71aade18cf
Component 5 / Kernel 1 of the SP20 design — central state-tracker
that updates 8 Wiener-α EMAs per training step:
- 4 ISV slots: ALPHA_EMA (511), WR_EMA (512),
HOLD_PCT_EMA (515), HOLD_REWARD_EMA (516)
- 4 internal: trade_duration_ema, aux_conf_p50_ema,
aux_conf_std_ema, aux_dir_acc_ema (private
scratch consumed by Phase 1.3 controllers)
Per-EMA i32 observation counters (mapped-pinned obs_count[8])
handle the pearl_first_observation_bootstrap sentinel transition
correctly even when 0.0 is a legitimate observation (e.g.,
first-loss WR=0). count==0 ⇒ replace, count>0 ⇒ Wiener-blend at
α = 0.4 (WIENER_ALPHA_FLOOR per
pearl_wiener_alpha_floor_for_nonstationary).
Phase 1.2 lands kernel + launcher + 5 tests (4 GPU oracle, 1
floor lock) + build entry + audit doc atomically per
feedback_no_partial_refactor. Production wire-up (Phase 1.4)
deferred — kernel is dead code until then.
Verified on RTX 3050 Ti (sm_86):
- 4 GPU oracle tests pass: first_observation_replaces_sentinel,
wiener_alpha_converges_to_long_run_mean,
hold_reward_ema_gated_on_hold_bars,
per_step_emas_fire_unconditionally
- 4 launcher unit tests pass (constants + struct sanity)
- 1 floor-lock test pass (WIENER_ALPHA_FLOOR == 0.4)
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;