e9481c6107
Two issues causing all 20 hyperopt trials to have identical f64::MAX objective (TPE optimizer blind): 1. Val-loss plateau early stopping fired at epoch 5-6 of every 8-epoch trial (plateau_window=5 too aggressive for short runs). Disabled early_stopping_enabled for hyperopt; gradient-collapse patience still active as safety net. 2. Penalty metrics used f64::MAX for gradient_norm/q_value_std which produced ~3.6e+308 objective. Changed to 100.0 so TPE can still differentiate between early-stopped trials by other metric fields. 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;