196 lines
6.8 KiB
Markdown
196 lines
6.8 KiB
Markdown
# Live Updates Implementation Summary
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## Overview
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Added real-time chart updates and model prediction visualization to the ANNOTATE dashboard, matching the functionality of the clean_dashboard.
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## What Was Implemented
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### 1. Live Chart Updates (1s and 1m charts)
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**Backend: `/api/live-updates` endpoint** (`ANNOTATE/web/app.py`)
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- Polls for latest candle data from orchestrator's data provider
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- Returns latest OHLCV data for requested symbol/timeframe
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- Provides model predictions (DQN, CNN, Transformer)
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**Frontend: Polling System** (`ANNOTATE/web/static/js/live_updates_polling.js`)
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- Polls `/api/live-updates` every 2 seconds
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- Subscribes to active timeframes (1s, 1m, etc.)
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- Automatically updates charts with new candles
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**Chart Updates** (`ANNOTATE/web/static/js/chart_manager.js`)
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- `updateLatestCandle()` - Updates or extends chart with new candle
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- Efficiently uses Plotly's `extendTraces` for new candles
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- Uses `restyle` for updating current candle
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### 2. Model Prediction Visualization
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**Orchestrator Storage** (`core/orchestrator.py`)
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- Added `recent_transformer_predictions` tracking
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- Added `store_transformer_prediction()` method
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- Tracks last 50 predictions per symbol
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**Dashboard Visualization** (`web/clean_dashboard.py`)
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- Added `_add_transformer_predictions_to_chart()` method
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- Added `_get_recent_transformer_predictions()` helper
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- Transformer predictions shown as:
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- Cyan lines/stars for UP predictions
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- Orange lines/stars for DOWN predictions
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- Light blue lines/stars for STABLE predictions
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**ANNOTATE Visualization** (`ANNOTATE/web/static/js/chart_manager.js`)
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- Added `updatePredictions()` method
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- Added `_addDQNPrediction()` - Shows arrows (▲/▼)
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- Added `_addCNNPrediction()` - Shows trend lines with diamonds (◆)
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- Added `_addTransformerPrediction()` - Shows trend lines with stars (★)
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## How It Works
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### Live Chart Updates Flow
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```
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1. JavaScript polls /api/live-updates every 2s
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2. Backend fetches latest candle from data_provider
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3. Backend returns candle + predictions
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4. Frontend updates chart with new/updated candle
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5. Frontend visualizes model predictions
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```
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### Prediction Visualization Flow
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```
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1. Model makes prediction
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2. Call orchestrator.store_transformer_prediction(symbol, prediction_data)
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3. Prediction stored in recent_transformer_predictions deque
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4. Dashboard/ANNOTATE polls and retrieves predictions
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5. Predictions rendered as shapes/annotations on charts
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```
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## Usage
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### Storing Transformer Predictions
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```python
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orchestrator.store_transformer_prediction('ETH/USDT', {
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'timestamp': datetime.now(),
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'confidence': 0.75,
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'current_price': 3500.0,
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'predicted_price': 3550.0,
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'price_change': 1.43, # percentage
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'horizon_minutes': 10
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})
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```
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### Enabling Live Updates
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Live updates start automatically when:
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- ANNOTATE dashboard loads
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- Charts are initialized
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- appState.currentTimeframes is set
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### Prediction Display
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Predictions appear on the 1m chart as:
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- **DQN**: Green/red arrows for BUY/SELL
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- **CNN**: Dotted trend lines with diamond markers
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- **Transformer**: Dash-dot lines with star markers
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- Opacity/size scales with confidence
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## Backtest Prediction Visualization
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### Implemented
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1. **Automatic prediction clearing** - Predictions are cleared when backtest starts
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2. **Prediction storage during backtest** - All model predictions stored in orchestrator
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3. **Model type detection** - Automatically detects Transformer/CNN/DQN models
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4. **Real-time visualization** - Predictions appear on charts as backtest runs
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## Training Prediction Visualization
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### Implemented
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1. **Automatic prediction clearing** - Predictions cleared when training starts
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2. **Prediction storage during training** - Model predictions stored every 10 batches on first epoch
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3. **Metadata tracking** - Current price and timestamp stored with each batch
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4. **Real-time visualization** - Training predictions appear on charts via polling
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### How It Works
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**Backtest:**
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```python
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# When backtest starts:
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orchestrator.clear_predictions(symbol)
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# During backtest, for each prediction:
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if 'transformer' in model_type:
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orchestrator.store_transformer_prediction(symbol, prediction_data)
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elif 'cnn' in model_type:
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orchestrator.recent_cnn_predictions[symbol].append(prediction_data)
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elif 'dqn' in model_type:
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orchestrator.recent_dqn_predictions[symbol].append(prediction_data)
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# Predictions automatically appear via /api/live-updates polling
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```
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**Training:**
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```python
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# When training starts:
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orchestrator.clear_predictions(symbol)
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# During training (every 10 batches on first epoch):
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with torch.no_grad():
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trainer.model.eval()
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outputs = trainer.model(**batch)
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# Extract prediction and store
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orchestrator.store_transformer_prediction(symbol, {
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'timestamp': batch['metadata']['timestamp'],
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'current_price': batch['metadata']['current_price'],
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'action': predicted_action,
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'confidence': confidence,
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'source': 'training'
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})
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# Predictions automatically appear via /api/live-updates polling
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```
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## What's Still Missing
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### Not Yet Implemented
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1. **Historical prediction replay** - Can't replay predictions from past sessions
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2. **Prediction accuracy overlay** - No visual feedback on prediction outcomes
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3. **Multi-symbol predictions** - Only primary symbol predictions are tracked
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4. **Prediction persistence** - Predictions are lost when app restarts
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## Files Modified
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### Backend
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- `core/orchestrator.py` - Added transformer prediction tracking
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- `web/clean_dashboard.py` - Added transformer visualization
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- `ANNOTATE/web/app.py` - Added `/api/live-updates` endpoint
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### Frontend
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- `ANNOTATE/web/static/js/chart_manager.js` - Added prediction visualization methods
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- `ANNOTATE/web/static/js/live_updates_polling.js` - Updated to call prediction visualization
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## Testing
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### Verify Live Updates
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1. Open ANNOTATE dashboard
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2. Check browser console for "Started polling for live updates"
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3. Watch 1s/1m charts update every 2 seconds
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4. Check network tab for `/api/live-updates` requests
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### Verify Predictions
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1. Ensure orchestrator is running
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2. Make predictions using models
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3. Call `orchestrator.store_transformer_prediction()`
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4. Check charts for prediction markers
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5. Verify predictions appear in `/api/live-updates` response
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## Performance
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- **Polling frequency**: 2 seconds (configurable)
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- **Prediction storage**: 50 predictions per symbol (deque with maxlen)
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- **Chart updates**: Efficient Plotly operations (extendTraces/restyle)
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- **Memory usage**: Minimal (predictions auto-expire from deque)
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## Next Steps
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1. **Add backtest prediction storage** - Store predictions during backtest runs
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2. **Add prediction outcome tracking** - Track if predictions were accurate
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3. **Add prediction accuracy visualization** - Show success/failure markers
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4. **Add prediction filtering** - Filter by model, confidence, timeframe
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5. **Add prediction export** - Export predictions for analysis
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