5d6e79263c
tft_real_dbn_data: StreamTensor::from_vec, quantile loss returns f32 ppo_recurrent_integration: PPO::new() API, get_policy_state &[f32] test_dbn_sequence_256: to_host + manual indexing instead of .i() ops ppo_checkpoint_roundtrip: save/load_checkpoint(&PathBuf) API mamba2_accuracy_fix: pure f64 arithmetic, no GPU tensors needed ppo_lstm_training_loop: PPO::new() API ppo_step_counter_fix: new checkpoint API ppo_recurrent_performance: forward_host, LSTM batch_size arg 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;