cob data providers tests
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Dobromir Popov
@ -2734,7 +2734,14 @@ class CleanTradingDashboard:
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'confidence': 0.0,
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'last_prediction': 'N/A',
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'training_samples': 0,
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'inference_rate': '0.00/s'
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'inference_rate': '0.00/s',
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'last_inference_time': 'Never',
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'last_inference_duration': 0.0,
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'pivot_price': None,
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'suggested_action': 'HOLD',
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'last_training_time': 'Never',
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'last_training_duration': 0.0,
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'last_training_loss': 0.0
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}
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# Get CNN prediction for ETH/USDT
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@ -2743,24 +2750,59 @@ class CleanTradingDashboard:
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# Get model performance metrics
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model_info = self.cnn_adapter.get_model_info() if hasattr(self.cnn_adapter, 'get_model_info') else {}
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# Get inference timing metrics
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last_inference_time = getattr(self.cnn_adapter, 'last_inference_time', None)
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last_inference_duration = getattr(self.cnn_adapter, 'last_inference_duration', 0.0)
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inference_count = getattr(self.cnn_adapter, 'inference_count', 0)
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# Format inference time
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if last_inference_time:
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inference_time_str = last_inference_time.strftime('%H:%M:%S')
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else:
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inference_time_str = 'Never'
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# Calculate inference rate
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inference_times = getattr(self.cnn_adapter, 'inference_times', [])
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if len(inference_times) > 0:
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avg_inference_time = sum(inference_times[-10:]) / min(len(inference_times), 10)
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inference_rate = f"{1.0/avg_inference_time:.2f}/s" if avg_inference_time > 0 else "0.00/s"
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if inference_count > 0 and last_inference_duration > 0:
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inference_rate = f"{1000.0/last_inference_duration:.2f}/s" # Convert ms to rate
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else:
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inference_rate = "0.00/s"
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# Get training timing metrics
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last_training_time = getattr(self.cnn_adapter, 'last_training_time', None)
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last_training_duration = getattr(self.cnn_adapter, 'last_training_duration', 0.0)
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last_training_loss = getattr(self.cnn_adapter, 'last_training_loss', 0.0)
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training_count = getattr(self.cnn_adapter, 'training_count', 0)
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# Format training time
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if last_training_time:
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training_time_str = last_training_time.strftime('%H:%M:%S')
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else:
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training_time_str = 'Never'
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# Get training data count
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training_samples = len(getattr(self.cnn_adapter, 'training_data', []))
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# Format last prediction
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if prediction:
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last_prediction = f"{prediction['action']} ({prediction['confidence']:.1%})"
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current_confidence = prediction['confidence']
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# Get last prediction output details
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last_prediction_output = getattr(self.cnn_adapter, 'last_prediction_output', None)
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# Format prediction details
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if last_prediction_output:
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suggested_action = last_prediction_output.get('action', 'HOLD')
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current_confidence = last_prediction_output.get('confidence', 0.0)
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pivot_price = last_prediction_output.get('pivot_price', None)
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# Format pivot price
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if pivot_price and pivot_price > 0:
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pivot_price_str = f"${pivot_price:.2f}"
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else:
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pivot_price_str = "N/A"
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last_prediction = f"{suggested_action} ({current_confidence:.1%})"
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else:
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last_prediction = "No prediction"
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suggested_action = 'HOLD'
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current_confidence = 0.0
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pivot_price_str = "N/A"
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last_prediction = "No prediction"
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# Get model status
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if hasattr(self.cnn_adapter, 'model') and self.cnn_adapter.model:
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@ -2775,14 +2817,25 @@ class CleanTradingDashboard:
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return {
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'status': status,
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'parameters': model_info.get('parameters', '50.0M'),
