45da3eac69
3 IMPORTANT items from #260 code-quality review (commit88ae74ca7), addressed post-smoke-validation (smoke-test-tkkx6 Succeeded): 1. Stale numeric line-references in 8+ doc-comments replaced with symbolic code anchors per feedback_trust_code_not_docs. Pre-existing stale `ISV_TOTAL_DIM = 60` comment also corrected. 2. q_dir_grad launcher allocated dedicated `q_dir_grad_subbuf_table_buf` + `q_dir_grad_subbuf_counts_buf` (2 entries each) instead of reusing the shared `oracle_subbuf_table_buf`. Eliminates implicit "must-run-before- oracle" temporal coupling; removes `K_MAX=4` local redefinition. 3. `launch_sp4_p99_producer_single_buf` helper extracted on GpuDqnTrainer. Collapses ~30-line boilerplate x 3 call sites (target_q, h_s2, bw_d_h_s2) into single-line calls. Multi-sub-buffer launchers (q_dir_grad, param_group_oracle) and shape-distinct producers (grad_norm 1-thread, atom_pos 4-iter+batched-Pearls) keep their bespoke shape. Build clean, 11 SP4 lib tests pass, 16 SP4 GPU tests pass on RTX 3050 Ti. Refs: #260 code-quality review, smoke-test-tkkx6 (commit88ae74ca7). Co-Authored-By: Claude Opus 4.7 (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;