per-step launch block). Compile clean; sp14_oracle_tests 2/2 non-GPU pass. 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;