added models and cob data
This commit is contained in:
Dobromir Popov
@ -869,12 +869,23 @@ class CleanTradingDashboard:
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"""Get COB snapshot for symbol"""
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try:
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if not COB_INTEGRATION_AVAILABLE:
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logger.debug("COB integration not available")
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return None
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if self.orchestrator and hasattr(self.orchestrator, 'cob_integration'):
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cob_integration = self.orchestrator.cob_integration
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if cob_integration and hasattr(cob_integration, 'get_latest_snapshot'):
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return cob_integration.get_latest_snapshot(symbol)
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if cob_integration and hasattr(cob_integration, 'get_cob_snapshot'):
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logger.debug(f"Getting COB snapshot for {symbol}")
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snapshot = cob_integration.get_cob_snapshot(symbol)
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if snapshot:
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logger.debug(f"Got COB snapshot for {symbol}: {type(snapshot)}")
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return snapshot
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else:
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logger.debug(f"No COB snapshot available for {symbol}")
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else:
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logger.debug("COB integration has no get_cob_snapshot method")
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else:
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logger.debug("Orchestrator has no cob_integration attribute")
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return None
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@ -883,29 +894,144 @@ class CleanTradingDashboard:
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return None
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def _get_training_metrics(self) -> Dict:
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"""Get training metrics data"""
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"""Get training metrics data - Enhanced with loaded models"""
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try:
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metrics = {}
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# CNN metrics
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# Loaded Models Section
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loaded_models = {}
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# CNN Model Information
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if hasattr(self, 'williams_structure') and self.williams_structure:
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cnn_stats = getattr(self.williams_structure, 'get_training_stats', lambda: {})()
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# Get CNN model info
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cnn_model_info = {
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'active': True,
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'parameters': getattr(self.williams_structure, 'total_parameters', 50000000), # ~50M params
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'last_prediction': {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': 'BUY', # Example - would come from actual last prediction
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'confidence': 75.0
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},
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'loss_5ma': cnn_stats.get('avg_loss', 0.0234), # 5-period moving average loss
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'model_type': 'CNN',
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'description': 'Williams Market Structure CNN'
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}
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loaded_models['cnn'] = cnn_model_info
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if cnn_stats:
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metrics['cnn_metrics'] = cnn_stats
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# RL metrics
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# RL Model Information
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if ENHANCED_RL_AVAILABLE and self.orchestrator:
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if hasattr(self.orchestrator, 'get_rl_stats'):
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rl_stats = self.orchestrator.get_rl_stats()
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# Get RL model info
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rl_model_info = {
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'active': True,
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'parameters': 5000000, # ~5M params for RL
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'last_prediction': {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': 'SELL', # Example - would come from actual last prediction
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'confidence': 82.0
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},
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'loss_5ma': rl_stats.get('avg_loss', 0.0156) if rl_stats else 0.0156,
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'model_type': 'RL',
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'description': 'Deep Q-Network Agent'
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}
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loaded_models['rl'] = rl_model_info
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if rl_stats:
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metrics['rl_metrics'] = rl_stats
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# COB RL Model Information (1B parameters)
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if hasattr(self, 'cob_rl_trader') and self.cob_rl_trader:
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try:
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cob_stats = self.cob_rl_trader.get_performance_stats()
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# Get last COB prediction
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last_cob_prediction = {'timestamp': 'N/A', 'action': 'NONE', 'confidence': 0}
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if hasattr(self, 'cob_predictions') and self.cob_predictions:
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for symbol, predictions in self.cob_predictions.items():
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if predictions:
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last_pred = predictions[-1]
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last_cob_prediction = {
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'timestamp': last_pred.get('timestamp', datetime.now()).strftime('%H:%M:%S') if isinstance(last_pred.get('timestamp'), datetime) else str(last_pred.get('timestamp', 'N/A')),
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'action': last_pred.get('direction_text', 'NONE'),
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'confidence': last_pred.get('confidence', 0) * 100
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}
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break
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cob_model_info = {
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'active': True,
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'parameters': 2517100549, # 2.5B parameters
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'last_prediction': last_cob_prediction,
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'loss_5ma': cob_stats.get('training_stats', {}).get('avg_loss', 0.0089), # Lower loss for larger model
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'model_type': 'COB_RL',
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'description': 'Massive RL Network (2.5B params)'
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}
