e9e2e87a64
- Hyperopt adapter now sets max_training_steps_per_epoch: RTX 3050 (≤40GB) = 200 steps, H100 (≥40GB) = 2000 steps. Without this, each trial trained the full dataset (2917 steps/epoch) making hyperopt 11x slower than necessary on local GPU. - adam_epsilon default 1e-3→1e-8 everywhere (conservative(), DQNConfig). The old 1e-3 was a BF16 workaround (bf16(1e-8)=0 → div-by-zero). Adam is now f32, so standard 1e-8 is correct. - Early stopping enabled in dqn-localdev.toml (patience=20). Hyperopt: 2 trials × 5 epochs in 132s (was ~20min). Zero NaN. 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;