eda1eccb1a
Brings in Phase 2A.1 (LobBar canonical ABI + 4 synthetic market generators), Phase 2A.2 (oracle + harness + pre-commit hook), and Phase 2B (17 #[ignore] behavioral test contracts) so the phase1 honest-numbers branch has access to the LobBar (price, half_spread, ofi) ABI needed for Phase 1.2.b cost-net sharpe consumer wiring. Path 2 of the BLOCKED 1.2.b investigation: the cost-net kernel needs GPU-resident streams (half_spread, ofi, rt_ind, side_ind, position) that do not exist on phase1; Phase 2A.1's LobBar provides the canonical ABI. Conflict resolution: docs/dqn-wire-up-audit.md — both branches prepended entries; merged by keeping all three (Phase 2A.1 from phase2a, Phase 1.6, Phase 1.7 from phase1). Phase 2A.1 entry placed above Phase 1.6 / 1.7 to keep this region's audit ordering consistent (newer-first locally). 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;