384 lines
10 KiB
Markdown
384 lines
10 KiB
Markdown
# Live Training Implementation Status
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## ✅ Phase 1: Backend Integration (COMPLETED)
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### 1. Orchestrator Integration
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**File**: `core/orchestrator.py`
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#### Initialization
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```python
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# Initialize EnhancedRealtimeTrainingSystem
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if ENHANCED_TRAINING_AVAILABLE:
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self.enhanced_training_system = EnhancedRealtimeTrainingSystem(
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orchestrator=self,
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data_provider=self.data_provider,
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dashboard=None
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)
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logger.info("EnhancedRealtimeTrainingSystem initialized successfully")
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```
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#### Methods Added
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1. **`start_live_training()`** - Start live inference and training
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2. **`stop_live_training()`** - Stop live training
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3. **`is_live_training_active()`** - Check if active
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4. **`get_live_training_stats()`** - Get performance statistics
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---
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### 2. API Endpoints
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**File**: `ANNOTATE/web/app.py`
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#### Endpoints Added
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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| `/api/live-training/start` | POST | Start live training mode |
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| `/api/live-training/stop` | POST | Stop live training mode |
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| `/api/live-training/status` | GET | Get status and statistics |
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#### Example Usage
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**Start Live Training:**
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```bash
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curl -X POST http://localhost:8051/api/live-training/start
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```
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Response:
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```json
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{
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"success": true,
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"status": "started",
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"message": "Live training mode started"
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}
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```
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**Get Status:**
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```bash
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curl http://localhost:8051/api/live-training/status
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```
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Response:
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```json
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{
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"success": true,
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"active": true,
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"stats": {
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"predictions_per_sec": 1.0,
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"training_batches_per_min": 6,
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"accuracy": 0.75,
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"models": {
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"dqn": {"loss": 0.234, "accuracy": 0.78},
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"cnn": {"loss": 0.156, "accuracy": 0.82}
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}
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}
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}
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```
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---
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## ⏳ Phase 2: Core Functionality (NEEDS IMPLEMENTATION)
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### What's Missing
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The `EnhancedRealtimeTrainingSystem` exists but needs these methods implemented:
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#### 1. Live Inference Loop
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```python
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def _live_inference_loop(self):
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"""Main loop - predict next candle every second"""
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# TODO: Implement
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pass
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```
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#### 2. Prediction Method
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```python
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def _make_next_candle_prediction(self, timeframe: str) -> Dict:
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"""Predict next candle OHLCV values"""
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# TODO: Implement using transformer's next_candles output
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pass
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```
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#### 3. Training Method
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```python
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def _train_on_timeframe(self, timeframe: str):
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"""Train on resolved predictions"""
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# TODO: Implement batch training
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pass
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```
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#### 4. Prediction Resolution
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```python
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def _resolve_timeframe_predictions(self, timeframe: str):
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"""Compare predicted vs actual candles"""
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# TODO: Implement
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pass
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```
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---
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## ⏳ Phase 3: UI Integration (NEEDS IMPLEMENTATION)
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### Dashboard Panel
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Create `ANNOTATE/web/templates/components/live_training_panel.html`:
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```html
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<div class="card mb-4">
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<div class="card-header">
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<h5>Live Inference & Training</h5>
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</div>
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<div class="card-body">
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<div class="row mb-3">
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<div class="col-md-6">
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<div class="status-indicator">
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<span id="live-status-badge" class="badge bg-secondary">Inactive</span>
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<span id="live-status-text">Not Running</span>
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</div>
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</div>
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<div class="col-md-6 text-end">
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<button id="start-live-btn" class="btn btn-success">
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<i class="fas fa-play"></i> Start Live Mode
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</button>
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<button id="stop-live-btn" class="btn btn-danger" disabled>
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<i class="fas fa-stop"></i> Stop
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</button>
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</div>
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</div>
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<div class="row" id="live-metrics" style="display: none;">
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<div class="col-md-3">
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<div class="metric-card">
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<div class="metric-label">Predictions/sec</div>
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<div class="metric-value" id="predictions-per-sec">0</div>
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</div>
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</div>
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<div class="col-md-3">
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<div class="metric-card">
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<div class="metric-label">Training/min</div>
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<div class="metric-value" id="training-per-min">0</div>
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</div>
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</div>
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<div class="col-md-3">
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<div class="metric-card">
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<div class="metric-label">Accuracy (1m)</div>
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<div class="metric-value" id="accuracy-1m">0%</div>
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</div>
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</div>
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<div class="col-md-3">
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<div class="metric-card">
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<div class="metric-label">GPU Load</div>
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<div class="metric-value" id="gpu-load">0%</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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```
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### JavaScript Integration
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Add to `ANNOTATE/web/static/js/main.js`:
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```javascript
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// Live Training Controls
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let liveTrainingInterval = null;
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function startLiveTraining() {
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fetch('/api/live-training/start', {
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method: 'POST',
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headers: {'Content-Type': 'application/json'}
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})
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.then(response => response.json())
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.then(data => {
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if (data.success) {
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updateLiveStatus(true);
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startLiveStatusPolling();
