1bf752d387
DQN: disable all exploration for production (warmup=0, epsilon=0, noisy_nets=false, count_bonus=false), read feature_count from checkpoint metadata instead of hardcoding 54, add loaded guard and NaN check on predict output. PPO: fix confidence formula — use act_with_log_prob() instead of act() which returns value_estimate not log_prob, add loaded guard and NaN check, remove WorkingPPO alias. Liquid: add loaded flag for is_ready()/predict() guards. Remove EgoboxOptimizer/EgoboxOptimizerBuilder backward-compat aliases — replaced with canonical ArgminOptimizer everywhere. 323 trading_service tests, 200 hyperopt tests, 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;