b4e5fc9891
Combined spec-quality review caught two minor issues in the Pearl 8
commit. Both are mechanical fixes; no behavior change.
1. training_loop.rs:3640 used `let _ = std::mem::ManuallyDrop::new(guard);`
to keep the cudarc StreamGuard alive past the inner let-binding
block — the pattern leaks the guard's borrow bookkeeping for the
lifetime of the function rather than dropping it after the kernel
launch. Replaced with the idiomatic
let (feat_dev_ptr, _guard) = features_buf.device_ptr(stream_ref);
at the same scope as the launch, matching the existing convention
for SP4 producer wire-ups elsewhere in this file. The
_guard binding lets cudarc's borrow drop naturally at end-of-block
after the kernel launch returns.
2. The audit doc's description of test 14 incorrectly claimed
atr_norm was chosen so atr_abs=10.0 and Short/Long=20.0. The
actual test uses atr_norm=0.5 → log_atr=1.0 → atr_abs=e^1≈2.7183
→ Short/Long≈5.4366. Updated the audit doc to match the actual
test values.
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;