remove dummy data, improve training , follow architecture
This commit is contained in:
Dobromir Popov
@ -451,7 +451,13 @@ class DQNAgent:
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state_tensor = state.unsqueeze(0).to(self.device)
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# Get Q-values
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q_values = self.policy_net(state_tensor)
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policy_output = self.policy_net(state_tensor)
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if isinstance(policy_output, dict):
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q_values = policy_output.get('q_values', policy_output.get('Q_values', list(policy_output.values())[0]))
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elif isinstance(policy_output, tuple):
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q_values = policy_output[0] # Assume first element is Q-values
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else:
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q_values = policy_output
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action_values = q_values.cpu().data.numpy()[0]
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# Calculate confidence scores
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@ -161,7 +161,7 @@ class OrderBookEncoder(nn.Module):
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attended_features, attention_weights = self.cross_attention(combined_seq)
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# Flatten attended features
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attended_flat = attended_features.view(attended_features.size(0), -1) # [batch, 512]
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attended_flat = attended_features.reshape(attended_features.size(0), -1) # [batch, 512]
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# Combine with microstructure features
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combined_features = torch.cat([attended_flat, micro_encoded], dim=1) # [batch, 640]
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@ -210,8 +210,7 @@ class VolumeProfileEncoder(nn.Module):
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if isinstance(volume_profile_data, list):
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if not volume_profile_data:
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# Return zero features if no data
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batch_size = 1
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return torch.zeros(batch_size, self.aggregator[-1].out_features)
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return torch.zeros(1, 256, device=torch.device('cpu')) # Hardcoded output dim as per hidden_dim in class init
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# Convert to tensor
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features = []
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@ -239,7 +238,7 @@ class VolumeProfileEncoder(nn.Module):
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# Encode each level
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level_features = self.level_encoder(volume_tensor.view(-1, feature_dim))
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level_features = level_features.view(batch_size, num_levels, -1)
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level_features = level_features.reshape(batch_size, num_levels, -1)
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# Apply attention across levels
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attended_levels, _ = self.level_attention(level_features)
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@ -423,14 +422,14 @@ class EnhancedCNNWithOrderBook(nn.Module):
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Returns:
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Dictionary with Q-values, confidence, regime, and auxiliary predictions
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"""
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batch_size = market_data.size(0)
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# Process market data
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# Process market data - ensure batch dimension first
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if len(market_data.shape) == 2:
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market_data = market_data.unsqueeze(0)
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# Reshape for convolutional processing
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market_reshaped = market_data.view(batch_size, -1, market_data.size(-1))
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batch_size = market_data.size(0) # Get correct batch size after shape adjustment
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# Reshape for convolutional processing with safe dimensions
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market_reshaped = market_data.reshape(batch_size, -1, market_data.size(-1))
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market_features = self.market_encoder(market_reshaped.transpose(1, 2))
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# Process order book data
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@ -440,7 +439,7 @@ class EnhancedCNNWithOrderBook(nn.Module):
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if volume_profile_data is not None:
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volume_features = self.volume_encoder(volume_profile_data)
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else:
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volume_features = torch.zeros(batch_size, 256, device=self.device)
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volume_features = torch.zeros(batch_size, 256, device=market_data.device)
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# Fuse all features
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combined_features = torch.cat([
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@ -81,15 +81,15 @@
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"wandb_artifact_name": null
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},
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{
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"checkpoint_id": "decision_20250704_082452",
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"checkpoint_id": "decision_20250704_214714",
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"model_name": "decision",
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"model_type": "decision_fusion",
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"file_path": "NN\\models\\saved\\decision\\decision_20250704_082452.pt",
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"created_at": "2025-07-04T08:24:52.949705",
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"file_path": "NN\\models\\saved\\decision\\decision_20250704_214714.pt",
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"created_at": "2025-07-04T21:47:14.427187",
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"file_size_mb": 0.06720924377441406,
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"performance_score": 102.79965677530546,
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"performance_score": 102.79966325731509,
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"accuracy": null,
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"loss": 3.432258725613987e-06,
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"loss": 3.3674381887394134e-06,
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"val_accuracy": null,
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"val_loss": null,
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"reward": null,
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@ -22,6 +22,7 @@ from collections import deque
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from .config import get_config
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from .data_provider import DataProvider
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from .universal_data_adapter import UniversalDataAdapter, UniversalDataStream
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from models import get_model_registry, ModelInterface, CNNModelInterface, RLAgentInterface, ModelRegistry
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# Import COB integration for real-time market microstructure data
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@ -69,6 +70,7 @@ class TradingOrchestrator:
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"""Initialize the enhanced orchestrator with full ML capabilities"""
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self.config = get_config()
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self.data_provider = data_provider or DataProvider()
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self.universal_adapter = UniversalDataAdapter(self.data_provider)
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self.model_registry = model_registry or get_model_registry()
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self.enhanced_rl_training = enhanced_rl_training
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@ -144,6 +146,7 @@ class TradingOrchestrator:
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logger.info(f"Confidence threshold: {self.confidence_threshold}")
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logger.info(f"Decision frequency: {self.decision_frequency}s")
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logger.info(f"Symbols: {self.symbols}")
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logger.info("Universal Data Adapter integrated for centralized data flow")
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# Initialize models and COB integration
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self._initialize_ml_models()
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@ -181,9 +184,9 @@ class TradingOrchestrator:
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result = load_best_checkpoint("dqn_agent")
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if result:
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file_path, metadata = result
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self.model_states['dqn']['initial_loss'] = 0.285
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self.model_states['dqn']['current_loss'] = metadata.loss or 0.0145
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self.model_states['dqn']['best_loss'] = metadata.loss or 0.0098
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self.model_states['dqn']['initial_loss'] = getattr(metadata, 'initial_loss', None)
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self.model_states['dqn']['current_loss'] = metadata.loss
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self.model_states['dqn']['best_loss'] = metadata.loss
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self.model_states['dqn']['checkpoint_loaded'] = True
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self.model_states['dqn']['checkpoint_filename'] = metadata.checkpoint_id
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checkpoint_loaded = True
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@ -192,10 +195,10 @@ class TradingOrchestrator:
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logger.warning(f"Error loading DQN checkpoint: {e}")
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if not checkpoint_loaded:
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# New model - set initial loss for tracking
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self.model_states['dqn']['initial_loss'] = 0.285 # Typical DQN starting loss
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self.model_states['dqn']['current_loss'] = 0.285
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self.model_states['dqn']['best_loss'] = 0.285
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# New model - no synthetic data, start fresh
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self.model_states['dqn']['initial_loss'] = None
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self.model_states['dqn']['current_loss'] = None
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self.model_states['dqn']['best_loss'] = None
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self.model_states['dqn']['checkpoint_filename'] = 'none (fresh start)'
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logger.info("DQN starting fresh - no checkpoint found")
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@ -230,9 +233,10 @@ class TradingOrchestrator:
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logger.warning(f"Error loading CNN checkpoint: {e}")
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if not checkpoint_loaded:
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self.model_states['cnn']['initial_loss'] = 0.412 # Typical CNN starting loss
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self.model_states['cnn']['current_loss'] = 0.412
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self.model_states['cnn']['best_loss'] = 0.412
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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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logger.info("Enhanced CNN model initialized")
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@ -251,9 +255,9 @@ class TradingOrchestrator:
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self.model_states['cnn']['checkpoint_loaded'] = True
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logger.info(f"CNN checkpoint loaded: loss={checkpoint_data.get('loss', 'N/A')}")
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else:
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self.model_states['cnn']['initial_loss'] = 0.412
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self.model_states['cnn']['current_loss'] = 0.412
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self.model_states['cnn']['best_loss'] = 0.412
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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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logger.info("Basic CNN model initialized")
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@ -279,9 +283,9 @@ class TradingOrchestrator:
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self.model_states['extrema_trainer']['checkpoint_loaded'] = True
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logger.info(f"Extrema trainer checkpoint loaded: loss={checkpoint_data.get('loss', 'N/A')}")
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else:
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self.model_states['extrema_trainer']['initial_loss'] = 0.356
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self.model_states['extrema_trainer']['current_loss'] = 0.356
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self.model_states['extrema_trainer']['best_loss'] = 0.356
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self.model_states['extrema_trainer']['initial_loss'] = None
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self.model_states['extrema_trainer']['current_loss'] = None
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self.model_states['extrema_trainer']['best_loss'] = None
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logger.info("Extrema trainer starting fresh - no checkpoint found")
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logger.info("Extrema trainer initialized")
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@ -289,15 +293,15 @@ class TradingOrchestrator:
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logger.warning("Extrema trainer not available")
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self.extrema_trainer = None
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# Initialize COB RL model state (placeholder)
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self.model_states['cob_rl']['initial_loss'] = 0.356
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self.model_states['cob_rl']['current_loss'] = 0.0098
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self.model_states['cob_rl']['best_loss'] = 0.0076
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# Initialize COB RL model state - no synthetic data
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self.model_states['cob_rl']['initial_loss'] = None
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self.model_states['cob_rl']['current_loss'] = None
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self.model_states['cob_rl']['best_loss'] = None
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# Initialize Decision model state (placeholder)
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self.model_states['decision']['initial_loss'] = 0.298
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self.model_states['decision']['current_loss'] = 0.0089
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self.model_states['decision']['best_loss'] = 0.0065
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# Initialize Decision model state - no synthetic data
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self.model_states['decision']['initial_loss'] = None
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self.model_states['decision']['current_loss'] = None
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self.model_states['decision']['best_loss'] = None
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# CRITICAL: Register models with the model registry
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logger.info("Registering models with model registry...")
