d289b2f264
- DQNAgent::load_from_safetensors: validate architecture hash before loading weights (was bypassing validation entirely - P0 production gap) - DQNEnsemble::save_to_directory: embed architecture metadata via safetensors::serialize_to_file instead of bare VarMap::save (save/load round-trip was guaranteed to fail) - architecture_hash: hash hidden_dims.len() before values to prevent theoretical collision between different-length configs - train_baseline_rl: NormStats write now atomic (write .tmp then rename) with cleanup guard on rename failure; serialization error is now loud (error! + return None) instead of silently discarded - train_baseline_rl: checkpoint rename failure cleans up orphaned .tmp - hyperopt_baseline_rl: fix clippy single_match_else (match on equality check -> if/else) 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;