e1d548bdf3
ROOT CAUSE: run_backtracking_epoch_end received epoch_sharpe (training Sharpe from experience collection, ~0.65-0.79 oscillating) instead of val_Sharpe (deterministic backtest, 33.49 frozen for 56 epochs). Training Sharpe oscillates even when the model is frozen → sharpe_frozen was always false → AND condition never met → backtracking never triggered despite 56 consecutive frozen epochs in train-2tgs7. Fixes: 1. Pass val_sharpe (-val_loss) to run_backtracking_epoch_end 2. prev_val_sharpe field on BacktrackingState (not sharpe_history) 3. improvement_rate uses val_Sharpe delta (not training Sharpe) 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;