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showNotification('Live training started', 'success');
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} else {
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showNotification('Failed to start: ' + data.error, 'error');
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}
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});
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}
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function stopLiveTraining() {
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fetch('/api/live-training/stop', {
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method: 'POST',
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headers: {'Content-Type': 'application/json'}
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})
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.then(response => response.json())
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.then(data => {
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if (data.success) {
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updateLiveStatus(false);
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stopLiveStatusPolling();
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showNotification('Live training stopped', 'info');
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}
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});
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}
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function startLiveStatusPolling() {
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liveTrainingInterval = setInterval(() => {
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fetch('/api/live-training/status')
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.then(response => response.json())
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.then(data => {
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if (data.success && data.active) {
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updateLiveMetrics(data.stats);
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}
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});
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}, 2000); // Poll every 2 seconds
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}
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function stopLiveStatusPolling() {
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if (liveTrainingInterval) {
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clearInterval(liveTrainingInterval);
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liveTrainingInterval = null;
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}
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}
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function updateLiveStatus(active) {
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const badge = document.getElementById('live-status-badge');
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const text = document.getElementById('live-status-text');
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const startBtn = document.getElementById('start-live-btn');
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const stopBtn = document.getElementById('stop-live-btn');
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const metrics = document.getElementById('live-metrics');
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if (active) {
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badge.className = 'badge bg-success';
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badge.textContent = 'Active';
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text.textContent = 'Running';
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startBtn.disabled = true;
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stopBtn.disabled = false;
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metrics.style.display = 'block';
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} else {
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badge.className = 'badge bg-secondary';
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badge.textContent = 'Inactive';
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text.textContent = 'Not Running';
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startBtn.disabled = false;
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stopBtn.disabled = true;
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metrics.style.display = 'none';
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}
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}
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function updateLiveMetrics(stats) {
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document.getElementById('predictions-per-sec').textContent =
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(stats.predictions_per_sec || 0).toFixed(1);
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document.getElementById('training-per-min').textContent =
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(stats.training_batches_per_min || 0).toFixed(0);
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document.getElementById('accuracy-1m').textContent =
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((stats.accuracy || 0) * 100).toFixed(1) + '%';
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document.getElementById('gpu-load').textContent =
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((stats.gpu_load || 0) * 100).toFixed(0) + '%';
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}
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// Initialize on page load
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document.addEventListener('DOMContentLoaded', function() {
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document.getElementById('start-live-btn').addEventListener('click', startLiveTraining);
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document.getElementById('stop-live-btn').addEventListener('click', stopLiveTraining);
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// Check initial status
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fetch('/api/live-training/status')
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.then(response => response.json())
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.then(data => {
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if (data.success && data.active) {
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updateLiveStatus(true);
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startLiveStatusPolling();
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}
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});
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});
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```
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---
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## Testing
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### 1. Test Backend Integration
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```python
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# In Python console or test script
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from core.orchestrator import TradingOrchestrator
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from core.data_provider import DataProvider
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# Initialize
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data_provider = DataProvider()
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orchestrator = TradingOrchestrator(data_provider=data_provider)
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# Check if available
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print(f"Enhanced training available: {orchestrator.enhanced_training_system is not None}")
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# Start live training
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success = orchestrator.start_live_training()
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print(f"Started: {success}")
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# Check status
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active = orchestrator.is_live_training_active()
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print(f"Active: {active}")
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# Get stats
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stats = orchestrator.get_live_training_stats()
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print(f"Stats: {stats}")
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# Stop
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orchestrator.stop_live_training()
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```
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### 2. Test API Endpoints
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```bash
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# Start
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curl -X POST http://localhost:8051/api/live-training/start
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# Status
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curl http://localhost:8051/api/live-training/status
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# Stop
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curl -X POST http://localhost:8051/api/live-training/stop
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```
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---
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## Next Steps
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### Priority 1: Core Functionality
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1. Implement `_live_inference_loop()` in `EnhancedRealtimeTrainingSystem`
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2. Implement `_make_next_candle_prediction()` using transformer
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3. Implement `_train_on_timeframe()` for batch training
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4. Implement `_resolve_timeframe_predictions()` for accuracy tracking
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### Priority 2: UI Integration
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1. Create live training panel HTML
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2. Add JavaScript controls
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3. Add CSS styling
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4. Integrate into main dashboard
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### Priority 3: Testing & Optimization
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1. Test with 1s timeframe first
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2. Monitor GPU/CPU usage
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3. Optimize batch sizes
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4. Add error handling
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5. Add logging
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---
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## Summary
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### ✅ Completed
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- Orchestrator integration
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- Start/stop methods
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- API endpoints
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- Basic infrastructure
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### ⏳ In Progress
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- Core prediction loop
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- Training logic
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- UI components
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### 📋 TODO
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- Implement prediction methods
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- Implement training methods
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- Create UI panel
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- Add JavaScript controls
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- Testing and optimization
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The foundation is in place - now we need to implement the core prediction and training logic!
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