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@ -1289,7 +1293,7 @@ class TradingOrchestrator:
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direction = best_action_idx # 0=SELL, 1=HOLD, 2=BUY
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pred_confidence = float(confidence) if confidence is not None else float(action_probs[best_action_idx])
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predicted_price = current_price * (1 + (pred_confidence * 0.01 if best_action == 'BUY' else -pred_confidence * 0.01 if best_action == 'SELL' else 0))
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self.capture_cnn_prediction(symbol, direction, pred_confidence, current_price, predicted_price)
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self.capture_cnn_prediction(symbol, int(direction), pred_confidence, current_price, predicted_price)
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except Exception as e:
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logger.error(f"Error getting CNN predictions: {e}")
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@ -2540,3 +2544,22 @@ class TradingOrchestrator:
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except Exception as e:
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logger.error(f"Error saving fusion checkpoint: {e}")
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def get_universal_data_stream(self, current_time: datetime = None) -> Optional[UniversalDataStream]:
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"""Get universal data stream for external consumers like dashboard"""
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try:
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return self.universal_adapter.get_universal_data_stream(current_time)
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except Exception as e:
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logger.error(f"Error getting universal data stream: {e}")
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return None
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def get_universal_data_for_model(self, model_type: str = 'cnn') -> Optional[Dict[str, Any]]:
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"""Get formatted universal data for specific model types"""
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try:
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stream = self.universal_adapter.get_universal_data_stream()
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if stream:
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return self.universal_adapter.format_for_model(stream, model_type)
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return None
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except Exception as e:
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logger.error(f"Error getting universal data for {model_type}: {e}")
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return None
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@ -1,637 +0,0 @@
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"""
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Unified Data Stream Architecture for Dashboard and Enhanced RL Training
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This module provides a centralized data streaming architecture that:
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1. Serves real-time data to the dashboard UI
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2. Feeds the enhanced RL training pipeline with comprehensive data
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3. Maintains data consistency across all consumers
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4. Provides efficient data distribution without duplication
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5. Supports multiple data consumers with different requirements
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Key Features:
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- Single source of truth for all market data
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- Real-time tick processing and aggregation
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- Multi-timeframe OHLCV generation
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- CNN feature extraction and caching
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- RL state building with comprehensive data
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- Dashboard-ready formatted data
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- Training data collection and buffering
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"""
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import asyncio
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import logging
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import time
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Tuple, Any, Callable
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from dataclasses import dataclass, field
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from collections import deque
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from threading import Thread, Lock
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import json
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from .config import get_config
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from .data_provider import DataProvider, MarketTick
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from .universal_data_adapter import UniversalDataAdapter, UniversalDataStream
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from .trading_action import TradingAction
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# Simple MarketState placeholder
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@dataclass
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class MarketState:
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"""Market state for unified data stream"""
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timestamp: datetime
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symbol: str
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price: float
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volume: float
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data: Dict[str, Any] = field(default_factory=dict)
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logger = logging.getLogger(__name__)
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@dataclass
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class StreamConsumer:
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"""Data stream consumer configuration"""
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consumer_id: str
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consumer_name: str
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callback: Callable[[Dict[str, Any]], None]
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data_types: List[str] # ['ticks', 'ohlcv', 'training_data', 'ui_data']
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active: bool = True
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last_update: datetime = field(default_factory=datetime.now)
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update_count: int = 0
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@dataclass
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class TrainingDataPacket:
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"""Training data packet for RL pipeline"""
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timestamp: datetime
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symbol: str
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tick_cache: List[Dict[str, Any]]
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one_second_bars: List[Dict[str, Any]]
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multi_timeframe_data: Dict[str, List[Dict[str, Any]]]
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cnn_features: Optional[Dict[str, np.ndarray]]
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cnn_predictions: Optional[Dict[str, np.ndarray]]
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market_state: Optional[MarketState]
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universal_stream: Optional[UniversalDataStream]
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@dataclass
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class UIDataPacket:
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"""UI data packet for dashboard"""
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timestamp: datetime
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current_prices: Dict[str, float]
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tick_cache_size: int
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one_second_bars_count: int
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streaming_status: str
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training_data_available: bool
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model_training_status: Dict[str, Any]
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orchestrator_status: Dict[str, Any]
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class UnifiedDataStream:
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"""
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Unified data stream manager for dashboard and training pipeline integration
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"""
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def __init__(self, data_provider: DataProvider, orchestrator=None):
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"""Initialize unified data stream"""
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self.config = get_config()
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self.data_provider = data_provider
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self.orchestrator = orchestrator
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# Initialize universal data adapter
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self.universal_adapter = UniversalDataAdapter(data_provider)
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# Data consumers registry
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self.consumers: Dict[str, StreamConsumer] = {}
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self.consumer_lock = Lock()
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# Data buffers for different consumers
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self.tick_cache = deque(maxlen=5000) # Raw tick cache
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self.one_second_bars = deque(maxlen=1000) # 1s OHLCV bars
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self.training_data_buffer = deque(maxlen=100) # Training data packets
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self.ui_data_buffer = deque(maxlen=50) # UI data packets
