c146c4fffd
Per spec §4.3 allocation map. Pre-allocates disjoint slot ranges to enable Approach B parallel sub-worktrees without index collisions: - Phase 0.B EGF retune: [397..401) - Phase 1.3 drawdown: [401..407) - Phase 1.2 cost: [407..409) - Phase 1.4 baselines: [409..417) - Phase 3.X-3.5.X teachings + recovery: [417..441) - Phase 3.5 deferred anchors: [441..443) Layout fingerprint extended with all 46 slot names. Pre-SP15 checkpoints will be incompatible (greenfield OK per Q1). Two regression tests verify: (1) every slot < ISV_TOTAL_DIM, (2) layout fingerprint locked at named indices. docs/isv-slots.md gets the SP15 section documenting the allocation map + greenfield sub-worktree plan. 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;