ad5423e016
Expand the DQN hyperopt parameter space from 31D to 38D by adding 7 GPU composite reward weights (w_dsr, w_pnl, w_dd, w_idle, dd_threshold, loss_aversion, time_decay_rate) at indices 31-37. - Remove hold_penalty_weight from DQNParams (replaced by w_idle) - Add #[serde(default)] for backward compat with old JSON results - Phase Fast fixes reward weights to defaults (not searched) - Wire reward weights from DQNParams → DQNHyperparameters in train_with_params - Fix all tests: 134 hyperopt + 7 ensemble + 5 JSON export tests pass - Fix stale 40D ensemble tests → 38D layout (were already broken pre-change) 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;