9adbca8262
Hypothesis test for SP22 H1 (label horizon mismatch). Finding from v9/v10 HEALTH_DIAG: aux_dir_acc=28-47% (BELOW RANDOM) across all observed cycles. Root cause: adaptive aux_horizon_update collapses H back to ~1.7 bars (observed avg winning hold time), making the aux label HFT microstructure noise. Experiment: 1. Bump SENTINEL_AUX_PRED_HORIZON_BARS 60.0 → 200.0 2. Disable launch_aux_horizon_chain call so H stays at sentinel Predicted: if aux_dir_acc rises >50% → H1 confirmed; if stays ≤50% → escalate to H2/H4 per SP22 plan. Cost: 1 smoke ~30min, kill early on cycle 1-2 trend. Files changed: - crates/ml/src/cuda_pipeline/sp14_isv_slots.rs - crates/ml/src/trainers/dqn/trainer/training_loop.rs - docs/dqn-wire-up-audit.md (H1 experiment entry) Reverts if H1 falsified. 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;