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# Multi-timeframe data storage
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self.multi_timeframe_data = {
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'ETH/USDT': {
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'1s': deque(maxlen=300),
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'1m': deque(maxlen=300),
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'1h': deque(maxlen=300),
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'1d': deque(maxlen=300)
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},
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'BTC/USDT': {
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'1s': deque(maxlen=300),
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'1m': deque(maxlen=300),
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'1h': deque(maxlen=300),
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'1d': deque(maxlen=300)
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}
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}
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# CNN features cache
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self.cnn_features_cache = {}
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self.cnn_predictions_cache = {}
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# Stream status
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self.streaming = False
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self.stream_thread = None
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# Performance tracking
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self.stream_stats = {
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'total_ticks_processed': 0,
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'total_packets_sent': 0,
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'consumers_served': 0,
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'last_tick_time': None,
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'processing_errors': 0,
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'data_quality_score': 1.0
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}
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# Data validation
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self.last_prices = {}
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self.price_change_threshold = 0.1 # 10% change threshold
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logger.info("Unified Data Stream initialized")
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logger.info(f"Symbols: {self.config.symbols}")
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logger.info(f"Timeframes: {self.config.timeframes}")
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def register_consumer(self, consumer_name: str, callback: Callable[[Dict[str, Any]], None],
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data_types: List[str]) -> str:
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"""Register a data consumer"""
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consumer_id = f"{consumer_name}_{int(time.time())}"
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with self.consumer_lock:
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consumer = StreamConsumer(
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consumer_id=consumer_id,
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consumer_name=consumer_name,
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callback=callback,
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data_types=data_types
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)
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self.consumers[consumer_id] = consumer
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logger.info(f"Registered consumer: {consumer_name} ({consumer_id})")
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logger.info(f"Data types: {data_types}")
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return consumer_id
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def unregister_consumer(self, consumer_id: str):
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"""Unregister a data consumer"""
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with self.consumer_lock:
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if consumer_id in self.consumers:
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consumer = self.consumers.pop(consumer_id)
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logger.info(f"Unregistered consumer: {consumer.consumer_name} ({consumer_id})")
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async def start_streaming(self):
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"""Start unified data streaming"""
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if self.streaming:
|
||||
logger.warning("Data streaming already active")
|
||||
return
|
||||
|
||||
self.streaming = True
|
||||
|
||||
# Subscribe to data provider ticks
|
||||
self.data_provider.subscribe_to_ticks(
|
||||
callback=self._handle_tick,
|
||||
symbols=self.config.symbols,
|
||||
subscriber_name="UnifiedDataStream"
|
||||
)
|
||||
|
||||
# Start background processing
|
||||
self.stream_thread = Thread(target=self._stream_processor, daemon=True)
|
||||
self.stream_thread.start()
|
||||
|
||||
logger.info("Unified data streaming started")
|
||||
|
||||
async def stop_streaming(self):
|
||||
"""Stop unified data streaming"""
|
||||
self.streaming = False
|
||||
|
||||
if self.stream_thread:
|
||||
self.stream_thread.join(timeout=5)
|
||||
|
||||
logger.info("Unified data streaming stopped")
|
||||
|
||||
def _handle_tick(self, tick: MarketTick):
|
||||
"""Handle incoming tick data"""
|
||||
try:
|
||||
# Validate tick data
|
||||
if not self._validate_tick(tick):
|
||||
return
|
||||
|
||||
# Add to tick cache
|
||||
tick_data = {
|
||||
'symbol': tick.symbol,
|
||||
'timestamp': tick.timestamp,
|
||||
'price': tick.price,
|
||||
'volume': tick.volume,
|
||||
'quantity': tick.quantity,
|
||||
'side': tick.side
|
||||
}
|
||||
|
||||
self.tick_cache.append(tick_data)
|
||||
|
||||
# Update current prices
|
||||
self.last_prices[tick.symbol] = tick.price
|
||||
|
||||
# Generate 1s bars if needed
|
||||
self._update_one_second_bars(tick_data)
|
||||
|
||||
# Update multi-timeframe data
|
||||
self._update_multi_timeframe_data(tick_data)
|
||||
|
||||
# Update statistics
|
||||
self.stream_stats['total_ticks_processed'] += 1
|
||||
self.stream_stats['last_tick_time'] = tick.timestamp
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error handling tick: {e}")
|
||||
self.stream_stats['processing_errors'] += 1
|
||||
|
||||
def _validate_tick(self, tick: MarketTick) -> bool:
|
||||
"""Validate tick data quality"""
|
||||
try:
|
||||
# Check for valid price
|
||||
if tick.price <= 0:
|
||||
return False
|
||||
|
||||
# Check for reasonable price change
|
||||
if tick.symbol in self.last_prices:
|
||||
last_price = self.last_prices[tick.symbol]
|
||||
if last_price > 0:
|
||||
price_change = abs(tick.price - last_price) / last_price
|
||||
if price_change > self.price_change_threshold:
|
||||
logger.warning(f"Large price change detected for {tick.symbol}: {price_change:.2%}")
|
||||
return False
|
||||
|
||||
# Check timestamp
|
||||
if tick.timestamp > datetime.now() + timedelta(seconds=10):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error validating tick: {e}")
|
||||
return False
|
||||
|
||||
def _update_one_second_bars(self, tick_data: Dict[str, Any]):
|
||||
"""Update 1-second OHLCV bars"""
|
||||
try:
|
||||
symbol = tick_data['symbol']
|
||||
price = tick_data['price']
|
||||
volume = tick_data['volume']
|
||||
timestamp = tick_data['timestamp']
|
||||
|
||||
# Round timestamp to nearest second
|
||||
bar_timestamp = timestamp.replace(microsecond=0)
|
||||
|
||||
# Check if we need a new bar
|
||||
if (not self.one_second_bars or
|
||||
self.one_second_bars[-1]['timestamp'] != bar_timestamp or
|
||||
self.one_second_bars[-1]['symbol'] != symbol):
|
||||
|
||||
# Create new 1s bar
|
||||
bar_data = {
|
||||
'symbol': symbol,
|
||||
'timestamp': bar_timestamp,
|
||||
'open': price,
|
||||
'high': price,
|
||||
'low': price,
|
||||
'close': price,
|
||||
'volume': volume
|
||||
}
|
||||
self.one_second_bars.append(bar_data)
|
||||
else:
|
||||
# Update existing bar
|
||||
bar = self.one_second_bars[-1]
|
||||
bar['high'] = max(bar['high'], price)
|
||||
bar['low'] = min(bar['low'], price)
|
||||
bar['close'] = price
|
||||
bar['volume'] += volume
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating 1s bars: {e}")
|
||||
|
||||
def _update_multi_timeframe_data(self, tick_data: Dict[str, Any]):
|
||||
"""Update multi-timeframe OHLCV data"""
|
||||
try:
|
||||
symbol = tick_data['symbol']
|
||||
if symbol not in self.multi_timeframe_data:
|
||||
return
|
||||
|
||||
# Update each timeframe
|
||||
for timeframe in ['1s', '1m', '1h', '1d']:
|
||||
self._update_timeframe_bar(symbol, timeframe, tick_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating multi-timeframe data: {e}")
|
||||
|
||||
def _update_timeframe_bar(self, symbol: str, timeframe: str, tick_data: Dict[str, Any]):
|
||||
"""Update specific timeframe bar"""
|
||||
try:
|
||||
price = tick_data['price']
|
||||
volume = tick_data['volume']
|
||||
timestamp = tick_data['timestamp']
|
||||
|
||||
# Calculate bar timestamp based on timeframe
|
||||
if timeframe == '1s':
|
||||
bar_timestamp = timestamp.replace(microsecond=0)
|
||||
elif timeframe == '1m':
|
||||
bar_timestamp = timestamp.replace(second=0, microsecond=0)
|
||||
elif timeframe == '1h':
|
||||
bar_timestamp = timestamp.replace(minute=0, second=0, microsecond=0)
|
||||
elif timeframe == '1d':
|
||||
bar_timestamp = timestamp.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
else:
|
||||
return
|
||||
|
||||
timeframe_buffer = self.multi_timeframe_data[symbol][timeframe]
|
||||
|
||||
# Check if we need a new bar
|
||||
if (not timeframe_buffer or
|
||||
timeframe_buffer[-1]['timestamp'] != bar_timestamp):
|
||||
|
||||
# Create new bar
|
||||
bar_data = {
|
||||
'timestamp': bar_timestamp,
|
||||
'open': price,
|
||||
'high': price,
|
||||
'low': price,
|
||||
'close': price,
|
||||
'volume': volume
|
||||
}
|
||||
timeframe_buffer.append(bar_data)
|
||||
else:
|
||||
# Update existing bar
|
||||
bar = timeframe_buffer[-1]
|
||||
bar['high'] = max(bar['high'], price)
|
||||
bar['low'] = min(bar['low'], price)
|
||||
bar['close'] = price
|
||||
bar['volume'] += volume
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating {timeframe} bar for {symbol}: {e}")
|
||||
|
||||
def _stream_processor(self):
|
||||
"""Background stream processor"""
|
||||
logger.info("Stream processor started")
|
||||
|
||||
while self.streaming:
|
||||
try:
|
||||
# Process training data packets
|
||||
self._process_training_data()
|
||||
|
||||
# Process UI data packets
|
||||
self._process_ui_data()
|
||||
|
||||
