f5cb953082
grad_norm was growing 5x per epoch (523→2673→13K→67K→NaN). The old clip at 10.0 allowed gradients to accumulate. With clip=1.0, the Adam optimizer receives bounded updates. Trial 2 (TPE-guided params) trains cleanly for 4 epochs: train_loss: 4.5→3.3 (decreasing!) Q-value: 12-19 (stable) grad_norm: 162-567 (bounded) Trial 1 (random initial params) still NaN's — this is expected and handled by the hyperopt penalty (1M objective). The optimizer learns to avoid unstable parameter regions. 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;