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This commit is contained in:
Dobromir Popov
2025-07-28 11:44:01 +03:00
parent 7c508ab536
commit 9219b78241
2 changed files with 27 additions and 8 deletions
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30
core/orchestrator.py
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@ -1204,10 +1204,12 @@ class TradingOrchestrator:
if not self.realtime_processing:
return
try:
# This is where you would feed the state to the DQN model for prediction
# or store them for training. For now, we just log and store the latest.
# self.latest_cob_state[symbol] = cob_data['state']
# logger.debug(f"COB DQN state updated for {symbol}: {cob_data['state'][:5]}...")
# Store the COB state for DQN model access
if 'state' in cob_data and cob_data['state'] is not None:
self.latest_cob_state[symbol] = cob_data['state']
logger.debug(f"COB DQN state updated for {symbol}: shape {np.array(cob_data['state']).shape}")
else:
logger.warning(f"COB data for {symbol} missing 'state' field: {list(cob_data.keys())}")
# If training is enabled, add to training data
if self.training_enabled and self.enhanced_training_system:
@ -1574,10 +1576,12 @@ class TradingOrchestrator:
# Log statistics periodically (every 10 inferences)
stats = self.model_statistics[model_name]
if stats.total_inferences % 10 == 0:
last_prediction_str = stats.last_prediction if stats.last_prediction is not None else "None"
last_confidence_str = f"{stats.last_confidence:.3f}" if stats.last_confidence is not None else "N/A"
logger.debug(f"Model {model_name} stats: {stats.total_inferences} inferences, "
f"{stats.inference_rate_per_minute:.1f}/min, "
f"avg: {stats.average_inference_time_ms:.1f}ms, "
f"last: {stats.last_prediction} ({stats.last_confidence:.3f})")
f"last: {last_prediction_str} ({last_confidence_str})")
except Exception as e:
logger.error(f"Error updating statistics for {model_name}: {e}")
@ -2148,12 +2152,26 @@ class TradingOrchestrator:
best_loss = model_stats.best_loss if model_stats else None
avg_loss = model_stats.average_loss if model_stats else None
# Calculate reward for logging
reward, _ = self._calculate_sophisticated_reward(
predicted_action,
predicted_confidence,
actual_price_change_pct,
time_diff_seconds / 60, # Convert to minutes
has_price_prediction=predicted_price is not None
)
# Enhanced logging with detailed information
logger.info(f"Completed immediate training for {model_name} - {outcome_status}")
logger.info(f" Prediction: {predicted_action} (confidence: {predicted_confidence:.3f})")
logger.info(f" {price_outcome}")
logger.info(f" Reward: {reward:.4f} | Time: {time_diff_seconds:.1f}s")
logger.info(f" Loss: {current_loss:.4f} | Best: {best_loss:.4f} | Avg: {avg_loss:.4f}")
# Safe formatting for loss values
current_loss_str = f"{current_loss:.4f}" if current_loss is not None else "N/A"
best_loss_str = f"{best_loss:.4f}" if best_loss is not None else "N/A"
avg_loss_str = f"{avg_loss:.4f}" if avg_loss is not None else "N/A"
logger.info(f" Loss: {current_loss_str} | Best: {best_loss_str} | Avg: {avg_loss_str}")
logger.info(f" Outcome: {outcome_status}")
# Add performance summary

5
web/layout_manager.py
View File

@ -256,11 +256,12 @@ class DashboardLayoutManager:
return html.Div([
html.Div([
html.Div([
# Chart header with manual trading buttons
# Chart header with manual trading buttons and live price
html.Div([
html.H6([
html.I(className="fas fa-chart-candlestick me-2"),
"Live 1m Price Chart (3h) + 1s Mini Chart (5min) - Updated Every Second"
"Live Price (WebSocket): ",
html.Span(id="current-price", className="ms-2 text-primary", style={"fontWeight": "bold", "fontSize": "1.2em"})
], className="card-title mb-0"),
html.Div([
html.Button([