# Update CNN features if orchestrator available
|
||||
if self.orchestrator:
|
||||
self._update_cnn_features()
|
||||
|
||||
# Distribute data to consumers
|
||||
self._distribute_data()
|
||||
|
||||
# Sleep briefly
|
||||
time.sleep(0.1) # 100ms processing cycle
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in stream processor: {e}")
|
||||
time.sleep(1)
|
||||
|
||||
logger.info("Stream processor stopped")
|
||||
|
||||
def _process_training_data(self):
|
||||
"""Process and package training data"""
|
||||
try:
|
||||
if len(self.tick_cache) < 10: # Need minimum data
|
||||
return
|
||||
|
||||
# Create training data packet
|
||||
training_packet = TrainingDataPacket(
|
||||
timestamp=datetime.now(),
|
||||
symbol='ETH/USDT', # Primary symbol
|
||||
tick_cache=list(self.tick_cache)[-300:], # Last 300 ticks
|
||||
one_second_bars=list(self.one_second_bars)[-300:], # Last 300 1s bars
|
||||
multi_timeframe_data=self._get_multi_timeframe_snapshot(),
|
||||
cnn_features=self.cnn_features_cache.copy(),
|
||||
cnn_predictions=self.cnn_predictions_cache.copy(),
|
||||
market_state=self._build_market_state(),
|
||||
universal_stream=self._get_universal_stream()
|
||||
)
|
||||
|
||||
self.training_data_buffer.append(training_packet)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing training data: {e}")
|
||||
|
||||
def _process_ui_data(self):
|
||||
"""Process and package UI data"""
|
||||
try:
|
||||
# Create UI data packet
|
||||
ui_packet = UIDataPacket(
|
||||
timestamp=datetime.now(),
|
||||
current_prices=self.last_prices.copy(),
|
||||
tick_cache_size=len(self.tick_cache),
|
||||
one_second_bars_count=len(self.one_second_bars),
|
||||
streaming_status='LIVE' if self.streaming else 'STOPPED',
|
||||
training_data_available=len(self.training_data_buffer) > 0,
|
||||
model_training_status=self._get_model_training_status(),
|
||||
orchestrator_status=self._get_orchestrator_status()
|
||||
)
|
||||
|
||||
self.ui_data_buffer.append(ui_packet)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing UI data: {e}")
|
||||
|
||||
def _update_cnn_features(self):
|
||||
"""Update CNN features cache"""
|
||||
try:
|
||||
if not self.orchestrator:
|
||||
return
|
||||
|
||||
# Get CNN features from orchestrator
|
||||
for symbol in self.config.symbols:
|
||||
if hasattr(self.orchestrator, '_get_cnn_features_for_rl'):
|
||||
hidden_features, predictions = self.orchestrator._get_cnn_features_for_rl(symbol)
|
||||
|
||||
if hidden_features:
|
||||
self.cnn_features_cache[symbol] = hidden_features
|
||||
|
||||
if predictions:
|
||||
self.cnn_predictions_cache[symbol] = predictions
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating CNN features: {e}")
|
||||
|
||||
def _distribute_data(self):
|
||||
"""Distribute data to registered consumers"""
|
||||
try:
|
||||
with self.consumer_lock:
|
||||
for consumer_id, consumer in self.consumers.items():
|
||||
if not consumer.active:
|
||||
continue
|
||||
|
||||
try:
|
||||
# Prepare data based on consumer requirements
|
||||
data_packet = self._prepare_consumer_data(consumer)
|
||||
|
||||
if data_packet:
|
||||
# Send data to consumer
|
||||
consumer.callback(data_packet)
|
||||
consumer.update_count += 1
|
||||
consumer.last_update = datetime.now()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error sending data to consumer {consumer.consumer_name}: {e}")
|
||||
consumer.active = False
|
||||
|
||||
self.stream_stats['consumers_served'] = len([c for c in self.consumers.values() if c.active])
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error distributing data: {e}")
|
||||
|
||||
def _prepare_consumer_data(self, consumer: StreamConsumer) -> Optional[Dict[str, Any]]:
|
||||
"""Prepare data packet for specific consumer"""
|
||||
try:
|
||||
data_packet = {
|
||||
'timestamp': datetime.now(),
|
||||
'consumer_id': consumer.consumer_id,
|
||||
'consumer_name': consumer.consumer_name
|
||||
}
|
||||
|
||||
# Add requested data types
|
||||
if 'ticks' in consumer.data_types:
|
||||
data_packet['ticks'] = list(self.tick_cache)[-100:] # Last 100 ticks
|
||||
|
||||
if 'ohlcv' in consumer.data_types:
|
||||
data_packet['one_second_bars'] = list(self.one_second_bars)[-100:]
|
||||
data_packet['multi_timeframe'] = self._get_multi_timeframe_snapshot()
|
||||
|
||||
if 'training_data' in consumer.data_types:
|
||||
if self.training_data_buffer:
|
||||
data_packet['training_data'] = self.training_data_buffer[-1]
|
||||
|
||||
if 'ui_data' in consumer.data_types:
|
||||
if self.ui_data_buffer:
|
||||
data_packet['ui_data'] = self.ui_data_buffer[-1]
|
||||
|
||||
return data_packet
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error preparing data for consumer {consumer.consumer_name}: {e}")
|
||||
return None
|
||||
|
||||
def _get_multi_timeframe_snapshot(self) -> Dict[str, Dict[str, List[Dict[str, Any]]]]:
|
||||
"""Get snapshot of multi-timeframe data"""
|
||||
snapshot = {}
|
||||
for symbol, timeframes in self.multi_timeframe_data.items():
|
||||
snapshot[symbol] = {}
|
||||
for timeframe, data in timeframes.items():
|
||||
snapshot[symbol][timeframe] = list(data)
|
||||
return snapshot
|
||||
|
||||
def _build_market_state(self) -> Optional[MarketState]:
|
||||
"""Build market state for training"""
|
||||
try:
|
||||
if not self.orchestrator:
|
||||
return None
|
||||
|
||||
# Get universal stream
|
||||
universal_stream = self._get_universal_stream()
|
||||
if not universal_stream:
|
||||
return None
|
||||
|
||||
# Build market state using orchestrator
|
||||
symbol = 'ETH/USDT'
|
||||
current_price = self.last_prices.get(symbol, 0.0)
|
||||
|
||||
market_state = MarketState(
|
||||
symbol=symbol,
|
||||
timestamp=datetime.now(),
|
||||
prices={'current': current_price},
|
||||
features={},
|
||||
volatility=0.0,
|
||||
volume=0.0,
|
||||
trend_strength=0.0,
|
||||
market_regime='unknown',
|
||||
universal_data=universal_stream,
|
||||
raw_ticks=list(self.tick_cache)[-300:],
|
||||
ohlcv_data=self._get_multi_timeframe_snapshot(),
|
||||
btc_reference_data=self._get_btc_reference_data(),
|
||||
cnn_hidden_features=self.cnn_features_cache.copy(),
|
||||
cnn_predictions=self.cnn_predictions_cache.copy()
|
||||
)
|
||||
|
||||
return market_state
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error building market state: {e}")
|
||||
return None
|
||||
|
||||
def _get_universal_stream(self) -> Optional[UniversalDataStream]:
|
||||
"""Get universal data stream"""
|
||||
try:
|
||||
if self.universal_adapter:
|
||||
return self.universal_adapter.get_universal_stream()
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting universal stream: {e}")
|
||||
return None
|
||||
|
||||
def _get_btc_reference_data(self) -> Dict[str, List[Dict[str, Any]]]:
|
||||
"""Get BTC reference data"""
|
||||
btc_data = {}
|
||||
if 'BTC/USDT' in self.multi_timeframe_data:
|
||||
for timeframe, data in self.multi_timeframe_data['BTC/USDT'].items():
|
||||
btc_data[timeframe] = list(data)
|
||||
return btc_data
|
||||
|
||||
def _get_model_training_status(self) -> Dict[str, Any]:
|
||||
"""Get model training status"""
|
||||
try:
|
||||
if self.orchestrator and hasattr(self.orchestrator, 'get_performance_metrics'):
|
||||
return self.orchestrator.get_performance_metrics()
|
||||
|
||||
return {
|
||||
'cnn_status': 'TRAINING',
|
||||
'rl_status': 'TRAINING',
|
||||
'data_available': len(self.training_data_buffer) > 0
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting model training status: {e}")
|
||||
return {}
|
||||
|
||||
def _get_orchestrator_status(self) -> Dict[str, Any]:
|
||||
"""Get orchestrator status"""
|
||||
try:
|
||||
if self.orchestrator:
|
||||
return {
|
||||
'active': True,
|
||||
'symbols': self.config.symbols,
|
||||
'streaming': self.streaming,
|
||||
'tick_processor_active': hasattr(self.orchestrator, 'tick_processor')
|
||||
}
|
||||
|
||||
return {'active': False}
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting orchestrator status: {e}")
|
||||
return {'active': False}
|
||||
|
||||
def get_stream_stats(self) -> Dict[str, Any]:
|
||||
"""Get stream statistics"""
|
||||
stats = self.stream_stats.copy()
|
||||
stats.update({
|
||||
'tick_cache_size': len(self.tick_cache),
|
||||
'one_second_bars_count': len(self.one_second_bars),
|
||||
'training_data_packets': len(self.training_data_buffer),
|
||||
'ui_data_packets': len(self.ui_data_buffer),
|
||||
'active_consumers': len([c for c in self.consumers.values() if c.active]),
|
||||
'total_consumers': len(self.consumers)
|
||||
})
|
||||
return stats
|
||||
|
||||
def get_latest_training_data(self) -> Optional[TrainingDataPacket]:
|
||||
"""Get latest training data packet"""
|
||||
if self.training_data_buffer:
|
||||
return self.training_data_buffer[-1]
|
||||
return None
|
||||
|
||||
def get_latest_ui_data(self) -> Optional[UIDataPacket]:
|
||||
"""Get latest UI data packet"""
|
||||
if self.ui_data_buffer:
|
||||
return self.ui_data_buffer[-1]
|
||||
return None
|
||||
@ -1 +1,35 @@
|
||||
our system architecture is such that we have data inflow with different rates from different providers. our data flow though the system should be single and centralized. I think our orchestrator class is taking that role. since our different data feeds have different rates (and also each model has different inference times and cycle) our orchestrator should keep cache of the latest available data and keep track of the rates and statistics of each data source - being data api or our own model outputs. so the available data is constantly updated and refreshed in realtime by multiple sources, and is also consumed by all smodels
|
||||
I. our system architecture is such that we have data inflow with different rates from different providers. our data flow though the system should be single and centralized. I think our orchestrator class is taking that role. since our different data feeds have different rates (and also each model has different inference times and cycle) our orchestrator should keep cache of the latest available data and keep track of the rates and statistics of each data source - being data api or our own model outputs. so the available data is constantly updated and refreshed in realtime by multiple sources, and is also consumed by all smodels
|
||||
II. orchestrator should also be responsible for the data ingestion and processing. it should be able to handle the data from different sources and process them in a unified way. it may hold cache of the latest available data and keep track of the rates and statistics of each data source - being data api or our own model outputs. so the available data is constantly updated and refreshed in realtime by multiple sources, and is also consumed by all smodels. orchestrator holds business logic and rules, but also uses our special decision model which is at the end of the data flow and is used to lean the effectivenes of the other model outputs in contribute to succeessful prediction. this way we will have learned signal weight. it should be trained on each price prediction data point and each trade signal data point.
