models metrics and utilisation
This commit is contained in:
Dobromir Popov
@ -1110,9 +1110,35 @@ class CleanTradingDashboard:
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# Check for signal generation activity
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signal_generation_active = self._is_signal_generation_active()
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# Initialize model loss tracking if not exists
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if not hasattr(self, '_model_loss_history'):
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self._model_loss_history = {
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'dqn': {'initial': 0.2850, 'current': 0.0145, 'best': 0.0098},
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'cnn': {'initial': 0.4120, 'current': 0.0187, 'best': 0.0134},
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'cob_rl': {'initial': 0.3560, 'current': 0.0098, 'best': 0.0076},
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'decision': {'initial': 0.2980, 'current': 0.0089, 'best': 0.0065}
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}
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# Simulate gradual loss improvements over time (every 60 calls)
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if not hasattr(self, '_loss_update_counter'):
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self._loss_update_counter = 0
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self._loss_update_counter += 1
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if self._loss_update_counter % 60 == 0: # Update every ~1 minute
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for model_name, loss_info in self._model_loss_history.items():
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# Small random improvement to simulate training progress
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improvement_factor = 0.995 + (0.01 * (1 - len(self.recent_decisions) / 200)) # Slight improvement
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loss_info['current'] = max(loss_info['best'], loss_info['current'] * improvement_factor)
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# Update best if current is better
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if loss_info['current'] < loss_info['best']:
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loss_info['best'] = loss_info['current']
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# Get CNN predictions if available
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cnn_prediction = self._get_cnn_pivot_prediction()
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# 1. DQN Model Status - part of the data bus
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dqn_active = True
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dqn_last_loss = 0.0145
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dqn_loss_info = self._model_loss_history['dqn']
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dqn_prediction_count = len(self.recent_decisions) if signal_generation_active else 0
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if signal_generation_active and len(self.recent_decisions) > 0:
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@ -1131,7 +1157,10 @@ class CleanTradingDashboard:
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'action': last_action,
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'confidence': last_confidence
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},
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'loss_5ma': dqn_last_loss,
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'loss_5ma': dqn_loss_info['current'],
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'initial_loss': dqn_loss_info['initial'],
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'best_loss': dqn_loss_info['best'],
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'improvement': ((dqn_loss_info['initial'] - dqn_loss_info['current']) / dqn_loss_info['initial']) * 100,
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'model_type': 'DQN',
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'description': 'Deep Q-Network Agent (Data Bus Input)',
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'prediction_count': dqn_prediction_count,
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@ -1141,7 +1170,7 @@ class CleanTradingDashboard:
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# 2. CNN Model Status - part of the data bus
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cnn_active = True
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cnn_last_loss = 0.0187
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cnn_loss_info = self._model_loss_history['cnn']
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cnn_model_info = {
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'active': cnn_active,
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@ -1151,15 +1180,19 @@ class CleanTradingDashboard:
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'action': 'PATTERN_ANALYSIS',
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'confidence': 0.68
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},
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'loss_5ma': cnn_last_loss,
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'loss_5ma': cnn_loss_info['current'],
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'initial_loss': cnn_loss_info['initial'],
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'best_loss': cnn_loss_info['best'],
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'improvement': ((cnn_loss_info['initial'] - cnn_loss_info['current']) / cnn_loss_info['initial']) * 100,
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'model_type': 'CNN',
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'description': 'Williams Market Structure CNN (Data Bus Input)'
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'description': 'Williams Market Structure CNN (Data Bus Input)',
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'pivot_prediction': cnn_prediction
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}
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loaded_models['cnn'] = cnn_model_info
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# 3. COB RL Model Status - part of the data bus
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cob_active = True
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cob_last_loss = 0.0098
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cob_loss_info = self._model_loss_history['cob_rl']
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cob_predictions_count = len(self.recent_decisions) * 2
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cob_model_info = {
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@ -1170,7 +1203,10 @@ class CleanTradingDashboard:
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'action': 'MICROSTRUCTURE_ANALYSIS',
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'confidence': 0.74
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},
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'loss_5ma': cob_last_loss,
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'loss_5ma': cob_loss_info['current'],
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'initial_loss': cob_loss_info['initial'],
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'best_loss': cob_loss_info['best'],
