12bba98ecd
Current Flat opp-cost was conviction-driven but |Q|-scale was implicit. When training drifts Q magnitudes into ±50, opp-cost becomes invisible relative to action Q-values → Flat wins argmax. Fix: multiply by isv_signals_ptr[21] (Q_DIR_ABS_REF_INDEX, EMA of max(|Q_mean|) across direction bins, populated by update_eval_v_range / q_stats_kernel). Self-scaling: opp-cost tracks |Q| proportionally throughout training. No tuned multiplier; relies on existing ISV slot. Floor 1e-3 for cold- start protection before EMA is warm. rc[4] is now written in the Flat branch so reward_component_ema kernel picks it up into ISV[67]. No parallel paths — the old formula IS modified in place. Plan 3 Task 2. Spec §4.B.1. 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;