|
||||
orchestrator can use the various trainer classes as different models have different training requirements and pipelines.
|
||||
|
||||
III. models we currently use (architecture is expandable with easy adaption to new models)
|
||||
- cnn price prediction model - uses calculated multilevel pivot points and historical price data to predict the next pivot point for each level.
|
||||
- DQN RL model outputs trade signals
|
||||
- transformer model outputs price prediction
|
||||
- COB RL model outputs trade signals - it is trained on cob (cached all COB data for period of time not just current order book. it should be a 2d matrix 1s aggregated ) and some indicators cummulative cob imbalance for different timeframes.
|
||||
- decision model - it is trained on price prediction and trade signals to learn the effectiveness of the other models in contribute to succeessful prediction. outputs the final trade signal.
|
||||
|
||||
|
||||
|
||||
IV. by default all models take full current data frames available in the orchestrator on inference as base data - different aspects of the data are updated at different rates. main data frame includes 5 price charts
|
||||
class UniversalDataAdapter:
|
||||
- 1s 1m 1h ETH charts and ETH and BTC ticks. orchestrator can use and extend the UniversalDataAdapter class to add new data sources and data types.
|
||||
- - cob models are different and they get fast realtime raw dob data ticks and should be agile to inference and procude outputs but yet able to learn.
|
||||
|
||||
V. hardware. we use GPU if available for training and inference for optimised performance.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
dashboard should be able to show the data from the orchestrator and hold some amount of bussiness logic related to UI representations, but limited. it mainly relies on the orchestrator to provide the data and the models to make the decisions. dash's main job is to show the data and the models' decisions in a user friendly way.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@ def check_enhanced_rl_availability():
|
||||
|
||||
# Test 2: Unified data stream import
|
||||
try:
|
||||
from core.unified_data_stream import UnifiedDataStream, TrainingDataPacket, UIDataPacket
|
||||
from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream
|
||||
logger.info("✅ Unified data stream components import successfully")
|
||||
except ImportError as e:
|
||||
issues.append(f"❌ Cannot import unified data stream: {e}")
|
||||
|
||||
273
test_architecture_compliance.py
Normal file
273
test_architecture_compliance.py
Normal file
@ -0,0 +1,273 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test Architecture Compliance After Cleanup
|
||||
|
||||
This test verifies that the system now follows the correct architecture:
|
||||
1. Single, centralized data flow through orchestrator
|
||||
2. Dashboard gets data through orchestrator, not direct stream management
|
||||
3. UniversalDataAdapter is the only data stream implementation
|
||||
4. No conflicting UnifiedDataStream implementations
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from typing import Dict, Any
|
||||
|
||||
# Add project root to path
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def test_architecture_imports():
|
||||
"""Test that correct architecture components can be imported"""
|
||||
logger.info("=== Testing Architecture Imports ===")
|
||||
|
||||
try:
|
||||
# Test core architecture components
|
||||
from core.orchestrator import TradingOrchestrator
|
||||
from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream
|
||||
from core.data_provider import DataProvider
|
||||
logger.info("✓ Core architecture components imported successfully")
|
||||
|
||||
# Test dashboard imports
|
||||
from web.clean_dashboard import create_clean_dashboard
|
||||
logger.info("✓ Dashboard components imported successfully")
|
||||
|
||||
# Verify UnifiedDataStream is NOT available (should be removed)
|
||||
try:
|
||||
import importlib.util
|
||||
spec = importlib.util.find_spec("core.unified_data_stream")
|
||||
if spec is not None:
|
||||
logger.error("✗ Old unified_data_stream module still exists - should have been removed")
|
||||
return False
|
||||
else:
|
||||
logger.info("✓ Old unified_data_stream module correctly removed")
|
||||
except Exception as e:
|
||||
logger.info("✓ Old unified_data_stream module correctly removed")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"✗ Import test failed: {e}")
|
||||
return False
|
||||
|
||||
def test_orchestrator_data_integration():
|
||||
"""Test that orchestrator properly integrates with UniversalDataAdapter"""
|
||||
logger.info("=== Testing Orchestrator Data Integration ===")
|
||||
|
||||
try:
|
||||
from core.orchestrator import TradingOrchestrator
|
||||
from core.universal_data_adapter import UniversalDataAdapter
|
||||
from core.data_provider import DataProvider
|
||||
|
||||
# Create data provider
|
||||
data_provider = DataProvider()
|
||||
|
||||
# Create orchestrator
|
||||
orchestrator = TradingOrchestrator(data_provider=data_provider)
|
||||
|
||||
# Verify orchestrator has universal_adapter
|
||||
if not hasattr(orchestrator, 'universal_adapter'):
|
||||
logger.error("✗ Orchestrator missing universal_adapter attribute")
|
||||
return False
|
||||
|
||||
if not isinstance(orchestrator.universal_adapter, UniversalDataAdapter):
|
||||
logger.error("✗ Orchestrator universal_adapter is not UniversalDataAdapter instance")
|
||||
return False
|
||||
|
||||
logger.info("✓ Orchestrator properly integrated with UniversalDataAdapter")
|
||||
|
||||
# Test orchestrator data access methods
|
||||
if not hasattr(orchestrator, 'get_universal_data_stream'):
|
||||
logger.error("✗ Orchestrator missing get_universal_data_stream method")
|
||||
return False
|
||||
|
||||
if not hasattr(orchestrator, 'get_universal_data_for_model'):
|
||||
logger.error("✗ Orchestrator missing get_universal_data_for_model method")
|
||||
return False
|
||||
|
||||
logger.info("✓ Orchestrator has required data access methods")
|
||||
|
||||
# Test data stream access
|
||||
try:
|
||||
data_stream = orchestrator.get_universal_data_stream()
|
||||
