18261c85a3
Tuning pass on the adaptive mechanisms. Changes: 1. F5 barrier weight raised 0.05 → 0.20 base, amplified 1×..2× by meta-Q collapse prediction (proactive, not reactive). Old 0.05 couldn't escape the Q-uniform attractor locally. 2. last_meta_q_pred field added on GpuDqnTrainer with set/get accessors, wired from DQNTrainer's meta_q.predict() each epoch boundary. Aux-op kernels now have per-step access to temporal collapse prediction. 3. DISTILL_HEALTH_THRESHOLD raised 0.4 → 0.55 (fire earlier). Additional temporal trigger: distill also fires when meta_q_pred > 0.5. 4. SNAPSHOT_HEALTH_THRESHOLD lowered 0.7 → 0.65. 5. Snapshot-on-winrate fallback: when last_epoch_win_rate >= 0.45, inflate effective health to 0.75 so a snapshot IS taken even if the health EMA is stuck in the 0.48 trough. Without this, the good moments (WinRate 56%, 49%) are never captured → distillation has nothing to pull toward. Result on local E1: distill=on every epoch (was permanently off), D6 fires only on bad-outcome epochs (was firing on convergence). Q-gap still collapsed — tuning alone won't fix the underlying attractor; root cause investigation next. 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;