6e7690d82d
Minor item 1: Remove extra column-alignment spaces from the 4 SP7 constants (LB_DIFF_VAR_CQL_BASE, LB_SAMPLE_VAR_CQL_BASE, LB_DIFF_VAR_C51_BASE, LB_SAMPLE_VAR_C51_BASE) so they match the no-alignment style of surrounding SP5 constants (BUDGET_C51_BASE etc.). Constant values (297, 301, 305, 309) and inline comments are unchanged. Minor item 2: Soften the Fix 31 T7 stub in dqn-wire-up-audit.md from "sentinel-aware consumer" to "sentinel-aware bootstrap with bootstrap constants matching the kernel's cold-start basis (defined in T7)" so the audit entry does not forward-reference specific constant names before they land. Cargo check: clean (zero warnings, zero errors). 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;