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'current_loss': model_info.get('current_loss', 0.0),
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'parameters': '50.0M', # Enhanced CNN parameters
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'current_loss': last_training_loss,
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'accuracy': model_info.get('accuracy', 0.0),
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'confidence': current_confidence,
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'last_prediction': last_prediction,
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'training_samples': training_samples,
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'inference_rate': inference_rate,
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'last_update': datetime.now().strftime('%H:%M:%S')
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'last_update': datetime.now().strftime('%H:%M:%S'),
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# Enhanced metrics
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'last_inference_time': inference_time_str,
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'last_inference_duration': f"{last_inference_duration:.1f}ms",
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'inference_count': inference_count,
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'pivot_price': pivot_price_str,
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'suggested_action': suggested_action,
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'last_training_time': training_time_str,
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'last_training_duration': f"{last_training_duration:.1f}ms",
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'last_training_loss': f"{last_training_loss:.6f}",
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'training_count': training_count
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}
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except Exception as e:
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@ -2795,7 +2848,14 @@ class CleanTradingDashboard:
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'confidence': 0.0,
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'last_prediction': f'Error: {str(e)}',
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'training_samples': 0,
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'inference_rate': '0.00/s'
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'inference_rate': '0.00/s',
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'last_inference_time': 'Error',
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'last_inference_duration': '0.0ms',
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'pivot_price': 'N/A',
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'suggested_action': 'HOLD',
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'last_training_time': 'Error',
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'last_training_duration': '0.0ms',
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'last_training_loss': '0.000000'
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}
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def _get_training_metrics(self) -> Dict:
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@ -2996,29 +3056,27 @@ class CleanTradingDashboard:
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}
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loaded_models['dqn'] = dqn_model_info
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# 2. CNN Model Status - using orchestrator SSOT
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# 2. CNN Model Status - using enhanced CNN adapter data
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cnn_state = model_states.get('cnn', {})
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cnn_timing = get_model_timing_info('CNN')
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cnn_active = True
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# Get latest CNN prediction
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cnn_latest = latest_predictions.get('cnn', {})
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if cnn_latest:
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cnn_action = cnn_latest.get('action', 'PATTERN_ANALYSIS')
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cnn_confidence = cnn_latest.get('confidence', 0.68)
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timestamp_val = cnn_latest.get('timestamp', datetime.now())
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if isinstance(timestamp_val, str):
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cnn_timestamp = timestamp_val
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elif hasattr(timestamp_val, 'strftime'):
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cnn_timestamp = timestamp_val.strftime('%H:%M:%S')
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else:
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cnn_timestamp = datetime.now().strftime('%H:%M:%S')
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cnn_predicted_price = cnn_latest.get('predicted_price', 0)
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else:
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cnn_action = 'PATTERN_ANALYSIS'
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cnn_confidence = 0.68
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cnn_timestamp = datetime.now().strftime('%H:%M:%S')
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cnn_predicted_price = 0
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# Get enhanced CNN panel data with detailed metrics
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cnn_panel_data = self._update_cnn_model_panel()
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cnn_active = cnn_panel_data.get('status') not in ['NOT_AVAILABLE', 'ERROR', 'NOT_LOADED']
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# Use enhanced CNN data for display
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cnn_action = cnn_panel_data.get('suggested_action', 'PATTERN_ANALYSIS')
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cnn_confidence = cnn_panel_data.get('confidence', 0.0)
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cnn_timestamp = cnn_panel_data.get('last_inference_time', 'Never')
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cnn_pivot_price = cnn_panel_data.get('pivot_price', 'N/A')
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# Parse pivot price for prediction
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cnn_predicted_price = 0
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if cnn_pivot_price != 'N/A' and cnn_pivot_price.startswith('$'):
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try:
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cnn_predicted_price = float(cnn_pivot_price[1:]) # Remove $ sign
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except:
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cnn_predicted_price = 0
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cnn_model_info = {
