d04b6c7023
- trade_ml.rs: Replace 3 mock data fallbacks (submit, predictions, performance) with proper error propagation. Commands now fail honestly when the API Gateway is unreachable instead of silently returning fake data. Mark 3 integration tests as #[ignore]. - monitoring_service: Add tonic-health with set_serving for MonitoringServiceServer. Enables grpc_health_probe readiness checks. - ml_training_service: Add tonic-health with set_serving for MlTrainingServiceServer. Wired into both TLS and non-TLS paths. - data_acquisition_service: Add tonic-health with set_serving for DataAcquisitionServiceServer. - ml/cuda_streams: Fix pre-existing unused variable clippy warning. All 8 services now have standard gRPC health checking enabled. 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;