1a80fcab10
Plan C Phase 2 T7. #[ignore]-gated GPU test that runs BOTH the
production experience_action_select kernel AND the standalone
direction_thompson_v2_test (added in commit 5de5e546a) on identical
Phase 0 Test 0.D failure-mode inputs and asserts the resulting
direction histograms are statistically indistinguishable.
Path (a) — bit-identical comparison via a shared pre-computed uniform
array — would require editing the standalone v2 kernel's signature to
accept a `float* uniforms` instead of generating its own LCG draws.
Out of scope for the 1-2-hour dispatch. Path (b) — KS-style histogram
comparison — is used.
Setup:
- Single per-direction logits tile (Phase 0 Test 0.D failure mode)
- Production: tile replicated across batch=100 000 samples; Philox
keyed on (i, timestep=0, ctr) per thread
- Standalone v2: same tile fed to single-tile launcher with n_seeds=100 000;
LCG keyed on (base_seed=0 + seed_idx) per thread
Assertion:
- KS distance over the {Short, Hold, Long, Flat} histograms ≤ 0.02
(n=100k empirical-CDF noise floor for matching distributions is
~4·sqrt(2/n) ≈ 0.018; algorithm divergences would shift mass by
O(10%) → KS ≈ O(0.1), easily detected)
- Sanity: both histograms have P(Long+Short) ≥ 0.20 (rules out
coincidental shared-mode collapse with KS ≈ 0)
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;