# Live Training Implementation Status

## ✅ Phase 1: Backend Integration (COMPLETED)

### 1. Orchestrator Integration

**File**: `core/orchestrator.py`

#### Initialization
```python
# Initialize EnhancedRealtimeTrainingSystem
if ENHANCED_TRAINING_AVAILABLE:
    self.enhanced_training_system = EnhancedRealtimeTrainingSystem(
        orchestrator=self,
        data_provider=self.data_provider,
        dashboard=None
    )
    logger.info("EnhancedRealtimeTrainingSystem initialized successfully")
```

#### Methods Added
1. **`start_live_training()`** - Start live inference and training
2. **`stop_live_training()`** - Stop live training
3. **`is_live_training_active()`** - Check if active
4. **`get_live_training_stats()`** - Get performance statistics

---

### 2. API Endpoints

**File**: `ANNOTATE/web/app.py`

#### Endpoints Added

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/live-training/start` | POST | Start live training mode |
| `/api/live-training/stop` | POST | Stop live training mode |
| `/api/live-training/status` | GET | Get status and statistics |

#### Example Usage

**Start Live Training:**
```bash
curl -X POST http://localhost:8051/api/live-training/start
```

Response:
```json
{
  "success": true,
  "status": "started",
  "message": "Live training mode started"
}
```

**Get Status:**
```bash
curl http://localhost:8051/api/live-training/status
```

Response:
```json
{
  "success": true,
  "active": true,
  "stats": {
    "predictions_per_sec": 1.0,
    "training_batches_per_min": 6,
    "accuracy": 0.75,
    "models": {
      "dqn": {"loss": 0.234, "accuracy": 0.78},
      "cnn": {"loss": 0.156, "accuracy": 0.82}
    }
  }
}
```

---

## ⏳ Phase 2: Core Functionality (NEEDS IMPLEMENTATION)

### What's Missing

The `EnhancedRealtimeTrainingSystem` exists but needs these methods implemented:

#### 1. Live Inference Loop
```python
def _live_inference_loop(self):
    """Main loop - predict next candle every second"""
    # TODO: Implement
    pass
```

#### 2. Prediction Method
```python
def _make_next_candle_prediction(self, timeframe: str) -> Dict:
    """Predict next candle OHLCV values"""
    # TODO: Implement using transformer's next_candles output
    pass
```

#### 3. Training Method
```python
def _train_on_timeframe(self, timeframe: str):
    """Train on resolved predictions"""
    # TODO: Implement batch training
    pass
```

#### 4. Prediction Resolution
```python
def _resolve_timeframe_predictions(self, timeframe: str):
    """Compare predicted vs actual candles"""
    # TODO: Implement
    pass
```

---

## ⏳ Phase 3: UI Integration (NEEDS IMPLEMENTATION)

### Dashboard Panel

Create `ANNOTATE/web/templates/components/live_training_panel.html`:

```html
<div class="card mb-4">
    <div class="card-header">
        <h5>Live Inference & Training</h5>
    </div>
    <div class="card-body">
        <div class="row mb-3">
            <div class="col-md-6">
                <div class="status-indicator">
                    <span id="live-status-badge" class="badge bg-secondary">Inactive</span>
                    <span id="live-status-text">Not Running</span>
                </div>
            </div>
            <div class="col-md-6 text-end">
                <button id="start-live-btn" class="btn btn-success">
                    <i class="fas fa-play"></i> Start Live Mode
                </button>
                <button id="stop-live-btn" class="btn btn-danger" disabled>
                    <i class="fas fa-stop"></i> Stop
                </button>
            </div>
        </div>
        
        <div class="row" id="live-metrics" style="display: none;">
            <div class="col-md-3">
                <div class="metric-card">
                    <div class="metric-label">Predictions/sec</div>
                    <div class="metric-value" id="predictions-per-sec">0</div>
                </div>
            </div>
            <div class="col-md-3">
                <div class="metric-card">
                    <div class="metric-label">Training/min</div>
                    <div class="metric-value" id="training-per-min">0</div>
                </div>
            </div>
            <div class="col-md-3">
                <div class="metric-card">
                    <div class="metric-label">Accuracy (1m)</div>
                    <div class="metric-value" id="accuracy-1m">0%</div>
                </div>
            </div>
            <div class="col-md-3">
                <div class="metric-card">
                    <div class="metric-label">GPU Load</div>
                    <div class="metric-value" id="gpu-load">0%</div>
                </div>
            </div>
        </div>
    </div>
</div>
```