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'active': cnn_active,
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@ -3028,16 +3086,29 @@ class CleanTradingDashboard:
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'action': cnn_action,
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'confidence': cnn_confidence,
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'predicted_price': cnn_predicted_price,
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'type': cnn_latest.get('type', 'cnn_pivot') if cnn_latest else 'cnn_pivot'
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'pivot_price': cnn_pivot_price,
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'type': 'enhanced_cnn_pivot'
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},
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'loss_5ma': cnn_state.get('current_loss'),
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'loss_5ma': float(cnn_panel_data.get('last_training_loss', '0.0').replace('f', '')),
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'initial_loss': cnn_state.get('initial_loss'),
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'best_loss': cnn_state.get('best_loss'),
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'improvement': safe_improvement_calc(
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cnn_state.get('initial_loss'),
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cnn_state.get('current_loss'),
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0.0 # No synthetic default improvement
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float(cnn_panel_data.get('last_training_loss', '0.0').replace('f', '')),
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0.0
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),
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# Enhanced timing metrics
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'enhanced_timing': {
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'last_inference_time': cnn_panel_data.get('last_inference_time', 'Never'),
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'last_inference_duration': cnn_panel_data.get('last_inference_duration', '0.0ms'),
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'inference_count': cnn_panel_data.get('inference_count', 0),
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'inference_rate': cnn_panel_data.get('inference_rate', '0.00/s'),
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'last_training_time': cnn_panel_data.get('last_training_time', 'Never'),
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'last_training_duration': cnn_panel_data.get('last_training_duration', '0.0ms'),
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'training_count': cnn_panel_data.get('training_count', 0),
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'training_samples': cnn_panel_data.get('training_samples', 0)
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},
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'checkpoint_loaded': cnn_state.get('checkpoint_loaded', False),
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'model_type': 'CNN',
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'description': 'Williams Market Structure CNN (Data Bus Input)',
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@ -5832,6 +5903,44 @@ class CleanTradingDashboard:
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logger.error(f"Error getting pivot points for {symbol}: {e}")
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return []
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def _format_cnn_metrics_for_display(self) -> Dict[str, str]:
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"""Format CNN metrics for dashboard display"""
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try:
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cnn_panel_data = self._update_cnn_model_panel()
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# Format the metrics for display
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formatted_metrics = {
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'status': cnn_panel_data.get('status', 'NOT_AVAILABLE'),
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'parameters': '50.0M',
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'last_inference': f"Inf: {cnn_panel_data.get('last_inference_time', 'Never')} ({cnn_panel_data.get('last_inference_duration', '0.0ms')})",
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'last_training': f"Train: {cnn_panel_data.get('last_training_time', 'Never')} ({cnn_panel_data.get('last_training_duration', '0.0ms')})",
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'inference_rate': cnn_panel_data.get('inference_rate', '0.00/s'),
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'training_samples': str(cnn_panel_data.get('training_samples', 0)),
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'current_loss': cnn_panel_data.get('last_training_loss', '0.000000'),
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'suggested_action': cnn_panel_data.get('suggested_action', 'HOLD'),
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'pivot_price': cnn_panel_data.get('pivot_price', 'N/A'),
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'confidence': f"{cnn_panel_data.get('confidence', 0.0):.1%}",
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'prediction_summary': f"{cnn_panel_data.get('suggested_action', 'HOLD')} @ {cnn_panel_data.get('pivot_price', 'N/A')} ({cnn_panel_data.get('confidence', 0.0):.1%})"
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}
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return formatted_metrics
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except Exception as e:
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logger.error(f"Error formatting CNN metrics for display: {e}")
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return {
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'status': 'ERROR',
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'parameters': '0M',
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'last_inference': 'Inf: Error',
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'last_training': 'Train: Error',
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'inference_rate': '0.00/s',
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'training_samples': '0',
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'current_loss': '0.000000',
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'suggested_action': 'HOLD',
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'pivot_price': 'N/A',
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'confidence': '0.0%',
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'prediction_summary': 'Error'
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}
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def _start_cnn_prediction_loop(self):
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"""Start CNN real-time prediction loop"""
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try:
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