popov/gogo2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

UI: fix models info

Browse Source
This commit is contained in:
Dobromir Popov
2025-07-29 00:46:16 +03:00
parent aefc460082
commit b44216ae1e
3 changed files with 58 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
config.yaml
View File

@ -50,7 +50,7 @@ exchanges:
bybit:
enabled: true
test_mode: false # Use mainnet (your credentials are for live trading)
trading_mode: "live" # simulation, testnet, live
trading_mode: "simulation" # simulation, testnet, live
supported_symbols: ["BTCUSDT", "ETHUSDT"] # Bybit perpetual format
base_position_percent: 5.0
max_position_percent: 20.0

43
web/clean_dashboard.py
View File

@ -1137,6 +1137,8 @@ class CleanTradingDashboard:
[Input('slow-interval-component', 'n_intervals')] # OPTIMIZED: Move to 10s interval
)
def update_training_metrics(n):
"""Update training metrics"""
try:
# Get toggle states from orchestrator
toggle_states = {}
if self.orchestrator:
@ -1150,11 +1152,14 @@ class CleanTradingDashboard:
"cob_rl": {"inference_enabled": True, "training_enabled": True},
"decision_fusion": {"inference_enabled": True, "training_enabled": True}
}
"""Update training metrics"""
try:
# Now using slow-interval-component (10s) - no batching needed
metrics_data = self._get_training_metrics()
metrics_data = self._get_training_metrics(toggle_states)
logger.debug(f"update_training_metrics callback: got metrics_data type={type(metrics_data)}")
if metrics_data and isinstance(metrics_data, dict):
logger.debug(f"Metrics data keys: {list(metrics_data.keys())}")
if 'loaded_models' in metrics_data:
logger.debug(f"Loaded models count: {len(metrics_data['loaded_models'])}")
return self.component_manager.format_training_metrics(metrics_data)
except PreventUpdate:
raise
@ -3291,7 +3296,7 @@ class CleanTradingDashboard:
'last_training_loss': '0.000000'
}
def _get_training_metrics(self) -> Dict:
def _get_training_metrics(self, toggle_states: Dict = None) -> Dict:
"""Get training metrics from unified orchestrator - using orchestrator as SSOT"""
try:
metrics = {}
@ -3305,12 +3310,14 @@ class CleanTradingDashboard:
if self.orchestrator and hasattr(self.orchestrator, 'get_model_states'):
try:
model_states = self.orchestrator.get_model_states()
logger.debug(f"Retrieved model states from orchestrator: {model_states}")
except Exception as e:
logger.debug(f"Error getting model states from orchestrator: {e}")
logger.error(f"Error getting model states from orchestrator: {e}")
model_states = None
# Fallback if orchestrator not available or returns None
if model_states is None:
logger.warning("No model states available from orchestrator, using fallback")
# FIXED: No longer using hardcoded placeholder loss values
# Dashboard should show "No Data" or actual training status instead
model_states = {
@ -3340,6 +3347,23 @@ class CleanTradingDashboard:
# Get enhanced training statistics if available
enhanced_training_stats = self._get_enhanced_training_stats()
# DEBUG: Check if orchestrator has model statistics
if self.orchestrator:
try:
all_stats = self.orchestrator.get_model_statistics()
logger.debug(f"Orchestrator model statistics: {all_stats}")
except Exception as e:
logger.debug(f"Error getting orchestrator model statistics: {e}")
# Ensure toggle_states are available
if toggle_states is None:
toggle_states = {
"dqn": {"inference_enabled": True, "training_enabled": True},
"cnn": {"inference_enabled": True, "training_enabled": True},
"cob_rl": {"inference_enabled": True, "training_enabled": True},
"decision_fusion": {"inference_enabled": True, "training_enabled": True}
}
# Helper function to safely calculate improvement percentage
def safe_improvement_calc(initial, current, default_improvement=0.0):
try:
@ -3844,6 +3868,15 @@ class CleanTradingDashboard:
# Add enhanced training statistics
metrics['enhanced_training_stats'] = enhanced_training_stats
# DEBUG: Log what we're returning
models_count = len(metrics.get('loaded_models', {}))
logger.info(f"Training metrics being returned: {models_count} models loaded")
if models_count == 0:
logger.warning("No models in loaded_models!")
logger.warning(f"Metrics keys: {list(metrics.keys())}")
for model_name, model_info in metrics.get('loaded_models', {}).items():
logger.info(f"Model {model_name}: active={model_info.get('active', False)}, checkpoint_loaded={model_info.get('checkpoint_loaded', False)}")
return metrics
except Exception as e:

8
web/component_manager.py
View File

@ -713,7 +713,15 @@ class DashboardComponentManager:
def format_training_metrics(self, metrics_data):
"""Format training metrics for display - Enhanced with loaded models"""
try:
# DEBUG: Log what we're receiving
logger.debug(f"format_training_metrics received: {type(metrics_data)}")
if metrics_data:
logger.debug(f"Metrics keys: {list(metrics_data.keys()) if isinstance(metrics_data, dict) else 'Not a dict'}")
if isinstance(metrics_data, dict) and 'loaded_models' in metrics_data:
logger.debug(f"Loaded models: {list(metrics_data['loaded_models'].keys())}")
if not metrics_data or 'error' in metrics_data:
logger.warning(f"No training data or error in metrics_data: {metrics_data}")
return [html.P("No training data", className="text-muted small")]
content = []