2674fa7a1e
Eliminates all per-fold CPU waste in walk-forward training: - FxCacheData replaces OHLCVBar as data backbone (features[42] + targets[4] + OFI[8]) - Walk-forward generates index ranges from timestamps, no bar cloning - DQNTrainer created once, reused across folds via reset_for_fold - Data uploaded to GPU once via init_from_fxcache, sliced by index per fold - Trainer accepts &[[f64;42]] + &[[f64;4]] slices, zero Vec<f64> allocation - PPO uses train_from_slices, no per-fold feature re-extraction - Ensemble trainers pre-created before fold loop, no per-fold re-upload - Deleted: prepare_fold_data, FoldData, features_to_trainer_format, train_ppo_fold, double-buffer, prefetch thread (~500 lines removed) Smoketest: 160s -> 4.97s (32x speedup) 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;