129b7fd3d0
Augments the existing DIAG_BUG2 one-shot block with two narrower scans
that disambiguate where state[0] heavy-tail (mean=6e-3, std=570,
mean_abs=20 over 3200 samples on smoke-test-xb78r) actually originates.
H1 (data-source corruption): scan features_buf.host_ptr for bars where
|feat[bar*42 + 0]| > 100. host_ptr aliases the same physical memory
the kernel reads (mapped-pinned) — direct read of the data
state_gather sees. Reports up to 10 outlier bar indices + mean/std/
max_abs over up to 2M bars.
H2 (kernel/gather bug): scan gpu_batch.states/next_states (already
DtoH-downloaded) for sample indices where |state[i*sd + 0]| > 100.
Reports up to 10 outlier sample indices.
Verdict line logged: feat-outliers nonempty → H1; state-outliers
nonempty AND feat-outliers empty → H2.
Context: smoke-test-xb78r at HEAD cba9f25ed went through DBN fallback
(train_baseline_rl.rs:611, "Loaded N bars (ts to ts)" message) — no
fxcache file exists on training-data PVC. DBN-fallback applies z-score
normalization (line 633) which has small stddev for log-return columns;
a single bar with bar.open ≈ 0 produces ln(ratio) = -30 → normalized
= -30000. Few bars in 700K could carry this signature.
Triggers once via static AtomicBool. Pre-graph-capture, no perf impact.
Cleanup gate: remove DIAG_BUG2 block once Bug 2 root cause is fixed
(likely a tighter clamp inside safe_log_return for log-return columns).
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;