pivot points option in UI
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Dobromir Popov
@@ -584,7 +584,6 @@ class TradingOrchestrator:
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return alias_to_canonical.get(name, name)
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except Exception:
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return name
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def _initialize_ml_models(self):
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"""Initialize ML models for enhanced trading"""
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try:
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@@ -738,45 +737,42 @@ class TradingOrchestrator:
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checkpoint_metadata = db_manager.get_best_checkpoint_metadata(
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"enhanced_cnn"
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)
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if checkpoint_metadata:
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self.model_states["cnn"]["initial_loss"] = 0.412
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self.model_states["cnn"]["current_loss"] = (
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checkpoint_metadata.performance_metrics.get("loss", 0.0187)
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)
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self.model_states["cnn"]["best_loss"] = (
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checkpoint_metadata.performance_metrics.get("loss", 0.0134)
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)
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self.model_states["cnn"]["checkpoint_loaded"] = True
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self.model_states["cnn"][
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"checkpoint_filename"
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] = checkpoint_metadata.checkpoint_id
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checkpoint_loaded = True
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loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}"
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logger.info(
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f"CNN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})"
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)
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if checkpoint_metadata and os.path.exists(checkpoint_metadata.file_path):
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try:
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saved = torch.load(checkpoint_metadata.file_path, map_location=self.device)
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if saved and saved.get("model_state_dict"):
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self.cnn_model.load_state_dict(saved["model_state_dict"], strict=False)
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checkpoint_loaded = True
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except Exception as load_ex:
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logger.warning(f"CNN checkpoint load_state_dict failed: {load_ex}")
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if not checkpoint_loaded:
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# Filesystem fallback
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from utils.checkpoint_manager import load_best_checkpoint as _load_best_ckpt
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result = _load_best_ckpt("enhanced_cnn")
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if result:
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ckpt_path, meta = result
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try:
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saved = torch.load(ckpt_path, map_location=self.device)
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if saved and saved.get("model_state_dict"):
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self.cnn_model.load_state_dict(saved["model_state_dict"], strict=False)
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checkpoint_loaded = True
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self.model_states["cnn"]["checkpoint_filename"] = getattr(meta, "checkpoint_id", os.path.basename(ckpt_path))
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except Exception as e_load:
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logger.warning(f"Failed loading CNN weights from {ckpt_path}: {e_load}")
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# Update model_states flags after attempts
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self.model_states["cnn"]["checkpoint_loaded"] = checkpoint_loaded
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except Exception as e:
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logger.warning(f"Error loading CNN checkpoint: {e}")
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# Filesystem fallback
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try:
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from utils.checkpoint_manager import get_checkpoint_manager
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cm = get_checkpoint_manager()
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result = cm.load_best_checkpoint("enhanced_cnn")
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if result:
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model_path, meta = result
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self.model_states["cnn"]["checkpoint_loaded"] = True
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self.model_states["cnn"]["checkpoint_filename"] = getattr(meta, 'checkpoint_id', None)
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checkpoint_loaded = True
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logger.info(f"CNN checkpoint (fs) detected: {getattr(meta, 'checkpoint_id', 'unknown')}")
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except Exception:
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pass
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checkpoint_loaded = False
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if not checkpoint_loaded:
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# New model - no synthetic data
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self.model_states["cnn"]["initial_loss"] = None
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self.model_states["cnn"]["current_loss"] = None
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self.model_states["cnn"]["best_loss"] = None
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logger.info("CNN starting fresh - no checkpoint found")
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self.model_states["cnn"]["checkpoint_loaded"] = False
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logger.info("CNN starting fresh - no checkpoint found or failed to load")
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else:
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logger.info("CNN weights loaded from checkpoint successfully")
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logger.info("Enhanced CNN model initialized directly")
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except ImportError:
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@@ -1339,7 +1335,6 @@ class TradingOrchestrator:
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}
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return stats
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def clear_session_data(self):
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"""Clear all session-related data for fresh start"""
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try:
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@@ -2122,7 +2117,6 @@ class TradingOrchestrator:
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except Exception as e:
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logger.error(f"Error registering model {model.name}: {e}")
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return False
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def unregister_model(self, model_name: str) -> bool:
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"""Unregister a model"""
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try:
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@@ -3540,7 +3534,6 @@ class TradingOrchestrator:
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except Exception as e:
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logger.error(f"Error in immediate training for {model_name}: {e}")
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async def _evaluate_and_train_on_record(self, record: Dict, current_price: float):
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"""Evaluate prediction outcome and train model"""
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try:
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@@ -5779,7 +5772,6 @@ class TradingOrchestrator:
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if symbol in self.recent_decisions:
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return self.recent_decisions[symbol][-limit:]
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return []
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def get_performance_metrics(self) -> Dict[str, Any]:
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"""Get performance metrics for the orchestrator"""
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return {
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@@ -6579,7 +6571,6 @@ class TradingOrchestrator:
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except Exception as e:
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logger.error(f"Error adding decision fusion training sample: {e}")
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def _train_decision_fusion_network(self):
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"""Train the decision fusion network on collected data"""
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try:
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@@ -8133,7 +8124,6 @@ class TradingOrchestrator:
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except Exception as e:
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logger.error(f"Error initializing checkpoint manager: {e}")
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self.checkpoint_manager = None
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def autosave_models(self):
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"""Attempt to autosave best model checkpoints periodically."""
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try:
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@@ -8990,4 +8980,4 @@ class TradingOrchestrator:
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except Exception as e:
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logger.error(f"Error in fallback data strategy: {e}")
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return False
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return False
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