237b3dbfb0
SP7 T1 added 24 ISV slots (LB_DIFF_VAR_CQL_BASE=297 ... LB_C51_ACTIVE through 321) without bumping ISV_TOTAL_DIM, leaving SP7's wiener-state and activation slots out-of-bounds of the allocated pinned buffer (294 * 4 = 1176 bytes; SP7 slots write to bytes 1188..1284). GPU direct-pointer writes silently corrupted memory in the next page; CPU read_isv_signal_at returned garbage in release builds. This explains the SP7 smoke's confusing zero-budget-everywhere observation: the activation flag was reading OOB memory, the consumer saw inconsistent values, and the controller's actual ISV state was never visible. Bumped ISV_TOTAL_DIM to 321 (max valid index 320, covering SP5_SLOT_END-1). Made pub(crate) so the new contract test can reference it from sp5_isv_slots.rs. Updated layout_fingerprint_seed()'s slot entries to include the previously missing D3 and SP7 slots (TRAINING_SHARPE_EMA=294 through LB_C51_ACTIVE_BASE=317) and updated ISV_TOTAL_DIM= literal to 321 in lockstep per feedback_no_partial_refactor. Added contract test all_sp5_slots_fit_within_isv_total_dim to permanently gate this class of bug at cargo test time. The test would have caught SP7 T1 instantly; future slot allocations cannot regress this. Files: gpu_dqn_trainer.rs (ISV_TOTAL_DIM + comment + fingerprint), sp5_isv_slots.rs (test), docs/dqn-wire-up-audit.md (Fix 31 sub-bullet). Cargo check workspace clean. Cargo test ml --lib 935 passed (934 baseline + 1 new contract test); 16 pre-existing GPU-hardware failures unchanged. Co-Authored-By: Claude Sonnet 4.6 <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;