e89fbc2b4d
Three root causes for "no metrics" on the training dashboard: 1. Dashboard template variables ($model, $fold) sourced from foxhunt_training_current_epoch which isn't emitted until the first epoch completes. Switch to foxhunt_training_step which fires from step 500 onward. 2. train.sh pod template missing Prometheus annotations (prometheus.io/scrape, port, path). Also add the app.kubernetes.io/component label to the eval manifest so evaluation pods are discoverable too. 3. DQN and PPO trainers only called set_epoch() at the END of each epoch. Move the call to the TOP of the epoch loop so the gauge exists from the first training iteration. 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;