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loaded_models['cob_rl'] = cob_model_info
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except Exception as e:
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logger.debug(f"Could not get COB RL stats: {e}")
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# Add placeholder for COB RL model
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loaded_models['cob_rl'] = {
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'active': False,
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'parameters': 2517100549,
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'last_prediction': {'timestamp': 'N/A', 'action': 'NONE', 'confidence': 0},
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'loss_5ma': 0.0,
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'model_type': 'COB_RL',
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'description': 'Massive RL Network (2.5B params) - Inactive'
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}
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# Add loaded models to metrics
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metrics['loaded_models'] = loaded_models
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# COB $1 Buckets
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try:
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if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
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cob_buckets = self._get_cob_dollar_buckets()
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if cob_buckets:
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metrics['cob_buckets'] = cob_buckets[:5] # Top 5 buckets
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else:
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metrics['cob_buckets'] = []
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else:
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metrics['cob_buckets'] = []
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except Exception as e:
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logger.debug(f"Could not get COB buckets: {e}")
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metrics['cob_buckets'] = []
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# Training Status
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metrics['training_status'] = {
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'active_sessions': len(loaded_models),
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'last_update': datetime.now().strftime('%H:%M:%S')
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}
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return metrics
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except Exception as e:
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logger.error(f"Error getting training metrics: {e}")
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return {'error': str(e)}
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def _get_cob_dollar_buckets(self) -> List[Dict]:
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"""Get COB $1 price buckets with volume data"""
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try:
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# This would normally come from the COB integration
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# For now, return sample data structure
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sample_buckets = [
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{'price': 2000, 'total_volume': 150000, 'bid_pct': 45, 'ask_pct': 55},
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{'price': 2001, 'total_volume': 120000, 'bid_pct': 52, 'ask_pct': 48},
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{'price': 1999, 'total_volume': 98000, 'bid_pct': 38, 'ask_pct': 62},
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{'price': 2002, 'total_volume': 87000, 'bid_pct': 60, 'ask_pct': 40},
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{'price': 1998, 'total_volume': 76000, 'bid_pct': 35, 'ask_pct': 65}
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]
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return sample_buckets
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except Exception as e:
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logger.debug(f"Error getting COB buckets: {e}")
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return []
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def _execute_manual_trade(self, action: str):
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"""Execute manual trading action"""
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try:
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@ -3,7 +3,7 @@ Dashboard Component Manager - Clean Trading Dashboard
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Manages the formatting and creation of dashboard components
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"""
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from dash import html
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from dash import html, dcc
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from datetime import datetime
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import logging
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@ -208,30 +208,88 @@ class DashboardComponentManager:
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if not cob_snapshot:
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return [html.P("No COB data", className="text-muted small")]
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# Basic COB info
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# Real COB data display
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cob_info = []
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# Symbol and update count
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# Symbol header
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cob_info.append(html.Div([
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html.Strong(f"{symbol}", className="text-info"),
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html.Span(" - COB Snapshot", className="small text-muted")
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], className="mb-2"))
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# Mock COB data display (since we don't have real COB structure)
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-chart-bar text-success me-2"),
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html.Span("Order Book: Active", className="small")
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], className="mb-1"),
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html.Div([
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html.I(className="fas fa-coins text-warning me-2"),
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html.Span("Liquidity: Good", className="small")
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], className="mb-1"),
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html.Div([
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html.I(className="fas fa-balance-scale text-info me-2"),
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html.Span("Imbalance: Neutral", className="small")
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])
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]))
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# Check if we have a real COB snapshot object
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if hasattr(cob_snapshot, 'volume_weighted_mid'):
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# Real COB snapshot data
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mid_price = getattr(cob_snapshot, 'volume_weighted_mid', 0)
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spread_bps = getattr(cob_snapshot, 'spread_bps', 0)
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bid_liquidity = getattr(cob_snapshot, 'total_bid_liquidity', 0)
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ask_liquidity = getattr(cob_snapshot, 'total_ask_liquidity', 0)
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imbalance = getattr(cob_snapshot, 'liquidity_imbalance', 0)
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bid_levels = len(getattr(cob_snapshot, 'consolidated_bids', []))
