PnL in reward, show leveraged power in dash (broken)
This commit is contained in:
Dobromir Popov
@ -964,6 +964,7 @@ class CleanTradingDashboard:
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)
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def update_metrics(n):
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"""Update key metrics - ENHANCED with position sync monitoring"""
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logger.debug(f"update_metrics callback triggered (n={n})")
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try:
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# PERIODIC POSITION SYNC: Every 30 seconds, verify position sync
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if n % 30 == 0 and n > 0: # Skip initial load (n=0)
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@ -1102,7 +1103,14 @@ class CleanTradingDashboard:
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# For simulation, show starting balance + session P&L
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current_balance = self._cached_live_balance if hasattr(self, '_cached_live_balance') else self._get_initial_balance()
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portfolio_value = current_balance + total_session_pnl # Live balance + unrealized P&L
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portfolio_str = f"${portfolio_value:.2f}"
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# Add max position info to portfolio display
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try:
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max_position_info = self._calculate_max_position_display()
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portfolio_str = f"${portfolio_value:.2f} | {max_position_info}"
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except Exception as e:
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logger.error(f"Error calculating max position display: {e}")
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portfolio_str = f"${portfolio_value:.2f}"
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# Profitability multiplier - get from trading executor
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profitability_multiplier = 0.0
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@ -1352,6 +1360,11 @@ class CleanTradingDashboard:
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logger.debug(f"Metrics data keys: {list(metrics_data.keys())}")
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if 'loaded_models' in metrics_data:
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logger.debug(f"Loaded models count: {len(metrics_data['loaded_models'])}")
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logger.debug(f"Loaded model names: {list(metrics_data['loaded_models'].keys())}")
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else:
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logger.warning("No 'loaded_models' key in metrics_data!")
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else:
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logger.warning(f"Invalid metrics_data: {metrics_data}")
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return self.component_manager.format_training_metrics(metrics_data)
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except PreventUpdate:
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raise
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@ -1646,6 +1659,38 @@ class CleanTradingDashboard:
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logger.debug(f"Error calculating opening fee: {e}")
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return position_size_usd * 0.0006 # Fallback to 0.06%
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def _calculate_max_position_display(self) -> str:
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"""Calculate and display maximum position size based on current balance and leverage"""
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try:
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# Get current balance
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current_balance = self._get_live_account_balance()
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if current_balance <= 0:
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return "No Balance"
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# Get current leverage
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leverage = getattr(self, 'current_leverage', 50) # Default to 50x
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# Get current price for ETH/USDT
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current_price = self._get_current_price('ETH/USDT')
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if not current_price or current_price <= 0:
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return "Price N/A"
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# Calculate maximum position value (balance * leverage)
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max_position_value = current_balance * leverage
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# Calculate maximum ETH quantity
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max_eth_quantity = max_position_value / current_price
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# Format display
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if max_eth_quantity >= 0.01: # Show in ETH if >= 0.01
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return f"${max_position_value:.1f} ({max_eth_quantity:.2f} ETH)"
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else:
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return f"${max_position_value:.1f} ({max_eth_quantity:.4f} ETH)"
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except Exception as e:
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logger.debug(f"Error calculating max position display: {e}")
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return "Calc Error"
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def _calculate_closing_fee(self, current_price: float, quantity: float) -> float:
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"""Calculate closing fee for a position at current price"""
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try:
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@ -3532,11 +3577,22 @@ class CleanTradingDashboard:
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if self.orchestrator and hasattr(self.orchestrator, 'get_model_states'):
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try:
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model_states = self.orchestrator.get_model_states()
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logger.debug(f"Retrieved model states from orchestrator: {model_states}")
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logger.debug(f"Retrieved model states from orchestrator: {list(model_states.keys()) if model_states else 'None'}")
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except Exception as e:
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logger.error(f"Error getting model states from orchestrator: {e}")
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model_states = None
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# Also try to get orchestrator statistics for debugging
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if self.orchestrator:
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try:
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all_stats = self.orchestrator.get_model_statistics()
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if all_stats:
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logger.debug(f"Available orchestrator statistics: {list(all_stats.keys())}")
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else:
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logger.debug("No orchestrator statistics available")
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except Exception as e:
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logger.debug(f"Error getting orchestrator statistics: {e}")
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# Fallback if orchestrator not available or returns None
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if model_states is None:
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logger.warning("No model states available from orchestrator, using fallback")