### JavaScript Integration

Add to `ANNOTATE/web/static/js/main.js`:

```javascript
// Live Training Controls
let liveTrainingInterval = null;

function startLiveTraining() {
    fetch('/api/live-training/start', {
        method: 'POST',
        headers: {'Content-Type': 'application/json'}
    })
    .then(response => response.json())
    .then(data => {
        if (data.success) {
            updateLiveStatus(true);
            startLiveStatusPolling();
            showNotification('Live training started', 'success');
        } else {
            showNotification('Failed to start: ' + data.error, 'error');
        }
    });
}

function stopLiveTraining() {
    fetch('/api/live-training/stop', {
        method: 'POST',
        headers: {'Content-Type': 'application/json'}
    })
    .then(response => response.json())
    .then(data => {
        if (data.success) {
            updateLiveStatus(false);
            stopLiveStatusPolling();
            showNotification('Live training stopped', 'info');
        }
    });
}

function startLiveStatusPolling() {
    liveTrainingInterval = setInterval(() => {
        fetch('/api/live-training/status')
            .then(response => response.json())
            .then(data => {
                if (data.success && data.active) {
                    updateLiveMetrics(data.stats);
                }
            });
    }, 2000); // Poll every 2 seconds
}

function stopLiveStatusPolling() {
    if (liveTrainingInterval) {
        clearInterval(liveTrainingInterval);
        liveTrainingInterval = null;
    }
}

function updateLiveStatus(active) {
    const badge = document.getElementById('live-status-badge');
    const text = document.getElementById('live-status-text');
    const startBtn = document.getElementById('start-live-btn');
    const stopBtn = document.getElementById('stop-live-btn');
    const metrics = document.getElementById('live-metrics');
    
    if (active) {
        badge.className = 'badge bg-success';
        badge.textContent = 'Active';
        text.textContent = 'Running';
        startBtn.disabled = true;
        stopBtn.disabled = false;
        metrics.style.display = 'block';
    } else {
        badge.className = 'badge bg-secondary';
        badge.textContent = 'Inactive';
        text.textContent = 'Not Running';
        startBtn.disabled = false;
        stopBtn.disabled = true;
        metrics.style.display = 'none';
    }
}

function updateLiveMetrics(stats) {
    document.getElementById('predictions-per-sec').textContent = 
        (stats.predictions_per_sec || 0).toFixed(1);
    document.getElementById('training-per-min').textContent = 
        (stats.training_batches_per_min || 0).toFixed(0);
    document.getElementById('accuracy-1m').textContent = 
        ((stats.accuracy || 0) * 100).toFixed(1) + '%';
    document.getElementById('gpu-load').textContent = 
        ((stats.gpu_load || 0) * 100).toFixed(0) + '%';
}

// Initialize on page load
document.addEventListener('DOMContentLoaded', function() {
    document.getElementById('start-live-btn').addEventListener('click', startLiveTraining);
    document.getElementById('stop-live-btn').addEventListener('click', stopLiveTraining);
    
    // Check initial status
    fetch('/api/live-training/status')
        .then(response => response.json())
        .then(data => {
            if (data.success && data.active) {
                updateLiveStatus(true);
                startLiveStatusPolling();
            }
        });
});
```

---

## Testing

### 1. Test Backend Integration

```python
# In Python console or test script
from core.orchestrator import TradingOrchestrator
from core.data_provider import DataProvider

# Initialize
data_provider = DataProvider()
orchestrator = TradingOrchestrator(data_provider=data_provider)

# Check if available
print(f"Enhanced training available: {orchestrator.enhanced_training_system is not None}")

# Start live training
success = orchestrator.start_live_training()
print(f"Started: {success}")

# Check status
active = orchestrator.is_live_training_active()
print(f"Active: {active}")

# Get stats
stats = orchestrator.get_live_training_stats()
print(f"Stats: {stats}")

# Stop
orchestrator.stop_live_training()
```

### 2. Test API Endpoints

```bash
# Start
curl -X POST http://localhost:8051/api/live-training/start

# Status
curl http://localhost:8051/api/live-training/status

# Stop
curl -X POST http://localhost:8051/api/live-training/stop
```

---

## Next Steps

### Priority 1: Core Functionality
1. Implement `_live_inference_loop()` in `EnhancedRealtimeTrainingSystem`
2. Implement `_make_next_candle_prediction()` using transformer
3. Implement `_train_on_timeframe()` for batch training
4. Implement `_resolve_timeframe_predictions()` for accuracy tracking

### Priority 2: UI Integration
1. Create live training panel HTML
2. Add JavaScript controls
3. Add CSS styling
4. Integrate into main dashboard

### Priority 3: Testing & Optimization
1. Test with 1s timeframe first
2. Monitor GPU/CPU usage
3. Optimize batch sizes
4. Add error handling
5. Add logging

---

## Summary

### ✅ Completed
- Orchestrator integration
- Start/stop methods
- API endpoints
- Basic infrastructure

### ⏳ In Progress
- Core prediction loop
- Training logic
- UI components

### 📋 TODO
- Implement prediction methods
- Implement training methods
- Create UI panel
- Add JavaScript controls
- Testing and optimization

The foundation is in place - now we need to implement the core prediction and training logic!