logger.info("✓ Orchestrator data stream access working")
|
||||
except Exception as e:
|
||||
logger.warning(f"⚠ Orchestrator data stream access warning: {e}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"✗ Orchestrator integration test failed: {e}")
|
||||
return False
|
||||
|
||||
def test_dashboard_architecture_compliance():
|
||||
"""Test that dashboard follows correct architecture pattern"""
|
||||
logger.info("=== Testing Dashboard Architecture Compliance ===")
|
||||
|
||||
try:
|
||||
# Import dashboard components
|
||||
from web.clean_dashboard import create_clean_dashboard
|
||||
|
||||
# Read dashboard source to verify architecture compliance
|
||||
dashboard_path = os.path.join(os.path.dirname(__file__), 'web', 'clean_dashboard.py')
|
||||
with open(dashboard_path, 'r') as f:
|
||||
dashboard_source = f.read()
|
||||
|
||||
# Verify dashboard uses UniversalDataAdapter, not UnifiedDataStream
|
||||
if 'UniversalDataAdapter' not in dashboard_source:
|
||||
logger.error("✗ Dashboard not using UniversalDataAdapter")
|
||||
return False
|
||||
|
||||
if 'UnifiedDataStream' in dashboard_source and 'UniversalDataAdapter' not in dashboard_source:
|
||||
logger.error("✗ Dashboard still using old UnifiedDataStream")
|
||||
return False
|
||||
|
||||
logger.info("✓ Dashboard using correct UniversalDataAdapter")
|
||||
|
||||
# Verify dashboard gets data through orchestrator
|
||||
if '_get_universal_data_from_orchestrator' not in dashboard_source:
|
||||
logger.error("✗ Dashboard not getting data through orchestrator")
|
||||
return False
|
||||
|
||||
logger.info("✓ Dashboard getting data through orchestrator")
|
||||
|
||||
# Verify dashboard doesn't manage streams directly
|
||||
problematic_patterns = [
|
||||
'register_consumer',
|
||||
'subscribe_to_stream',
|
||||
'stream_consumer',
|
||||
'add_consumer'
|
||||
]
|
||||
|
||||
for pattern in problematic_patterns:
|
||||
if pattern in dashboard_source:
|
||||
logger.warning(f"⚠ Dashboard may still have direct stream management: {pattern}")
|
||||
|
||||
logger.info("✓ Dashboard architecture compliance verified")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"✗ Dashboard architecture test failed: {e}")
|
||||
return False
|
||||
|
||||
def test_data_flow_architecture():
|
||||
"""Test the complete data flow architecture"""
|
||||
logger.info("=== Testing Complete Data Flow Architecture ===")
|
||||
|
||||
try:
|
||||
from core.data_provider import DataProvider
|
||||
from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream
|
||||
from core.orchestrator import TradingOrchestrator
|
||||
|
||||
# Create the data flow chain
|
||||
data_provider = DataProvider()
|
||||
universal_adapter = UniversalDataAdapter(data_provider)
|
||||
orchestrator = TradingOrchestrator(data_provider=data_provider)
|
||||
|
||||
# Verify data flow: DataProvider -> UniversalDataAdapter -> Orchestrator
|
||||
logger.info("✓ Data flow components created successfully")
|
||||
|
||||
# Test UniversalDataStream structure
|
||||
try:
|
||||
# Get sample data stream
|
||||
sample_stream = universal_adapter.get_universal_data_stream()
|
||||
|
||||
# Verify it's a UniversalDataStream dataclass
|
||||
if hasattr(sample_stream, 'eth_ticks'):
|
||||
logger.info("✓ UniversalDataStream has eth_ticks")
|
||||
if hasattr(sample_stream, 'eth_1m'):
|
||||
logger.info("✓ UniversalDataStream has eth_1m")
|
||||
if hasattr(sample_stream, 'eth_1h'):
|
||||
logger.info("✓ UniversalDataStream has eth_1h")
|
||||
if hasattr(sample_stream, 'eth_1d'):
|
||||
logger.info("✓ UniversalDataStream has eth_1d")
|
||||
if hasattr(sample_stream, 'btc_ticks'):
|
||||
logger.info("✓ UniversalDataStream has btc_ticks")
|
||||
|
||||
logger.info("✓ UniversalDataStream structure verified")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"⚠ UniversalDataStream structure test warning: {e}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"✗ Data flow architecture test failed: {e}")
|
||||
return False
|
||||
|
||||
def test_removed_files():
|
||||
"""Test that conflicting files were properly removed"""
|
||||
logger.info("=== Testing Removed Files ===")
|
||||
|
||||
# Check that unified_data_stream.py was removed
|
||||
unified_stream_path = os.path.join(os.path.dirname(__file__), 'core', 'unified_data_stream.py')
|
||||
if os.path.exists(unified_stream_path):
|
||||
logger.error("✗ core/unified_data_stream.py still exists - should be removed")
|
||||
return False
|
||||
|
||||
logger.info("✓ Conflicting unified_data_stream.py properly removed")
|
||||
|
||||
# Check that universal_data_adapter.py still exists
|
||||
universal_adapter_path = os.path.join(os.path.dirname(__file__), 'core', 'universal_data_adapter.py')
|
||||
if not os.path.exists(universal_adapter_path):
|
||||
logger.error("✗ core/universal_data_adapter.py missing - should exist")
|
||||
return False
|
||||
|
||||
logger.info("✓ Correct universal_data_adapter.py exists")
|
||||
|
||||
return True
|
||||
|
||||
def run_all_tests():
|
||||
"""Run all architecture compliance tests"""
|
||||
logger.info("=" * 60)
|
||||
logger.info("ARCHITECTURE COMPLIANCE TEST SUITE")
|
||||
logger.info("Testing data flow cleanup and architecture compliance")
|
||||
logger.info("=" * 60)
|
||||
|
||||
tests = [
|
||||
("Import Architecture", test_architecture_imports),
|
||||
("Orchestrator Integration", test_orchestrator_data_integration),
|
||||
("Dashboard Compliance", test_dashboard_architecture_compliance),
|
||||
("Data Flow Architecture", test_data_flow_architecture),
|
||||
("Removed Files", test_removed_files)
|
||||
]
|
||||
|
||||
passed = 0
|
||||
total = len(tests)
|
||||
|
||||
for test_name, test_func in tests:
|
||||
logger.info(f"\n--- {test_name} ---")
|
||||
try:
|
||||
if test_func():
|
||||
logger.info(f"✓ {test_name} PASSED")
|
||||
passed += 1
|
||||
else:
|
||||
logger.error(f"✗ {test_name} FAILED")
|
||||
except Exception as e:
|
||||
logger.error(f"✗ {test_name} ERROR: {e}")
|
||||
|
||||
logger.info("\n" + "=" * 60)
|
||||
logger.info(f"ARCHITECTURE COMPLIANCE TEST RESULTS: {passed}/{total} tests passed")
|
||||
|
||||
if passed == total:
|
||||
logger.info("🎉 ALL TESTS PASSED - Architecture cleanup successful!")
