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streamline logging. fixes

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This commit is contained in:
Dobromir Popov
2025-06-25 13:45:18 +03:00
parent ad76b70788
commit 9bbc93c4ea
5 changed files with 214 additions and 235 deletions
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6
core/enhanced_orchestrator.py
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@ -194,7 +194,7 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
self.neural_fusion.register_model("dqn_agent", "RL", "action")
self.neural_fusion.register_model("cob_rl", "COB_RL", "direction")
logger.info("✅ Neural Decision Fusion initialized - NN-driven trading active")
logger.info("Neural Decision Fusion initialized - NN-driven trading active")
# Initialize COB Integration for real-time market microstructure
# PROPERLY INITIALIZED: Create the COB integration instance synchronously
@ -381,7 +381,7 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
self.neural_fusion.register_model("dqn_agent", "RL", "action")
self.neural_fusion.register_model("cob_rl", "COB_RL", "direction")
logger.info("✅ Neural Decision Fusion initialized - NN-driven trading active")
logger.info("Neural Decision Fusion initialized - NN-driven trading active")
def _initialize_timeframe_weights(self) -> Dict[str, float]:
"""Initialize weights for different timeframes"""
@ -460,7 +460,7 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
decisions.append(action)
logger.info(f"🧠 NN DECISION: {symbol} {fusion_decision.action} "
logger.info(f"NN DECISION: {symbol} {fusion_decision.action} "
f"(conf: {fusion_decision.confidence:.3f}, "
f"size: {fusion_decision.position_size:.4f})")
logger.info(f" Reasoning: {fusion_decision.reasoning}")

2
core/nn_decision_fusion.py
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@ -94,7 +94,7 @@ class NeuralDecisionFusion:
self.registered_models = {}
self.last_predictions = {}
logger.info(f"🧠 Neural Decision Fusion initialized on {self.device}")
logger.info(f"Neural Decision Fusion initialized on {self.device}")
def register_model(self, model_name: str, model_type: str, prediction_format: str):
"""Register a model that will provide predictions"""

2
core/orchestrator.py
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@ -160,7 +160,7 @@ class TradingOrchestrator:
predictions = await self._get_all_predictions(symbol)
if not predictions:
logger.warning(f"No predictions available for {symbol}")
logger.debug(f"No predictions available for {symbol}")
return None
# Combine predictions