110d3b4125
Cleanup of compiler warnings flagged by both local cargo check and the cluster ensure-binary log. Per `feedback_no_hiding`, every site is either deleted or wired up — no #[allow] suppressions. Lib (5 sites): - gpu_backtest_evaluator.rs:34 — drop unused DevicePtrMut. - gpu_dqn_trainer.rs:49 — drop unused DevicePtrMut (8 device_ptr_mut calls don't need the trait import in current cudarc). Line 19852: drop unnecessary parens around `b * sh2`. - training_loop.rs:20 — drop unused DevicePtrMut; unbrace single- symbol use at 5766. - state_reset_registry.rs:4 — delete the 10-symbol use-block of slot constants. Names appear in description strings (documentation only), symbols are never referenced. Examples (3 sites): - alpha_dqn_h600_smoke.rs:181, 186 — drop COL_RAW_CLOSE, FEAT_DIM, FillCoeffs, FillModel imports. - alpha_baseline.rs:79 — delete unused MappedI32::read. Batched path uses read_all for N-element action readback; the single-element method was leftover from the pre-batched legacy path. Lib + examples now have zero removable warnings. The remaining unsafe_block lints (each cudarc kernel launch needs unsafe) are structural and not actionable under the project's -W unsafe-code policy. Co-Authored-By: Claude Opus 4.7 <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;