64ca8f97ce
The batch span processor needs a tokio runtime for gRPC transport and periodic flush. Async services already have one via #[tokio::main], but sync training binaries (hyperopt, train, evaluate) don't. Previous approach (making binaries async with #[tokio::main]) caused "Cannot start a runtime from within a runtime" panics because the ML crate's internal code creates its own tokio runtimes for block_on(). New approach: build_otel_tracer() detects runtime context via Handle::try_current(). If absent, it creates a dedicated 1-worker multi-thread runtime stored in a process-lifetime OnceLock. The worker thread actively polls the OTLP batch export task. Reverts training binaries to sync fn main() so internal runtime creation (hyperopt adapters, DQN/PPO trainers) continues working as before. Co-Authored-By: Claude Opus 4.6 <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;