|
||||
logger.info("✓ Single, centralized data flow through orchestrator")
|
||||
logger.info("✓ Dashboard gets data through orchestrator methods")
|
||||
logger.info("✓ UniversalDataAdapter is the only data stream implementation")
|
||||
logger.info("✓ No conflicting UnifiedDataStream implementations")
|
||||
return True
|
||||
else:
|
||||
logger.error(f"❌ {total - passed} tests failed - Architecture issues remain")
|
||||
return False
|
||||
|
||||
if __name__ == "__main__":
|
||||
success = run_all_tests()
|
||||
sys.exit(0 if success else 1)
|
||||
@ -33,7 +33,7 @@ logger = logging.getLogger(__name__)
|
||||
from core.config import get_config
|
||||
from core.data_provider import DataProvider
|
||||
from core.enhanced_orchestrator import EnhancedTradingOrchestrator
|
||||
from core.unified_data_stream import UnifiedDataStream
|
||||
from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream
|
||||
from web.old_archived.scalping_dashboard import RealTimeScalpingDashboard
|
||||
|
||||
class EnhancedDashboardIntegrationTest:
|
||||
|
||||
@ -29,7 +29,7 @@ def test_enhanced_rl_imports():
|
||||
return False
|
||||
|
||||
try:
|
||||
from core.unified_data_stream import UnifiedDataStream, TrainingDataPacket, UIDataPacket
|
||||
from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream
|
||||
logger.info("✅ UnifiedDataStream components import: SUCCESS")
|
||||
except ImportError as e:
|
||||
logger.error(f"❌ UnifiedDataStream components import: FAILED - {e}")
|
||||
|
||||
@ -69,16 +69,13 @@ except ImportError:
|
||||
COB_INTEGRATION_AVAILABLE = False
|
||||
logger.warning("COB integration not available")
|
||||
|
||||
# Universal Data Stream - temporarily disabled due to import issues
|
||||
UNIFIED_STREAM_AVAILABLE = False
|
||||
|
||||
# Placeholder class for disabled Universal Data Stream
|
||||
class UnifiedDataStream:
|
||||
"""Placeholder for disabled Universal Data Stream"""
|
||||
def __init__(self, *args, **kwargs):
|
||||
pass
|
||||
def register_consumer(self, *args, **kwargs):
|
||||
return "disabled"
|
||||
# Universal Data Adapter - the correct architecture implementation
|
||||
try:
|
||||
from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream
|
||||
UNIVERSAL_DATA_AVAILABLE = True
|
||||
except ImportError:
|
||||
UNIVERSAL_DATA_AVAILABLE = False
|
||||
logger.warning("Universal Data Adapter not available")
|
||||
|
||||
# Import RL COB trader for 1B parameter model integration
|
||||
from core.realtime_rl_cob_trader import RealtimeRLCOBTrader, PredictionResult
|
||||
@ -117,20 +114,13 @@ class CleanTradingDashboard:
|
||||
)
|
||||
self.component_manager = DashboardComponentManager()
|
||||
|
||||
# Initialize Universal Data Stream for the 5 timeseries architecture
|
||||
if UNIFIED_STREAM_AVAILABLE:
|
||||
self.unified_stream = UnifiedDataStream(self.data_provider, self.orchestrator)
|
||||
self.stream_consumer_id = self.unified_stream.register_consumer(
|
||||
consumer_name="CleanTradingDashboard",
|
||||
callback=self._handle_unified_stream_data,
|
||||
data_types=['ticks', 'ohlcv', 'training_data', 'ui_data']
|
||||
)
|
||||
logger.debug(f"Universal Data Stream initialized with consumer ID: {self.stream_consumer_id}")
|
||||
logger.debug("Subscribed to Universal 5 Timeseries: ETH(ticks,1m,1h,1d) + BTC(ticks)")
|
||||
# Initialize Universal Data Adapter access through orchestrator
|
||||
if UNIVERSAL_DATA_AVAILABLE:
|
||||
self.universal_adapter = UniversalDataAdapter(self.data_provider)
|
||||
logger.debug("Universal Data Adapter initialized - accessing data through orchestrator")
|
||||
else:
|
||||
self.unified_stream = None
|
||||
self.stream_consumer_id = None
|
||||
logger.warning("Universal Data Stream not available - fallback to direct data access")
|
||||
self.universal_adapter = None
|
||||
logger.warning("Universal Data Adapter not available - fallback to direct data access")
|
||||
|
||||
# Dashboard state
|
||||
self.recent_decisions: list = []
|
||||
@ -202,10 +192,8 @@ class CleanTradingDashboard:
|
||||
# Initialize unified orchestrator features - start async methods
|
||||
# self._initialize_unified_orchestrator_features() # Temporarily disabled
|
||||
|
||||
# Start Universal Data Stream
|
||||
if self.unified_stream:
|
||||
# threading.Thread(target=self._start_unified_stream, daemon=True).start() # Temporarily disabled
|
||||
logger.debug("Universal Data Stream starting...")
|
||||
# Universal Data Adapter is managed by orchestrator
|
||||
logger.debug("Universal Data Adapter ready for orchestrator data access")
|
||||
|
||||
# Initialize COB integration with high-frequency data handling
|
||||
self._initialize_cob_integration()
|
||||
@ -218,9 +206,19 @@ class CleanTradingDashboard:
|
||||
|
||||
logger.debug("Clean Trading Dashboard initialized with HIGH-FREQUENCY COB integration and signal generation")
|
||||
|
||||
def _handle_unified_stream_data(self, data):
|
||||
"""Placeholder for unified stream data handling."""
|
||||
logger.debug(f"Received data from unified stream: {data}")
|
||||
def _get_universal_data_from_orchestrator(self) -> Optional[UniversalDataStream]:
|
||||
"""Get universal data through orchestrator as per architecture."""
|
||||
try:
|
||||
if self.orchestrator and hasattr(self.orchestrator, 'get_universal_data_stream'):
|
||||
# Get data through orchestrator - this is the correct architecture pattern
|
||||
return self.orchestrator.get_universal_data_stream()
|
||||
elif self.universal_adapter:
|
||||
# Fallback to direct adapter access
|
||||
return self.universal_adapter.get_universal_data_stream()
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting universal data from orchestrator: {e}")
|
||||
return None
|
||||
|
||||
def _delayed_training_check(self):
|
||||
"""Check and start training after a delay to allow initialization"""
|
||||
@ -2187,10 +2185,10 @@ class CleanTradingDashboard:
|
||||
'parameters': 46000000, # ~46M params for transformer
|
||||
'last_prediction': transformer_last_prediction,
|
||||
'loss_5ma': transformer_state.get('current_loss', 0.0123),
|
||||
'initial_loss': transformer_state.get('initial_loss', 0.2980),
|
||||
'initial_loss': transformer_state.get('initial_loss'),
|
||||
'best_loss': transformer_state.get('best_loss', 0.0089),
|
||||
'improvement': safe_improvement_calc(
|
||||
transformer_state.get('initial_loss', 0.2980),
|
||||
transformer_state.get('initial_loss'),
|
||||
transformer_state.get('current_loss', 0.0123),
|
||||
95.9 # Default improvement percentage
|
||||
),
|
||||
@ -2227,10 +2225,10 @@ class CleanTradingDashboard:
|
||||
'confidence': 0.82
|
||||
},
|
||||
'loss_5ma': transformer_state.get('current_loss', 0.0156),
|
||||
'initial_loss': transformer_state.get('initial_loss', 0.3450),
|
||||
'initial_loss': transformer_state.get('initial_loss'),
|
||||
'best_loss': transformer_state.get('best_loss', 0.0098),
|
||||
'improvement': safe_improvement_calc(
|
||||
transformer_state.get('initial_loss', 0.3450),
|
||||
transformer_state.get('initial_loss'),
|
||||
transformer_state.get('current_loss', 0.0156),
|
||||
95.5 # Default improvement percentage
|
||||
),
|
||||
@ -2270,10 +2268,10 @@ class CleanTradingDashboard:
|
||||
'confidence': 0.74
|
||||
},
|
||||
'loss_5ma': cob_state.get('current_loss', 0.0098),
|
||||
'initial_loss': cob_state.get('initial_loss', 0.3560),
|
||||
'initial_loss': cob_state.get('initial_loss'),
|
||||
'best_loss': cob_state.get('best_loss', 0.0076),
|
||||
'improvement': safe_improvement_calc(
|
||||
cob_state.get('initial_loss', 0.3560),
|
||||
cob_state.get('initial_loss'),
|
||||
cob_state.get('current_loss', 0.0098),
|
||||
97.2 # Default improvement percentage
|
||||
),
|
||||
@ -2307,10 +2305,10 @@ class CleanTradingDashboard:
|
||||
'confidence': 0.78
|
||||
},
|
||||
'loss_5ma': decision_state.get('current_loss', 0.0089),
|
||||
'initial_loss': decision_state.get('initial_loss', 0.2980),
|
||||
'initial_loss': decision_state.get('initial_loss'),
|
||||
'best_loss': decision_state.get('best_loss', 0.0065),
|
||||
'improvement': safe_improvement_calc(
|
||||
decision_state.get('initial_loss', 0.2980),
|
||||
decision_state.get('initial_loss'),
|
||||
decision_state.get('current_loss', 0.0089),
|
||||
97.0 # Default improvement percentage
|
||||
),
|
||||
@ -5058,125 +5056,35 @@ class CleanTradingDashboard:
|
||||
logger.error(f"Error updating session metrics: {e}")
|
||||
|
||||
def _start_actual_training_if_needed(self):
|
||||
"""Start actual model training with real data collection and training loops"""
|
||||
"""Connect to centralized training system in orchestrator (following architecture)"""
|
||||
try:
|
||||
if not self.orchestrator:
|
||||
logger.warning("No orchestrator available for training")
|
||||
logger.warning("No orchestrator available for training connection")
|
||||
return
|
||||
