0426ce8887
Per train-multi-seed-pfh9n post-mortem follow-up: slot 460 stuck at 50 in Fold 1 was NOT a launch-lifecycle bug. Producer fires per-epoch but kernel had early-return guard on AUX_DIR_ACC_SHORT_EMA (slot 373) at sentinel 0.5. Slot 373 reset on fold boundary; aux dir-acc EMA either didn't fire or settled within ε of 0.5 → kernel kept early-returning. Fix: drop slot 373 dependency entirely. Drive temperature from observed hold-rate vs target overrun: overrun = max(0, observed_hold_rate - target_hold_rate) overrun_norm = clamp(overrun / max(target, 0.01), 0, 1) new_temp = TEMP_MIN + (TEMP_MAX - TEMP_MIN) × overrun_norm blended_temp = Welford EMA α=0.05 with Pearl-A bootstrap When over-holding: temp HIGH → exit ramp permissive (matches design intent). When at/under target: temp LOW → exit penalty strict. Survives fold reset: hold-rate measurement starts fresh with real data immediately, no chained-input-sentinel masking. Slot 330 (KELLY_WARMUP_FLOOR) investigated and confirmed NON-BUG: producer behaves correctly per pearl_kelly_cap_signal_driven_floors cross-fold- persistence. floor=0 post-warmup is correct steady state. Behavioral tests: - sp16_phase1_min_hold_temp_climbs_with_hold_overrun - sp16_phase1_min_hold_temp_strict_when_at_target - sp16_phase1_min_hold_temp_strict_when_under_target - sp16_phase1_min_hold_temp_no_longer_reads_slot_373 Instrumentation: HEALTH_DIAG[N]: min_hold_temp_diag obs/target/overrun/temp. 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;