09202aa991
Step 1 of β migration — Option B (collector-native): collector loads its own CudaFunction handles for the 3 SP14 producer kernels plus their sub-kernels (4 total: aux_dir_acc_reduce, aux_pred_to_isv_tanh, q_disagreement_update, alpha_grad_compute). Mirrors SP13 hold_rate pattern at gpu_experience_collector.rs:1820. Cleaner than cross-component launcher calls (avoids trainer-stream / collector-stream race; no signature surgery on the existing trainer launchers). Cubin static decls flipped to pub(crate) so the collector can re-load on its own stream. Compile clean; sp14_oracle_tests pass (2/2 non-GPU; GPU-gated 7 ignored on RTX 3050 Ti host). No launches yet — additive infrastructure commit. 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;