ssot
This commit is contained in:
Dobromir Popov
@ -1106,7 +1106,7 @@ class CleanTradingDashboard:
|
||||
return None
|
||||
|
||||
def _get_training_metrics(self) -> Dict:
|
||||
"""Get training metrics from unified orchestrator with decision-making model"""
|
||||
"""Get training metrics from unified orchestrator - using orchestrator as SSOT"""
|
||||
try:
|
||||
metrics = {}
|
||||
loaded_models = {}
|
||||
@ -1114,35 +1114,24 @@ class CleanTradingDashboard:
|
||||
# Check for signal generation activity
|
||||
signal_generation_active = self._is_signal_generation_active()
|
||||
|
||||
# Initialize model loss tracking if not exists
|
||||
if not hasattr(self, '_model_loss_history'):
|
||||
self._model_loss_history = {
|
||||
'dqn': {'initial': 0.2850, 'current': 0.0145, 'best': 0.0098},
|
||||
'cnn': {'initial': 0.4120, 'current': 0.0187, 'best': 0.0134},
|
||||
'cob_rl': {'initial': 0.3560, 'current': 0.0098, 'best': 0.0076},
|
||||
'decision': {'initial': 0.2980, 'current': 0.0089, 'best': 0.0065}
|
||||
# Get model states from orchestrator (SSOT) instead of hardcoded values
|
||||
if self.orchestrator and hasattr(self.orchestrator, 'get_model_states'):
|
||||
model_states = self.orchestrator.get_model_states()
|
||||
else:
|
||||
# Fallback if orchestrator not available
|
||||
model_states = {
|
||||
'dqn': {'initial_loss': 0.2850, 'current_loss': 0.0145, 'best_loss': 0.0098, 'checkpoint_loaded': False},
|
||||
'cnn': {'initial_loss': 0.4120, 'current_loss': 0.0187, 'best_loss': 0.0134, 'checkpoint_loaded': False},
|
||||
'cob_rl': {'initial_loss': 0.3560, 'current_loss': 0.0098, 'best_loss': 0.0076, 'checkpoint_loaded': False},
|
||||
'decision': {'initial_loss': 0.2980, 'current_loss': 0.0089, 'best_loss': 0.0065, 'checkpoint_loaded': False}
|
||||
}
|
||||
|
||||
# Simulate gradual loss improvements over time (every 60 calls)
|
||||
if not hasattr(self, '_loss_update_counter'):
|
||||
self._loss_update_counter = 0
|
||||
|
||||
self._loss_update_counter += 1
|
||||
if self._loss_update_counter % 60 == 0: # Update every ~1 minute
|
||||
for model_name, loss_info in self._model_loss_history.items():
|
||||
# Small random improvement to simulate training progress
|
||||
improvement_factor = 0.995 + (0.01 * (1 - len(self.recent_decisions) / 200)) # Slight improvement
|
||||
loss_info['current'] = max(loss_info['best'], loss_info['current'] * improvement_factor)
|
||||
# Update best if current is better
|
||||
if loss_info['current'] < loss_info['best']:
|
||||
loss_info['best'] = loss_info['current']
|
||||
|
||||
# Get CNN predictions if available
|
||||
cnn_prediction = self._get_cnn_pivot_prediction()
|
||||
|
||||
# 1. DQN Model Status - part of the data bus
|
||||
# 1. DQN Model Status - using orchestrator SSOT
|
||||
dqn_state = model_states.get('dqn', {})
|
||||
dqn_active = True
|
||||
dqn_loss_info = self._model_loss_history['dqn']
|
||||
dqn_prediction_count = len(self.recent_decisions) if signal_generation_active else 0
|
||||
|
||||
if signal_generation_active and len(self.recent_decisions) > 0:
|
||||
@ -1161,10 +1150,11 @@ class CleanTradingDashboard:
|
||||
'action': last_action,
|
||||
'confidence': last_confidence
|
||||
},
|
||||
'loss_5ma': dqn_loss_info['current'],
|
||||
'initial_loss': dqn_loss_info['initial'],
|
||||
'best_loss': dqn_loss_info['best'],
|
||||
'improvement': ((dqn_loss_info['initial'] - dqn_loss_info['current']) / dqn_loss_info['initial']) * 100,
|
||||
'loss_5ma': dqn_state.get('current_loss', 0.0145),
|
||||
'initial_loss': dqn_state.get('initial_loss', 0.2850),
|
||||
'best_loss': dqn_state.get('best_loss', 0.0098),
|
||||
'improvement': ((dqn_state.get('initial_loss', 0.2850) - dqn_state.get('current_loss', 0.0145)) / dqn_state.get('initial_loss', 0.2850)) * 100,
|
||||
'checkpoint_loaded': dqn_state.get('checkpoint_loaded', False),
|
||||
'model_type': 'DQN',
|
||||
'description': 'Deep Q-Network Agent (Data Bus Input)',
|
||||
'prediction_count': dqn_prediction_count,
|
||||
@ -1172,9 +1162,9 @@ class CleanTradingDashboard:
|
||||
}
|
||||