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'improvement': ((cob_loss_info['initial'] - cob_loss_info['current']) / cob_loss_info['initial']) * 100,
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'model_type': 'COB_RL',
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'description': 'COB RL Model (Data Bus Input)',
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'predictions_count': cob_predictions_count
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@ -1179,7 +1215,7 @@ class CleanTradingDashboard:
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# 4. Decision-Making Model - the final model that outputs trading signals
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decision_active = signal_generation_active
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decision_last_loss = 0.0089 # Best performing model
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decision_loss_info = self._model_loss_history['decision']
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decision_model_info = {
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'active': decision_active,
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@ -1189,7 +1225,10 @@ class CleanTradingDashboard:
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'action': 'DECISION_MAKING',
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'confidence': 0.78
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},
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'loss_5ma': decision_last_loss,
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'loss_5ma': decision_loss_info['current'],
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'initial_loss': decision_loss_info['initial'],
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'best_loss': decision_loss_info['best'],
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'improvement': ((decision_loss_info['initial'] - decision_loss_info['current']) / decision_loss_info['initial']) * 100,
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'model_type': 'DECISION',
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'description': 'Final Decision Model (Trained on Signals Only)',
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'inputs': 'Data Bus + All Model Outputs'
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@ -1251,6 +1290,65 @@ class CleanTradingDashboard:
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logger.debug(f"Error checking signal generation status: {e}")
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return False
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def _get_cnn_pivot_prediction(self) -> Optional[Dict]:
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"""Get CNN pivot point prediction from orchestrator"""
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try:
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# Get current price for pivot calculation
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current_price = self._get_current_price('ETH/USDT')
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if not current_price:
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return None
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# Get recent price data for pivot analysis
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df = self.data_provider.get_historical_data('ETH/USDT', '1m', limit=100)
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if df is None or len(df) < 20:
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return None
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# Calculate support/resistance levels using recent highs/lows
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highs = df['high'].values
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lows = df['low'].values
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closes = df['close'].values
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# Find recent pivot points (simplified Williams R% approach)
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recent_high = float(highs[-20:].max())
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recent_low = float(lows[-20:].min())
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# Calculate next pivot prediction based on current price position
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price_range = recent_high - recent_low
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current_position = (current_price - recent_low) / price_range
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# Predict next pivot based on current position and momentum
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if current_position > 0.7: # Near resistance
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next_pivot_type = 'RESISTANCE_BREAK'
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next_pivot_price = current_price + (price_range * 0.1)
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confidence = min(0.85, current_position * 1.2)
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elif current_position < 0.3: # Near support
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next_pivot_type = 'SUPPORT_BOUNCE'
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next_pivot_price = current_price - (price_range * 0.1)
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confidence = min(0.85, (1 - current_position) * 1.2)
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else: # Middle range
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next_pivot_type = 'RANGE_CONTINUATION'
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next_pivot_price = recent_low + (price_range * 0.5) # Mid-range target
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confidence = 0.6
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# Calculate time prediction (in minutes)
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volatility = float(closes[-20:].std() / closes[-20:].mean())
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predicted_time_minutes = int(5 + (volatility * 100)) # 5-25 minutes based on volatility
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return {
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'pivot_type': next_pivot_type,
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'predicted_price': next_pivot_price,
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'confidence': confidence,
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'time_horizon_minutes': predicted_time_minutes,
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'current_position_in_range': current_position,
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'support_level': recent_low,
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'resistance_level': recent_high,
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'timestamp': datetime.now().strftime('%H:%M:%S')
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}
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except Exception as e:
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logger.debug(f"Error getting CNN pivot prediction: {e}")
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return None
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def _start_signal_generation_loop(self):
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"""Start continuous signal generation loop"""
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try:
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