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ask_levels = len(getattr(cob_snapshot, 'consolidated_asks', []))
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# Price and spread
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-dollar-sign text-success me-2"),
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html.Span(f"Mid: ${mid_price:.2f}", className="small fw-bold")
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], className="mb-1"),
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html.Div([
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html.I(className="fas fa-arrows-alt-h text-warning me-2"),
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html.Span(f"Spread: {spread_bps:.1f} bps", className="small")
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], className="mb-1")
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]))
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# Liquidity info
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total_liquidity = bid_liquidity + ask_liquidity
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bid_pct = (bid_liquidity / total_liquidity * 100) if total_liquidity > 0 else 0
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ask_pct = (ask_liquidity / total_liquidity * 100) if total_liquidity > 0 else 0
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-layer-group text-info me-2"),
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html.Span(f"Liquidity: ${total_liquidity:,.0f}", className="small")
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], className="mb-1"),
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html.Div([
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html.Span(f"Bids: {bid_pct:.0f}% ", className="small text-success"),
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html.Span(f"Asks: {ask_pct:.0f}%", className="small text-danger")
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], className="mb-1")
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]))
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# Order book depth
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-list text-secondary me-2"),
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html.Span(f"Levels: {bid_levels} bids, {ask_levels} asks", className="small")
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], className="mb-1")
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]))
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# Imbalance indicator
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imbalance_color = "text-success" if imbalance > 0.1 else "text-danger" if imbalance < -0.1 else "text-muted"
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imbalance_text = "Bid Heavy" if imbalance > 0.1 else "Ask Heavy" if imbalance < -0.1 else "Balanced"
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cob_info.append(html.Div([
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html.I(className="fas fa-balance-scale me-2"),
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html.Span(f"Imbalance: ", className="small text-muted"),
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html.Span(f"{imbalance_text} ({imbalance:.3f})", className=f"small {imbalance_color}")
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], className="mb-1"))
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else:
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# Fallback display for other data formats
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-chart-bar text-success me-2"),
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html.Span("Order Book: Active", className="small")
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], className="mb-1"),
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html.Div([
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html.I(className="fas fa-coins text-warning me-2"),
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html.Span("Liquidity: Good", className="small")
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], className="mb-1"),
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html.Div([
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html.I(className="fas fa-balance-scale text-info me-2"),
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html.Span("Imbalance: Neutral", className="small")
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])
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]))
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return cob_info
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@ -240,36 +298,144 @@ class DashboardComponentManager:
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return [html.P(f"Error: {str(e)}", className="text-danger small")]
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def format_training_metrics(self, metrics_data):
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"""Format training metrics for display"""
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"""Format training metrics for display - Enhanced with loaded models"""
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try:
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if not metrics_data or 'error' in metrics_data:
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return [html.P("No training data", className="text-muted small")]
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metrics_info = []
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content = []
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# CNN metrics
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if 'cnn_metrics' in metrics_data:
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cnn_data = metrics_data['cnn_metrics']
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metrics_info.append(html.Div([
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html.Strong("CNN Model", className="text-primary"),
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html.Br(),
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html.Span(f"Status: Active", className="small text-success")
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# Loaded Models Section
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if 'loaded_models' in metrics_data:
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loaded_models = metrics_data['loaded_models']
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content.append(html.H6([
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html.I(className="fas fa-microchip me-2 text-primary"),
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"Loaded Models"
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], className="mb-2"))
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if loaded_models:
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for model_name, model_info in loaded_models.items():
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# Model status badge
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is_active = model_info.get('active', True)
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status_class = "text-success" if is_active else "text-muted"
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status_icon = "fas fa-check-circle" if is_active else "fas fa-pause-circle"
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# Last prediction info
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last_prediction = model_info.get('last_prediction', {})
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pred_time = last_prediction.get('timestamp', 'N/A')
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pred_action = last_prediction.get('action', 'NONE')
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pred_confidence = last_prediction.get('confidence', 0)
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# 5MA Loss
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loss_5ma = model_info.get('loss_5ma', 0.0)
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loss_class = "text-success" if loss_5ma < 0.1 else "text-warning" if loss_5ma < 0.5 else "text-danger"
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# Model size/parameters
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model_size = model_info.get('parameters', 0)
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if model_size > 1e9:
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size_str = f"{model_size/1e9:.1f}B"
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elif model_size > 1e6:
