d313486dc2
Extract 9 new sub-crates from the ml monolith to enable parallel compilation across the workspace: New crates (this commit): - ml-features (282 tests): feature engineering, 21 modules - ml-labeling (45 tests): triple barrier, meta-labeling, fractional diff - ml-ensemble (116 tests): ensemble coordination, voting, confidence - ml-hyperopt (47 tests): core PSO/TPE optimizer, parameter space - ml-checkpoint (41 tests): checkpoint persistence, compression, signing - ml-regime (68 tests): CUSUM, Bayesian changepoint, regime classification - ml-data-validation (67 tests): FDR correction, CPCV, data quality - ml-risk (33 tests): neural VaR, Kelly criterion, circuit breakers - ml-validation (43 tests): statistical validation, walk-forward, DSR Extended existing crates: - ml-dqn: added evaluation/ (backtesting engine, metrics, reports) and checkpoint implementation - ml-supervised: added checkpoint implementations - ml-core: added shared types needed by new sub-crates Pattern: each module in ml/ becomes a thin facade (pub use subcrate::*) with bridge modules staying in ml for cross-model adapter code. Dead code deleted (~7K lines): - 13 undeclared files in microstructure/ (never compiled) - 7 undeclared files + tests/ in risk/ (never compiled) - parquet_io, cache_service, cache_storage, minio_integration (unused) - extraction_wave_d_impl.rs (bare fn outside impl block) All 2,746 sub-crate tests + 951 ml tests pass. Full workspace builds clean. 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;