better CNN info in the dash
This commit is contained in:
Dobromir Popov
@ -31,6 +31,7 @@ from .extrema_trainer import ExtremaTrainer
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from .trading_action import TradingAction
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from .negative_case_trainer import NegativeCaseTrainer
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from .trading_executor import TradingExecutor
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from .cnn_monitor import log_cnn_prediction, start_cnn_training_session
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# Enhanced pivot RL trainer functionality integrated into orchestrator
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logger = logging.getLogger(__name__)
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@ -790,19 +791,145 @@ class EnhancedTradingOrchestrator:
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async def _get_timeframe_prediction_universal(self, model: CNNModelInterface, feature_matrix: np.ndarray,
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timeframe: str, market_state: MarketState,
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universal_stream: UniversalDataStream) -> Tuple[Optional[np.ndarray], float]:
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"""Get prediction for specific timeframe using universal data format"""
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"""Get prediction for specific timeframe using universal data format with CNN monitoring"""
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try:
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# Check if model supports timeframe-specific prediction
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# Measure prediction timing
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prediction_start_time = time.time()
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# Get current price for context
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current_price = market_state.prices.get(timeframe)
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# Check if model supports timeframe-specific prediction or enhanced predict method
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if hasattr(model, 'predict_timeframe'):
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action_probs, confidence = model.predict_timeframe(feature_matrix, timeframe)
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elif hasattr(model, 'predict') and hasattr(model.predict, '__call__'):
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# Enhanced CNN model with detailed output
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if hasattr(model, 'enhanced_predict'):
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# Get detailed prediction results
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prediction_result = model.enhanced_predict(feature_matrix)
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action_probs = prediction_result.get('probabilities', [])
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confidence = prediction_result.get('confidence', 0.0)
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else:
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# Standard prediction
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prediction_result = model.predict(feature_matrix)
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if isinstance(prediction_result, dict):
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action_probs = prediction_result.get('probabilities', [])
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confidence = prediction_result.get('confidence', 0.0)
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else:
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action_probs, confidence = prediction_result
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else:
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action_probs, confidence = model.predict(feature_matrix)
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# Calculate prediction latency
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prediction_latency_ms = (time.time() - prediction_start_time) * 1000
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if action_probs is not None and confidence is not None:
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# Enhance confidence based on universal data quality and market conditions
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enhanced_confidence = self._enhance_confidence_with_universal_context(
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confidence, timeframe, market_state, universal_stream
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)
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# Log detailed CNN prediction for monitoring
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try:
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# Convert probabilities to list if needed
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if hasattr(action_probs, 'tolist'):
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prob_list = action_probs.tolist()
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elif isinstance(action_probs, (list, tuple)):
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prob_list = list(action_probs)
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else:
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prob_list = [float(action_probs)]
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# Determine action and action confidence
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if len(prob_list) >= 2:
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action_idx = np.argmax(prob_list)
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action_name = ['SELL', 'BUY'][action_idx] if len(prob_list) == 2 else ['SELL', 'HOLD', 'BUY'][action_idx]
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action_confidence = prob_list[action_idx]
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else:
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action_idx = 0
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action_name = 'HOLD'
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action_confidence = enhanced_confidence
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# Get model memory usage if available
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model_memory_mb = None
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if hasattr(model, 'get_memory_usage'):
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try:
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memory_info = model.get_memory_usage()
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if isinstance(memory_info, dict):
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model_memory_mb = memory_info.get('total_size_mb', 0.0)
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else:
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model_memory_mb = float(memory_info)
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except:
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pass
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# Create detailed prediction result for monitoring
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detailed_prediction = {
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'action': action_idx,
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'action_name': action_name,
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'confidence': float(enhanced_confidence),
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'action_confidence': float(action_confidence),
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'probabilities': prob_list,
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'raw_logits': prob_list # Use probabilities as proxy for logits if not available
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}
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# Add enhanced model outputs if available
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if hasattr(model, 'enhanced_predict') and isinstance(prediction_result, dict):
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detailed_prediction.update({
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'regime_probabilities': prediction_result.get('regime_probabilities'),
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'volatility_prediction': prediction_result.get('volatility_prediction'),
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'extrema_prediction': prediction_result.get('extrema_prediction'),
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'risk_assessment': prediction_result.get('risk_assessment')
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})
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# Calculate price changes for context
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price_change_1m = None
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price_change_5m = None
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volume_ratio = None
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if current_price and timeframe in market_state.prices:
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# Try to get historical prices for context
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try:
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# Get 1m and 5m price changes if available
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if '1m' in market_state.prices and market_state.prices['1m'] != current_price:
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price_change_1m = (current_price - market_state.prices['1m']) / market_state.prices['1m']
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if '5m' in market_state.prices and market_state.prices['5m'] != current_price:
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price_change_5m = (current_price - market_state.prices['5m']) / market_state.prices['5m']
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# Volume ratio (current vs average)
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volume_ratio = market_state.volume
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except:
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pass
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# Log the CNN prediction with full context
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log_cnn_prediction(
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model_name=getattr(model, 'name', model.__class__.__name__),
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symbol=market_state.symbol,
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prediction_result=detailed_prediction,
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feature_matrix_shape=feature_matrix.shape,
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current_price=current_price,
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prediction_latency_ms=prediction_latency_ms,
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model_memory_usage_mb=model_memory_mb
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)
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# Enhanced logging for detailed analysis
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logger.info(f"CNN [{getattr(model, 'name', 'Unknown')}] {market_state.symbol} {timeframe}: "
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f"{action_name} (conf: {enhanced_confidence:.3f}, "
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f"action_conf: {action_confidence:.3f}, "
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f"latency: {prediction_latency_ms:.1f}ms)")
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if detailed_prediction.get('regime_probabilities'):
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regime_idx = np.argmax(detailed_prediction['regime_probabilities'])
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regime_conf = detailed_prediction['regime_probabilities'][regime_idx]
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logger.info(f" Regime: {regime_idx} (conf: {regime_conf:.3f})")
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if detailed_prediction.get('volatility_prediction') is not None:
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logger.info(f" Volatility: {detailed_prediction['volatility_prediction']:.3f}")
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if price_change_1m is not None:
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logger.info(f" Context: 1m_change: {price_change_1m:.4f}, volume_ratio: {volume_ratio:.2f}")
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except Exception as e:
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logger.warning(f"Error logging CNN prediction details: {e}")
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return action_probs, enhanced_confidence
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except Exception as e:
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