512535076f
sample_action(), act(), act_with_log_prob(), greedy_action() now return FactoredAction instead of TradingAction. Fixes the architectural disconnect where num_actions=45 output neurons were sampled through a 3-action bottleneck. TrajectoryStep.action and TrajectoryBatch.actions now use FactoredAction. Added FactoredAction::from_legacy() for backward compatibility in tests. Updated all PPO consumers: trainers/ppo.rs, hyperopt/adapters/ppo.rs, validation/ppo_adapter.rs, benchmark/ppo_benchmark.rs. 2487 tests pass, 0 clippy warnings. 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;