a22e6420b6
Capture the full DQN backtest step loop (gather → forward → DtoD → env_step × max_len) as a replayable CUDA Graph: - evaluate_dqn_graphed(): captures on first call via CudaStream::begin_capture/end_capture, replays cached graph on subsequent calls. Falls back to evaluate_dqn() on any failure. - invalidate_dqn_graph(): discards cached graph when weights change. - SendSyncGraph: newtype wrapper for CudaGraph (single-threaded use). - Full unrolled capture: each step's step_i32 argument is baked in at capture time, avoiding GPU-resident step counter complexity. Eliminates ~5-10μs per kernel launch × 4 kernels × max_len steps of CUDA driver overhead per evaluation. evaluate_baseline.rs: added --cuda-graphs CLI flag to opt in. 14 backtest evaluator tests pass, 0 clippy errors. 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;