af940671bc
Add 7 composite reward fields to DQNHyperparameters: w_dsr, w_pnl, w_dd, w_idle, dd_threshold, loss_aversion, time_decay_rate. Add RewardSection to training_profile.rs with Option<f64> fields and apply_to() mapping. Add [reward] section to all 3 DQN TOML profiles (production, smoketest, hyperopt) with identical defaults. Remove hold_reward from ExperienceSection (replaced by w_idle). Add 7 reward search bounds to SearchSpaceSection and bound() match. Add 7 reward phase_fast defaults to PhaseFastSection. hold_penalty kept as deprecated field for hyperopt adapter compat (Task 4 will clean it up). 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;