0d62bdba2e
Zero candle_core/candle_nn imports in ml/src/. Three-agent parallel migration: - cuda_pipeline/ (19 files): Tensor→CudaSlice, Device→Arc<CudaStream>, VarMap→GpuVarStore, cudarc import path fixed - trainers/ + adapters (45 files): DQN/PPO/TFT trainers, 10 ensemble adapters, 11 hyperopt adapters — all migrated to MlDevice, GpuTensor, GpuVarStore, GpuAdamW - model dirs + infra (40 files): 10 trainable adapters, preprocessing, inference, transformers, validation, benchmarks 61 test/example files still reference candle — next commit. candle-nn still in Cargo.toml (needed by tests until migrated). 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;