# ml

Machine learning models for Foxhunt.

## Models

- **DQN (Rainbow)** -- Deep Q-Network with prioritized experience replay, dueling heads, noisy nets, double Q-learning
- **PPO** -- Proximal Policy Optimization with GAE, LSTM policies, clip-higher option
- **TFT** -- Temporal Fusion Transformer for multi-horizon time series forecasting
- **Mamba2** -- State space model for efficient sequence prediction
- **Liquid Networks** -- Biologically inspired neural networks for non-stationary data
- **TLOB** -- Transformer-based Limit Order Book analysis
- **Flash Attention** -- Optimized attention implementation

## Training

Two paths per model:

1. **Standalone trainer** -- direct training loop (e.g., `DQN::train`, `PpoTrainer`)
2. **UnifiedTrainable adapter** -- wraps models for the hyperopt pipeline (e.g., `DQNTrainableAdapter`, `UnifiedTrainablePPO`)

## Inference

`InferenceAdapterBridge` connects models to the ensemble coordinator in `adaptive-strategy`. Each model exposes an `InferenceAdapter` trait for prediction.

## Backend

- **Candle v0.9.1** -- `VarMap`, `AdamW`, `loss.backward()`, `GradStore`, `opt.step(&grads)`
- **CUDA required for training** -- tested on RTX 3050 Ti 4GB, max batch size 230
- **CPU inference** supported

## Hyperopt

`ArgminOptimizer` (Particle Swarm Optimization) with per-model adapters:
DQN, PPO, ContinuousPPO, TFT, Mamba2. Uses `ParameterSpace` trait for continuous parameter mapping.

## ModelType Enum

15 variants: `CompactDQN`, `DistilledMicroNet`, `DQN`, `RainbowDQN`, `MAMBA`, `TFT`, `TGGN`, `LNN`, `TLOB`, `PPO`, `Transformer`, `Mamba`, `LiquidNet`, `TGNN`, `Ensemble`.

## Key Modules

`dqn`, `ppo`, `tft`, `mamba`, `liquid`, `tlob`, `flash_attention`, `ensemble`, `evaluation`, `inference`, `trainers`, `hyperopt`, `checkpoint`, `preprocessing`, `data_loaders`, `features`, `model_factory`, `training_pipeline`, `regime_detection`, `stress_testing`, `validation`, `bridge`, `common`, `metrics`.

## Testing

```bash
SQLX_OFFLINE=true cargo test -p ml --lib  # ~2009 tests
```