4c71835a84
5 HtoD sites in GpuBacktestEvaluator::new + reset_evaluation_state: - prices_buf (f32): stream.clone_htod → clone_to_device_f32_via_pinned - features_buf (f32): clone_htod_f32 → clone_to_device_f32_via_pinned - window_lens_buf (i32): stream.clone_htod → clone_to_device_i32_via_pinned - portfolio_buf init (f32): stream.clone_htod → clone_to_device_f32_via_pinned - portfolio_buf reset (f32, per-eval): stream.clone_htod → pinned Fields stay typed CudaSlice<T> because the env_step kernel mutates portfolio_buf each backtest step (must remain device-resident); the read-only fields keep CudaSlice so the existing `.arg(&self.field)` kernel-launch sites at lines 885+ continue to work without cascade. Audit row appended (Fix 12) in docs/dqn-gpu-hot-path-audit.md. 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;