77fe520e08
Add live training metrics monitor CLI command (streaming & one-shot) using the monitoring gRPC service. Update DQN tests to match post-fix defaults: IQN disabled, CQL alpha=0.1, v_min/v_max widened, 26D search space. - train.rs: `fxt train monitor [--once] [--model X] [--interval N]` - Rewrite gradient collapse test for BF16 mixed precision awareness - Update inference test config to match trainer defaults (IQN off, CQL on) - Update production smoke test for 26D parameter space - Add dqn_action_collapse_fix_test.rs verifying all 6 root cause fixes - Add planning docs for monitoring service and epoch financial metrics 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;