gogo2/ANNOTATE/LIVE_UPDATES_IMPLEMENTATION.md

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

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:

# 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:

# 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