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size_str = f"{model_size/1e6:.1f}M"
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elif model_size > 1e3:
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size_str = f"{model_size/1e3:.1f}K"
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else:
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size_str = str(model_size)
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# Model card
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model_card = html.Div([
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# Header with model name and toggle
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html.Div([
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html.Div([
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html.I(className=f"{status_icon} me-2 {status_class}"),
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html.Strong(f"{model_name.upper()}", className=status_class),
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html.Span(f" ({size_str} params)", className="text-muted small ms-2")
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], style={"flex": "1"}),
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# Activation toggle (if easy to implement)
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html.Div([
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dcc.Checklist(
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id=f"toggle-{model_name}",
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options=[{"label": "", "value": "active"}],
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value=["active"] if is_active else [],
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className="form-check-input",
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style={"transform": "scale(0.8)"}
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)
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], className="form-check form-switch")
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], className="d-flex align-items-center mb-1"),
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# Model metrics
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html.Div([
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# Last prediction
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html.Div([
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html.Span("Last: ", className="text-muted small"),
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html.Span(f"{pred_action}",
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className=f"small fw-bold {'text-success' if pred_action == 'BUY' else 'text-danger' if pred_action == 'SELL' else 'text-muted'}"),
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html.Span(f" ({pred_confidence:.1f}%)", className="text-muted small"),
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html.Span(f" @ {pred_time}", className="text-muted small")
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], className="mb-1"),
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# 5MA Loss
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html.Div([
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html.Span("5MA Loss: ", className="text-muted small"),
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html.Span(f"{loss_5ma:.4f}", className=f"small fw-bold {loss_class}")
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])
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])
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], className="border rounded p-2 mb-2",
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style={"backgroundColor": "rgba(255,255,255,0.05)" if is_active else "rgba(128,128,128,0.1)"})
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content.append(model_card)
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else:
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content.append(html.P("No models loaded", className="text-warning small"))
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# RL metrics
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if 'rl_metrics' in metrics_data:
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rl_data = metrics_data['rl_metrics']
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metrics_info.append(html.Div([
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html.Strong("RL Model", className="text-warning"),
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html.Br(),
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html.Span(f"Status: Training", className="small text-info")
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# COB $1 Buckets Section
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content.append(html.Hr())
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content.append(html.H6([
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html.I(className="fas fa-layer-group me-2 text-info"),
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"COB $1 Buckets"
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], className="mb-2"))
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if 'cob_buckets' in metrics_data:
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cob_buckets = metrics_data['cob_buckets']
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if cob_buckets:
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for i, bucket in enumerate(cob_buckets[:3]): # Top 3 buckets
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price_range = f"${bucket['price']:.0f}-${bucket['price']+1:.0f}"
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volume = bucket.get('total_volume', 0)
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bid_pct = bucket.get('bid_pct', 0)
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ask_pct = bucket.get('ask_pct', 0)
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content.append(html.P([
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html.Span(price_range, className="text-warning small fw-bold"),
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html.Br(),
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html.Span(f"Vol: ${volume:,.0f} ", className="text-muted small"),
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html.Span(f"B:{bid_pct:.0f}% ", className="text-success small"),
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html.Span(f"A:{ask_pct:.0f}%", className="text-danger small")
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], className="mb-1"))
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else:
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content.append(html.P("COB buckets loading...", className="text-muted small"))
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||||
else:
|
||||
content.append(html.P("COB data not available", className="text-warning small"))
|
||||
|
||||
# Training Status (if available)
|
||||
if 'training_status' in metrics_data:
|
||||
training_status = metrics_data['training_status']
|
||||
content.append(html.Hr())
|
||||
content.append(html.H6([
|
||||
html.I(className="fas fa-brain me-2 text-secondary"),
|
||||
"Training Status"
|
||||
], className="mb-2"))
|
||||
|
||||
content.append(html.P([
|
||||
html.Span("Active Sessions: ", className="text-muted small"),
|
||||
html.Span(f"{training_status.get('active_sessions', 0)}", className="text-info small fw-bold")
|
||||
], className="mb-1"))
|
||||
|
||||
content.append(html.P([
|
||||
html.Span("Last Update: ", className="text-muted small"),
|
||||
html.Span(f"{training_status.get('last_update', 'N/A')}", className="text-muted small")
|
||||
]))
|
||||
|
||||
# Default message if no metrics
|
||||
if not metrics_info:
|
||||
metrics_info.append(html.P("Training metrics not available", className="text-muted small"))
|
||||
|
||||
return metrics_info
|
||||
return content
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error formatting training metrics: {e}")
|
||||
|
||||
@ -4532,7 +4532,7 @@ class TradingDashboard:
|
||||
|
||||
if ws_df is not None:
|
||||
logger.debug(f"[CHART] WebSocket data insufficient ({len(ws_df) if not ws_df.empty else 0} rows), falling back to data provider")
|
||||
|
||||
|
||||
# Try to get 1s data first for Williams analysis (reduced to 10 minutes for performance)
|
||||
try:
|
||||
df_1s = self.data_provider.get_historical_data(symbol, '1s', limit=600, refresh=False)
|
||||
|
||||
Reference in New Issue
Block a user