a27cb40a9a
The 9-quarter precompute_features run OOM-killed at ~56Gi on the ci-compile-cpu pool (POP2-HC-32C-64G) on 2026-05-16. Root cause: lines 672-684's `t.iter().map(...).collect()` borrows the source DbnTrade Vec while building the Mbp10Trade Vec — both alive simultaneously, transient peak ~25GB just from this transformation for the 199M-trade dataset. `.into_iter()` consumes the source element-by-element so the allocation drops as the destination grows, capping peak at the larger of the two Vecs (~15GB) rather than their sum. Should let the 9-quarter precompute fit comfortably on the existing 64GB ci-compile-cpu pool without provisioning a high-memory node. Co-Authored-By: Claude Opus 4.7 <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;