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@ -3549,6 +3605,26 @@ class CleanTradingDashboard:
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'decision': {'initial_loss': None, 'current_loss': None, 'best_loss': None, 'checkpoint_loaded': False}
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}
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# Create mapping for model states to handle both old and new model names
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if model_states and self.orchestrator:
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# Map new registry names to old dashboard names for compatibility
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registry_to_dashboard_mapping = {
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'dqn_agent': 'dqn',
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'enhanced_cnn': 'cnn',
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'cob_rl_model': 'cob_rl',
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'decision_fusion': 'decision_fusion',
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'transformer': 'transformer'
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}
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# Copy states from new names to old names if they exist
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for registry_name, dashboard_name in registry_to_dashboard_mapping.items():
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if registry_name in model_states and dashboard_name not in model_states:
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model_states[dashboard_name] = model_states[registry_name]
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logger.debug(f"Mapped model state {registry_name} -> {dashboard_name}")
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elif dashboard_name not in model_states:
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# Ensure we have a state for the dashboard name
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model_states[dashboard_name] = {'initial_loss': None, 'current_loss': None, 'best_loss': None, 'checkpoint_loaded': False}
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# Get latest predictions from all models
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latest_predictions = self._get_latest_model_predictions()
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cnn_prediction = self._get_cnn_pivot_prediction()
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@ -3598,6 +3674,23 @@ class CleanTradingDashboard:
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"transformer": {"inference_enabled": True, "training_enabled": True}
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}
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# Create mapping for backward compatibility between old dashboard names and new registry names
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model_name_mapping = {
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'dqn': 'dqn_agent',
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'cnn': 'enhanced_cnn',
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'cob_rl': 'cob_rl_model',
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'decision_fusion': 'decision_fusion',
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'transformer': 'transformer'
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}
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# Ensure we have toggle states for the old names used by the dashboard
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for old_name, new_name in model_name_mapping.items():
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if old_name not in toggle_states and new_name in toggle_states:
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toggle_states[old_name] = toggle_states[new_name]
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elif old_name not in toggle_states:
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# Default state if neither old nor new name exists
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toggle_states[old_name] = {"inference_enabled": True, "training_enabled": True}
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# Helper function to safely calculate improvement percentage
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def safe_improvement_calc(initial, current, default_improvement=0.0):
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try:
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@ -3705,8 +3798,8 @@ class CleanTradingDashboard:
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last_confidence = 0.68
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last_timestamp = datetime.now().strftime('%H:%M:%S')
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# Get real DQN statistics from orchestrator
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dqn_stats = orchestrator_stats.get('dqn_agent')
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# Get real DQN statistics from orchestrator (try both old and new names)
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dqn_stats = orchestrator_stats.get('dqn_agent') or orchestrator_stats.get('dqn')
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dqn_current_loss = dqn_stats.current_loss if dqn_stats else None
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dqn_best_loss = dqn_stats.best_loss if dqn_stats else None
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dqn_accuracy = dqn_stats.accuracy if dqn_stats else None
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@ -3786,8 +3879,8 @@ class CleanTradingDashboard:
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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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# Get real CNN statistics from orchestrator
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cnn_stats = orchestrator_stats.get('enhanced_cnn')
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# Get real CNN statistics from orchestrator (try both old and new names)
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cnn_stats = orchestrator_stats.get('enhanced_cnn') or orchestrator_stats.get('cnn')
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cnn_active = cnn_stats is not None
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# Get latest CNN prediction from orchestrator statistics
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@ -4153,12 +4246,15 @@ class CleanTradingDashboard:
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# DEBUG: Log what we're returning
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models_count = len(metrics.get('loaded_models', {}))
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logger.info(f"Training metrics being returned: {models_count} models loaded")
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logger.debug(f"Training metrics being returned: {models_count} models loaded")
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if models_count == 0:
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logger.warning("No models in loaded_models!")
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logger.warning(f"Metrics keys: {list(metrics.keys())}")
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for model_name, model_info in metrics.get('loaded_models', {}).items():
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logger.info(f"Model {model_name}: active={model_info.get('active', False)}, checkpoint_loaded={model_info.get('checkpoint_loaded', False)}")
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logger.warning(f"Model states available: {list(model_states.keys()) if model_states else 'None'}")
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logger.warning(f"Toggle states available: {list(toggle_states.keys()) if toggle_states else 'None'}")
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else:
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for model_name, model_info in metrics.get('loaded_models', {}).items():
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logger.debug(f"Model {model_name}: active={model_info.get('active', False)}, checkpoint_loaded={model_info.get('checkpoint_loaded', False)}")
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return metrics
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