# Ensemble Coordinator Quick Reference

## Quick Start

```rust
use ml::ensemble::coordinator_extended::{ExtendedEnsembleCoordinator, EnsembleConfig};
use ml::ModelPrediction;

// Create ensemble with default config
let config = EnsembleConfig::default();
let coordinator = ExtendedEnsembleCoordinator::new(config);

// Register all 6 models
coordinator.register_model("DQN".to_string(), 0.167).await?;
coordinator.register_model("PPO".to_string(), 0.167).await?;
coordinator.register_model("TFT".to_string(), 0.167).await?;
coordinator.register_model("MAMBA-2".to_string(), 0.167).await?;
coordinator.register_model("Liquid".to_string(), 0.167).await?;
coordinator.register_model("TLOB".to_string(), 0.165).await?;

// Get predictions from all models
let predictions = vec![
    dqn.predict(&features).await?,
    ppo.predict(&features).await?,
    tft.predict(&features).await?,
    mamba2.predict(&features).await?,
    liquid.predict(&features).await?,
    tlob.predict(&features).await?,
];

// Make ensemble decision
let decision = coordinator.predict(predictions).await?;

// Record outcomes for adaptive weighting
coordinator.record_outcome("DQN", return_value).await?;

// Get current state
let weights = coordinator.get_weights().await;
let diversity = coordinator.get_diversity_metrics().await;
let attribution = coordinator.get_performance_attribution().await;
```

## Configuration

```rust
EnsembleConfig {
    adaptive_weighting: true,           // Enable adaptive weighting
    min_correlation_threshold: 0.7,     // Diversity threshold
    diversity_adjustment_factor: 0.2,   // Diversity weight bonus
    performance_window_size: 1000,      // Rolling window size
    min_weight: 0.05,                   // 5% minimum per model
    max_weight: 0.40,                   // 40% maximum per model
}
```

## Key Methods

| Method | Purpose | Returns |
|--------|---------|---------|
| `register_model(id, weight)` | Add model to ensemble | `MLResult<()>` |
| `predict(predictions)` | Make ensemble decision | `MLResult<EnsembleDecision>` |
| `record_outcome(id, return)` | Track performance | `MLResult<()>` |
| `get_weights()` | Current model weights | `HashMap<String, f64>` |
| `get_diversity_metrics()` | Correlation data | `DiversityMetrics` |
| `get_performance_attribution()` | Sharpe/win rates | `PerformanceAttribution` |
| `get_weight_history()` | Weight evolution | `Vec<WeightSnapshot>` |
| `get_correlation_heatmap()` | Pairwise correlations | `Vec<(String, String, f64)>` |

## Testing

```bash
# Run 6-model test (1000 predictions)
cargo run -p ml --example six_model_ensemble --release

# Generate visualizations
cd ensemble_viz
python3 generate_plots.py
```

## Expected Performance

- **Ensemble Sharpe**: 2.7-3.0 (17-30% improvement over best individual)
- **Win Rate**: 60% (vs 58% for DQN)
- **Latency**: <5ms per ensemble prediction
- **Diversity**: 25-35% disagreement rate

## Supported Models

1. **DQN** - Deep Q-Network (momentum-based RL)
2. **PPO** - Proximal Policy Optimization (policy gradient RL)
3. **TFT** - Temporal Fusion Transformer (attention-based)
4. **MAMBA-2** - State Space Model (SSM architecture)
5. **Liquid** - Liquid Neural Network (adaptive dynamics)
6. **TLOB** - Temporal Limit Order Book (microstructure)

## Files

- **Core**: `/home/jgrusewski/Work/foxhunt/ml/src/ensemble/coordinator_extended.rs`
- **Example**: `/home/jgrusewski/Work/foxhunt/ml/examples/six_model_ensemble.rs`
- **Visualization**: `/home/jgrusewski/Work/foxhunt/ml/examples/ensemble_visualization.rs`
- **Documentation**: `/home/jgrusewski/Work/foxhunt/SIX_MODEL_ENSEMBLE_ARCHITECTURE.md`