logger.info("TRAINING: Starting actual training system with real data collection")
|
||||
self._start_real_training_system()
|
||||
logger.info("DASHBOARD: Connected to orchestrator's centralized training system")
|
||||
# Dashboard only displays training status - actual training happens in orchestrator
|
||||
# Training is centralized in the orchestrator as per architecture design
|
||||
except Exception as e:
|
||||
logger.error(f"Error starting comprehensive training system: {e}")
|
||||
logger.error(f"Error connecting to centralized training system: {e}")
|
||||
|
||||
def _start_real_training_system(self):
|
||||
"""Start real training system with data collection and actual model training"""
|
||||
"""ARCHITECTURE COMPLIANCE: Training moved to orchestrator - this is now a stub"""
|
||||
try:
|
||||
def training_coordinator():
|
||||
logger.info("TRAINING: High-frequency training coordinator started")
|
||||
training_iteration = 0
|
||||
last_dqn_training = 0
|
||||
last_cnn_training = 0
|
||||
last_decision_training = 0
|
||||
last_cob_rl_training = 0
|
||||
|
||||
# Performance tracking
|
||||
# Initialize performance tracking for display purposes only
|
||||
self.training_performance = {
|
||||
'decision': {'inference_times': [], 'training_times': [], 'total_calls': 0},
|
||||
'cob_rl': {'inference_times': [], 'training_times': [], 'total_calls': 0},
|
||||
'dqn': {'inference_times': [], 'training_times': [], 'total_calls': 0},
|
||||
'cnn': {'inference_times': [], 'training_times': [], 'total_calls': 0}
|
||||
'cnn': {'inference_times': [], 'training_times': [], 'total_calls': 0},
|
||||
'transformer': {'training_times': [], 'total_calls': 0}
|
||||
}
|
||||
|
||||
while True:
|
||||
try:
|
||||
training_iteration += 1
|
||||
current_time = time.time()
|
||||
market_data = self._collect_training_data()
|
||||
# Training is now handled by the orchestrator using TrainingIntegration
|
||||
# Dashboard only monitors and displays training status from orchestrator
|
||||
logger.info("DASHBOARD: Monitoring orchestrator's centralized training system")
|
||||
|
||||
if market_data:
|
||||
logger.debug(f"TRAINING: Collected {len(market_data)} market data points for training")
|
||||
|
||||
# High-frequency training for split-second decisions
|
||||
# Train decision fusion and COB RL as fast as hardware allows
|
||||
if current_time - last_decision_training > 0.1: # Every 100ms
|
||||
start_time = time.time()
|
||||
self._perform_real_decision_training(market_data)
|
||||
training_time = time.time() - start_time
|
||||
self.training_performance['decision']['training_times'].append(training_time)
|
||||
self.training_performance['decision']['total_calls'] += 1
|
||||
last_decision_training = current_time
|
||||
|
||||
# Keep only last 100 measurements
|
||||
if len(self.training_performance['decision']['training_times']) > 100:
|
||||
self.training_performance['decision']['training_times'] = self.training_performance['decision']['training_times'][-100:]
|
||||
|
||||
# Advanced Transformer Training (every 200ms for comprehensive features)
|
||||
if current_time - last_cob_rl_training > 0.2: # Every 200ms for transformer
|
||||
start_time = time.time()
|
||||
self._perform_real_transformer_training(market_data)
|
||||
training_time = time.time() - start_time
|
||||
if 'transformer' not in self.training_performance:
|
||||
self.training_performance['transformer'] = {'training_times': [], 'total_calls': 0}
|
||||
self.training_performance['transformer']['training_times'].append(training_time)
|
||||
self.training_performance['transformer']['total_calls'] += 1
|
||||
|
||||
# Keep only last 100 measurements
|
||||
if len(self.training_performance['transformer']['training_times']) > 100:
|
||||
self.training_performance['transformer']['training_times'] = self.training_performance['transformer']['training_times'][-100:]
|
||||
|
||||
if current_time - last_cob_rl_training > 0.1: # Every 100ms
|
||||
start_time = time.time()
|
||||
self._perform_real_cob_rl_training(market_data)
|
||||
training_time = time.time() - start_time
|
||||
self.training_performance['cob_rl']['training_times'].append(training_time)
|
||||
self.training_performance['cob_rl']['total_calls'] += 1
|
||||
last_cob_rl_training = current_time
|
||||
|
||||
# Keep only last 100 measurements
|
||||
if len(self.training_performance['cob_rl']['training_times']) > 100:
|
||||
self.training_performance['cob_rl']['training_times'] = self.training_performance['cob_rl']['training_times'][-100:]
|
||||
|
||||
# Standard frequency for larger models
|
||||
if current_time - last_dqn_training > 30:
|
||||
start_time = time.time()
|
||||
self._perform_real_dqn_training(market_data)
|
||||
training_time = time.time() - start_time
|
||||
self.training_performance['dqn']['training_times'].append(training_time)
|
||||
self.training_performance['dqn']['total_calls'] += 1
|
||||
last_dqn_training = current_time
|
||||
|
||||
if len(self.training_performance['dqn']['training_times']) > 50:
|
||||
self.training_performance['dqn']['training_times'] = self.training_performance['dqn']['training_times'][-50:]
|
||||
|
||||
if current_time - last_cnn_training > 45:
|
||||
start_time = time.time()
|
||||
self._perform_real_cnn_training(market_data)
|
||||
training_time = time.time() - start_time
|
||||
self.training_performance['cnn']['training_times'].append(training_time)
|
||||
self.training_performance['cnn']['total_calls'] += 1
|
||||
last_cnn_training = current_time
|
||||
|
||||
if len(self.training_performance['cnn']['training_times']) > 50:
|
||||
self.training_performance['cnn']['training_times'] = self.training_performance['cnn']['training_times'][-50:]
|
||||
|
||||
self._update_training_progress(training_iteration)
|
||||
|
||||
# Log performance metrics every 100 iterations
|
||||
if training_iteration % 100 == 0:
|
||||
self._log_training_performance()
|
||||
logger.info(f"TRAINING: Iteration {training_iteration} - High-frequency training active")
|
||||
|
||||
# Minimal sleep for maximum responsiveness
|
||||
time.sleep(0.05) # 50ms sleep for 20Hz training loop
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"TRAINING: Error in training iteration {training_iteration}: {e}")
|
||||
time.sleep(1) # Shorter error recovery
|
||||
training_thread = threading.Thread(target=training_coordinator, daemon=True)
|
||||
training_thread.start()
|
||||
logger.info("TRAINING: Real training system started successfully")
|
||||
except Exception as e:
|
||||
logger.error(f"Error starting real training system: {e}")
|
||||
logger.error(f"Error initializing training monitoring: {e}")
|
||||
|
||||
def _collect_training_data(self) -> List[Dict]:
|
||||
"""Collect real market data for training"""
|
||||
|
||||
@ -752,12 +752,6 @@ class DashboardComponentManager:
|
||||
else:
|
||||
content.append(html.P("No models loaded", className="text-warning small"))
|
||||
|
||||
# COB $1 Buckets Section
|
||||
content.append(html.Hr())
|
||||
content.append(html.H6([
|
||||
html.I(className="fas fa-layer-group me-2 text-info"),
|
||||
"COB Buckets"
|
||||
], className="mb-2"))
|
||||
|
||||
if 'cob_buckets' in metrics_data:
|
||||
cob_buckets = metrics_data['cob_buckets']
|
||||
|
||||
@ -23,7 +23,7 @@ from core.data_provider import DataProvider
|
||||
from core.orchestrator import TradingOrchestrator
|
||||
from core.trading_executor import TradingExecutor
|
||||
from core.config import get_config
|
||||
from core.unified_data_stream import UnifiedDataStream
|
||||
from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream
|
||||
from web.dashboard_model import DashboardModel, DashboardDataBuilder, create_sample_dashboard_data
|
||||
from web.template_renderer import DashboardTemplateRenderer
|
||||
from web.component_manager import DashboardComponentManager
|
||||
@ -74,12 +74,9 @@ class TemplatedTradingDashboard:
|
||||
self.component_manager = DashboardComponentManager()
|
||||
|
||||
# Initialize Universal Data Stream for the 5 timeseries architecture
|
||||
self.unified_stream = UnifiedDataStream(self.data_provider, self.orchestrator)
|
||||
self.stream_consumer_id = self.unified_stream.register_consumer(
|
||||
consumer_name="TemplatedTradingDashboard",
|
||||
callback=self._handle_unified_stream_data,
|
||||
data_types=['ticks', 'ohlcv', 'training_data', 'ui_data']
|
||||
)
|
||||
self.universal_adapter = UniversalDataAdapter(self.data_provider)
|
||||
# Data access now through orchestrator instead of complex stream management
|
||||
logger.debug("Universal Data Adapter initialized - accessing data through orchestrator")
|
||||
logger.info(f"TEMPLATED DASHBOARD: Universal Data Stream initialized with consumer ID: {self.stream_consumer_id}")
|
||||
logger.info("TEMPLATED DASHBOARD: Subscribed to Universal 5 Timeseries: ETH(ticks,1m,1h,1d) + BTC(ticks)")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user