a26478eca3
- cuda_pipeline/mod.rs: all Tensor→CudaSlice, DqnGpuData/PpoGpuData GPU-native, d2d_subrange helper, build_state_tensor host-interleave + single HtoD - portfolio_transformer.rs: complete rewrite with GpuLinear + cuBLAS - trainers/tlob.rs: GpuVarStore + GpuLinear, host-side MSE/MAE - trainers/tft: StreamTensor trait, MlDevice constructors - trainers/liquid.rs, mamba2.rs: GpuTensor pairs, f64 loss accumulation - training/orchestrator.rs: forward_loss(&[f32]) trait interface - hyperopt/adapters/ppo.rs: MlDevice, PPO::new() constructor - ml-ppo/lib.rs: fixed cuda_compat→cuda_compile re-export 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;