9b02f86fca
Fix last 2 tests: - action_masking: test used 81-action FactoredAction decoder for 45-action mask. Fixed to use mask's own 5×9 exposure encoding. - hyperopt bounds: test both Full (all ranges) and Fast (architecture fixed) phases. Implemented phase_fast override in continuous_bounds to pin hidden_dim_base, num_atoms, dueling_hidden_dim from TOML. Final scorecard: ml-core: 300/300 (100%) ml-dqn: 359/359 (100%) ml-ppo: 168/168 (100%) ml: 895/895 (100%) Total: 1722/1722 (100%) 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;