d4b484e30b
New backtest_env_step_batch kernel processes all chunk_len steps in a single launch. Each thread handles one window, loops over steps sequentially reading from the chunked_actions buffer. Portfolio state stays in registers across the step loop — zero global memory round-trips between steps. Eliminates 512 individual kernel launches per chunk (was: DtoD copy + env_step per step = 1024 launches per chunk). With 301 chunks for 154K bars: ~154K launches → 301 launches. Kernel launch overhead drops from ~1.15s to ~2.3ms. Combined with chunk 64→512 + sync reduction: validation expected to drop from 2.5s to <0.5s. Co-Authored-By: Claude Opus 4.6 (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;