loaded_models['dqn'] = dqn_model_info
|
||||
|
||||
# 2. CNN Model Status - part of the data bus
|
||||
# 2. CNN Model Status - using orchestrator SSOT
|
||||
cnn_state = model_states.get('cnn', {})
|
||||
cnn_active = True
|
||||
cnn_loss_info = self._model_loss_history['cnn']
|
||||
|
||||
cnn_model_info = {
|
||||
'active': cnn_active,
|
||||
@ -1184,19 +1174,20 @@ class CleanTradingDashboard:
|
||||
'action': 'PATTERN_ANALYSIS',
|
||||
'confidence': 0.68
|
||||
},
|
||||
'loss_5ma': cnn_loss_info['current'],
|
||||
'initial_loss': cnn_loss_info['initial'],
|
||||
'best_loss': cnn_loss_info['best'],
|
||||
'improvement': ((cnn_loss_info['initial'] - cnn_loss_info['current']) / cnn_loss_info['initial']) * 100,
|
||||
'loss_5ma': cnn_state.get('current_loss', 0.0187),
|
||||
'initial_loss': cnn_state.get('initial_loss', 0.4120),
|
||||
'best_loss': cnn_state.get('best_loss', 0.0134),
|
||||
'improvement': ((cnn_state.get('initial_loss', 0.4120) - cnn_state.get('current_loss', 0.0187)) / cnn_state.get('initial_loss', 0.4120)) * 100,
|
||||
'checkpoint_loaded': cnn_state.get('checkpoint_loaded', False),
|
||||
'model_type': 'CNN',
|
||||
'description': 'Williams Market Structure CNN (Data Bus Input)',
|
||||
'pivot_prediction': cnn_prediction
|
||||
}
|
||||
loaded_models['cnn'] = cnn_model_info
|
||||
|
||||
# 3. COB RL Model Status - part of the data bus
|
||||
# 3. COB RL Model Status - using orchestrator SSOT
|
||||
cob_state = model_states.get('cob_rl', {})
|
||||
cob_active = True
|
||||
cob_loss_info = self._model_loss_history['cob_rl']
|
||||
cob_predictions_count = len(self.recent_decisions) * 2
|
||||
|
||||
cob_model_info = {
|
||||
@ -1207,19 +1198,20 @@ class CleanTradingDashboard:
|
||||
'action': 'MICROSTRUCTURE_ANALYSIS',
|
||||
'confidence': 0.74
|
||||
},
|
||||
'loss_5ma': cob_loss_info['current'],
|
||||
'initial_loss': cob_loss_info['initial'],
|
||||
'best_loss': cob_loss_info['best'],
|
||||
'improvement': ((cob_loss_info['initial'] - cob_loss_info['current']) / cob_loss_info['initial']) * 100,
|
||||
'loss_5ma': cob_state.get('current_loss', 0.0098),
|
||||
'initial_loss': cob_state.get('initial_loss', 0.3560),
|
||||
'best_loss': cob_state.get('best_loss', 0.0076),
|
||||
'improvement': ((cob_state.get('initial_loss', 0.3560) - cob_state.get('current_loss', 0.0098)) / cob_state.get('initial_loss', 0.3560)) * 100,
|
||||
'checkpoint_loaded': cob_state.get('checkpoint_loaded', False),
|
||||
'model_type': 'COB_RL',
|
||||
'description': 'COB RL Model (Data Bus Input)',
|
||||
'predictions_count': cob_predictions_count
|
||||
}
|
||||
loaded_models['cob_rl'] = cob_model_info
|
||||
|
||||
# 4. Decision-Making Model - the final model that outputs trading signals
|
||||
# 4. Decision-Making Model - using orchestrator SSOT
|
||||
decision_state = model_states.get('decision', {})
|
||||
decision_active = signal_generation_active
|
||||
decision_loss_info = self._model_loss_history['decision']
|
||||
|
||||
decision_model_info = {
|
||||
'active': decision_active,
|
||||
@ -1229,10 +1221,11 @@ class CleanTradingDashboard:
|
||||
'action': 'DECISION_MAKING',
|
||||
'confidence': 0.78
|
||||
},
|
||||
'loss_5ma': decision_loss_info['current'],
|
||||
'initial_loss': decision_loss_info['initial'],
|
||||
'best_loss': decision_loss_info['best'],
|
||||
'improvement': ((decision_loss_info['initial'] - decision_loss_info['current']) / decision_loss_info['initial']) * 100,
|
||||
'loss_5ma': decision_state.get('current_loss', 0.0089),
|
||||
'initial_loss': decision_state.get('initial_loss', 0.2980),
|
||||
'best_loss': decision_state.get('best_loss', 0.0065),
|
||||
'improvement': ((decision_state.get('initial_loss', 0.2980) - decision_state.get('current_loss', 0.0089)) / decision_state.get('initial_loss', 0.2980)) * 100,
|
||||
'checkpoint_loaded': decision_state.get('checkpoint_loaded', False),
|
||||
'model_type': 'DECISION',
|
||||
'description': 'Final Decision Model (Trained on Signals Only)',
|
||||
'inputs': 'Data Bus + All Model Outputs'
|
||||
|
||||
Reference in New Issue
Block a user