e04edc0a84
- common_device_functions.cuh: DQN_NUM_ACTIONS 5→9, TOTAL_ACTIONS 45→81 - experience_kernels.cu: exposure_idx_to_fraction() formula (-1+idx*0.25) - experience_kernels.cu: action_to_exposure() same formula - backtest_env_kernel.cu: switch→formula for 9-level mapping - All Q-gap flat_idx: hardcoded 2 → b0_size/2 (generalized) - c51_loss_kernel.cu: MAX_BRANCH_SIZE 5→9, BRANCH_0_SIZE 5→9 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;