3e9cefdbd9
`train_walk_forward` called `reset_for_fold().await?` directly at the fold boundary, which applies `shrink_and_perturb(α=0.8, σ=0.01)` to whatever `params_flat` holds. At end-of-fold, `params_flat` carries the LATEST-EPOCH decayed weights, NOT the best-Sharpe snapshot saved mid-fold. Fold N+1 then started from `0.8 × decayed + noise` and carried the within-fold edge-decay forward. Insert `restore_best_gpu_params()` BEFORE `reset_for_fold` so S&P operates on the best-Sharpe snapshot. Cold-start guard swallows the "no snapshot saved" error on the first fold (or any fold without a Sharpe improvement) — matches pre-fix behavior on cold start, no regression. State interaction with `reset_for_fold`: restore-best writes `params_flat` ONLY (online weights); reset_for_fold then runs S&P on the restored online, hard-syncs target ← restored online, and resets Adam state on every optimizer. Non-conflicting. `best_params_snapshot` is constructor-init `None` on FusedTrainingCtx and is NEVER cleared at fold boundary. The trainer's `self.best_sharpe` IS reset to NEG_INFINITY in `reset_for_fold` so the first improvement in fold N+1 will overwrite the snapshot. Until then, the snapshot holds whichever fold most recently saved a peak — intended training-scoped behavior. Strict within-fold semantics flagged as a follow-up consideration. Behavioral test (CPU oracle) pins the math contract: - shrink_and_perturb(α, σ) on X = α × X + (1−α) × N(0, σ) - with-fix vs without-fix differ by α × (W_best − W_curr) - cold-start (best == current) is bit-identical between orderings GPU-level kernel coverage stays in compile_training_kernels smoke and the L40S 30-epoch validation gating SP18 Phase 1. Pre-commit Invariant 7: docs/dqn-wire-up-audit.md updated with full rationale, state-interaction analysis, lifecycle notes, and the preserved cross-pearl invariants. Per: - feedback_no_partial_refactor (call-ordering + test + audit-doc atomic) - feedback_no_legacy_aliases (reuses existing API unchanged) - feedback_wire_everything_up (restore_best_gpu_params gains second cold-path consumer) - pearl_no_host_branches_in_captured_graph (runs outside graph capture)
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;