83398cd301
The epoch_vol_ema, epoch_dsr_mean, and epoch_dsr_var variables were loaded from global memory at kernel start but never wired into the actual computation. The EMA normalizer (ema_mean/ema_var) and DSR accumulators (dsr_A/dsr_B) were initialized with hardcoded constants, so epoch state round-tripped unchanged. Changes: - Seed ema_mean/ema_var from epoch_dsr_mean/epoch_dsr_var at kernel start - Seed dsr_A/dsr_B from epoch_dsr_mean/epoch_dsr_var at kernel start - Seed ema_init/dsr_initialized from epoch_step_count > 0 (skip cold start on subsequent epochs) - Add local_vol_ema/local_median_vol seeded from epoch_vol_ema/epoch_median_vol - Update vol EMA each timestep from market feature index 3 (log close return), mirroring CPU DQNTrainer::vol_ema / median_vol logic - At writeback, write actual computed dsr_A/dsr_B or ema_mean/ema_var (conditioned on use_dsr), and computed local_vol_ema/local_median_vol, instead of unmodified loaded epoch values - Applied identically to both dqn_full_experience_kernel (standard) and dqn_full_experience_kernel_warp (warp-cooperative) variants Co-Authored-By: Claude Opus 4.6 <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;