# Live Updates Implementation Summary

## Overview
Added real-time chart updates and model prediction visualization to the ANNOTATE dashboard, matching the functionality of the clean_dashboard.

## What Was Implemented

### 1. Live Chart Updates (1s and 1m charts)

**Backend: `/api/live-updates` endpoint** (`ANNOTATE/web/app.py`)
- Polls for latest candle data from orchestrator's data provider
- Returns latest OHLCV data for requested symbol/timeframe
- Provides model predictions (DQN, CNN, Transformer)

**Frontend: Polling System** (`ANNOTATE/web/static/js/live_updates_polling.js`)
- Polls `/api/live-updates` every 2 seconds
- Subscribes to active timeframes (1s, 1m, etc.)
- Automatically updates charts with new candles

**Chart Updates** (`ANNOTATE/web/static/js/chart_manager.js`)
- `updateLatestCandle()` - Updates or extends chart with new candle
- Efficiently uses Plotly's `extendTraces` for new candles
- Uses `restyle` for updating current candle

### 2. Model Prediction Visualization

**Orchestrator Storage** (`core/orchestrator.py`)
- Added `recent_transformer_predictions` tracking
- Added `store_transformer_prediction()` method
- Tracks last 50 predictions per symbol

**Dashboard Visualization** (`web/clean_dashboard.py`)
- Added `_add_transformer_predictions_to_chart()` method
- Added `_get_recent_transformer_predictions()` helper
- Transformer predictions shown as:
  - Cyan lines/stars for UP predictions
  - Orange lines/stars for DOWN predictions
  - Light blue lines/stars for STABLE predictions

**ANNOTATE Visualization** (`ANNOTATE/web/static/js/chart_manager.js`)
- Added `updatePredictions()` method
- Added `_addDQNPrediction()` - Shows arrows (▲/▼)
- Added `_addCNNPrediction()` - Shows trend lines with diamonds (◆)
- Added `_addTransformerPrediction()` - Shows trend lines with stars (★)

## How It Works

### Live Chart Updates Flow
```
1. JavaScript polls /api/live-updates every 2s
2. Backend fetches latest candle from data_provider
3. Backend returns candle + predictions
4. Frontend updates chart with new/updated candle
5. Frontend visualizes model predictions
```

### Prediction Visualization Flow
```
1. Model makes prediction
2. Call orchestrator.store_transformer_prediction(symbol, prediction_data)
3. Prediction stored in recent_transformer_predictions deque
4. Dashboard/ANNOTATE polls and retrieves predictions
5. Predictions rendered as shapes/annotations on charts
```

## Usage

### Storing Transformer Predictions
```python
orchestrator.store_transformer_prediction('ETH/USDT', {
    'timestamp': datetime.now(),
    'confidence': 0.75,
    'current_price': 3500.0,
    'predicted_price': 3550.0,
    'price_change': 1.43,  # percentage
    'horizon_minutes': 10
})
```

### Enabling Live Updates
Live updates start automatically when:
- ANNOTATE dashboard loads
- Charts are initialized
- appState.currentTimeframes is set

### Prediction Display
Predictions appear on the 1m chart as:
- **DQN**: Green/red arrows for BUY/SELL
- **CNN**: Dotted trend lines with diamond markers
- **Transformer**: Dash-dot lines with star markers
- Opacity/size scales with confidence

## Backtest Prediction Visualization

### Implemented
1. **Automatic prediction clearing** - Predictions are cleared when backtest starts
2. **Prediction storage during backtest** - All model predictions stored in orchestrator
3. **Model type detection** - Automatically detects Transformer/CNN/DQN models
4. **Real-time visualization** - Predictions appear on charts as backtest runs

## Training Prediction Visualization

### Implemented
1. **Automatic prediction clearing** - Predictions cleared when training starts
2. **Prediction storage during training** - Model predictions stored every 10 batches on first epoch
3. **Metadata tracking** - Current price and timestamp stored with each batch
4. **Real-time visualization** - Training predictions appear on charts via polling

### How It Works

**Backtest:**
```python
# When backtest starts:
orchestrator.clear_predictions(symbol)

# During backtest, for each prediction:
if 'transformer' in model_type:
    orchestrator.store_transformer_prediction(symbol, prediction_data)
elif 'cnn' in model_type:
    orchestrator.recent_cnn_predictions[symbol].append(prediction_data)
elif 'dqn' in model_type:
    orchestrator.recent_dqn_predictions[symbol].append(prediction_data)

# Predictions automatically appear via /api/live-updates polling
```

**Training:**
```python
# When training starts:
orchestrator.clear_predictions(symbol)

# During training (every 10 batches on first epoch):
with torch.no_grad():
    trainer.model.eval()
    outputs = trainer.model(**batch)
    # Extract prediction and store
    orchestrator.store_transformer_prediction(symbol, {
        'timestamp': batch['metadata']['timestamp'],
        'current_price': batch['metadata']['current_price'],
        'action': predicted_action,
        'confidence': confidence,
        'source': 'training'
    })

# Predictions automatically appear via /api/live-updates polling
```

## What's Still Missing

### Not Yet Implemented
1. **Historical prediction replay** - Can't replay predictions from past sessions
2. **Prediction accuracy overlay** - No visual feedback on prediction outcomes
3. **Multi-symbol predictions** - Only primary symbol predictions are tracked
4. **Prediction persistence** - Predictions are lost when app restarts

## Files Modified

### Backend
- `core/orchestrator.py` - Added transformer prediction tracking
- `web/clean_dashboard.py` - Added transformer visualization
- `ANNOTATE/web/app.py` - Added `/api/live-updates` endpoint

### Frontend
- `ANNOTATE/web/static/js/chart_manager.js` - Added prediction visualization methods
- `ANNOTATE/web/static/js/live_updates_polling.js` - Updated to call prediction visualization

## Testing

### Verify Live Updates
1. Open ANNOTATE dashboard
2. Check browser console for "Started polling for live updates"
3. Watch 1s/1m charts update every 2 seconds
4. Check network tab for `/api/live-updates` requests

### Verify Predictions
1. Ensure orchestrator is running
2. Make predictions using models
3. Call `orchestrator.store_transformer_prediction()`
4. Check charts for prediction markers
5. Verify predictions appear in `/api/live-updates` response

## Performance

- **Polling frequency**: 2 seconds (configurable)
- **Prediction storage**: 50 predictions per symbol (deque with maxlen)
- **Chart updates**: Efficient Plotly operations (extendTraces/restyle)
- **Memory usage**: Minimal (predictions auto-expire from deque)

## Next Steps

1. **Add backtest prediction storage** - Store predictions during backtest runs
2. **Add prediction outcome tracking** - Track if predictions were accurate
3. **Add prediction accuracy visualization** - Show success/failure markers
4. **Add prediction filtering** - Filter by model, confidence, timeframe
5. **Add prediction export** - Export predictions for analysis