b4d4a8d046
Wires the q_drift_rate_ema kernel + ISV slot Q_DRIFT_RATE_INDEX=129 (landed infc8dbb0a8) into the production training path: - StateResetRegistry FoldReset entry isv_q_drift_rate -> 0.0 (companion to A.1's prev_epoch_q_mean reset; both ensure no cross-fold leakage of drift state). - reset_named_state dispatch arm in training_loop.rs. - Per-epoch launch_q_drift_rate_ema call after the Q-drift kill check, gated on the same `prev_epoch_q_mean.abs() > 1e-6` cold-start guard. Both q_mean_curr and q_mean_prev are in scope BEFORE the `prev_epoch_q_mean = q_mean` update, so the producer sees the correct delta. - tau_update_kernel.cu multiplies the cosine-scheduled, health-coupled tau_eff by `1 / (1 + clip(ISV[129], 0, 4))` so tau ranges [tau_base/5, tau_base] — monotone dampening under drift; healthy runs (drift ≈ 0) unaffected. - reset_for_fold comment in trainer/mod.rs notes the registry entry handles the new ISV slot. Predicted impact: Plan C fold 0 ep2 with q_mean(t)=0.82, q_mean(t-1)=-0.018, ISV[16]+ISV[21]≈0.1 -> drift_rate ≈ 8.4 -> clipped to 4 -> tau_eff = tau_base × 0.2 (5× slower target sync, dampens Q-target optimism through the inflation spike).a52d99613fold 0 with q_mean staying ~0.21 -> drift_rate ≈ 0 -> tau_eff = tau_base (unchanged). Per pearl_adaptive_moe_lambda.md — canonical "EMA-tracked diagnostic drives a controller" pattern. Per pearl_cold_path_no_exception_to_gpu_drives.md — cold-path scalar arithmetic stays on GPU. Per feedback_no_partial_refactor.md — consumer (tau_update + state_reset + producer launch) all migrate together in this commit. Layout fingerprint already shifted byfc8dbb0a8(slot + ISV_TOTAL_DIM bump); no additional fingerprint shift in this commit. 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;