d7061ca2ac
The hyperopt preload_data() created a loader with default hyperparams (mbp10_data_dir=None), so MBP-10/trades data was never loaded. Each trial then set mbp10_data_dir → state_dim=51, but the preloaded data had no OFI features → shape mismatch [128,43] vs [51,1024]. - Pass mbp10_data_dir/trades_data_dir to preload hyperparams - Extract ofi_features from loader after preload - Store as preloaded_ofi_features: Option<Arc<Vec<[f64;8]>>> - Inject into each trial's DQNTrainer before training 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;