5b4cdec4ff
Adds `pearl_6_kelly_kernel.cu` (single-block 6-thread kernel reading portfolio_state directly) to populate ISV[280..286) with Bayesian-prior Kelly fraction, conviction identity, trade variance, cumulative sample count (max-semantics), win-rate and loss-rate EMAs. EWMA α=0.01 is an Invariant 1 structural anchor for cross-fold inertia. Key design departure from A1-A5: ISV[280..286) are intentionally EXEMPT from the StateResetRegistry and from apply_pearls/Pearls A+D. portfolio_state has WindowReset lifecycle (resets at EVERY window boundary, not just fold), making cross-fold persistence essential. The max()-semantics for sample_count (s==3) ensure the cumulative count never decreases across any boundary. Wiener offsets [525..543) that naive formula would produce are intentionally unused; in-kernel EWMA replaces external wiener bootstrap. Verification: cargo check -p ml --offline clean (11 pre-existing warnings, no new). sp5_producer_unit_tests --no-run clean. Two GPU tests (12, 13) validate EWMA blend and cross-fold persistence. Co-Authored-By: Claude Sonnet 4.6 <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;