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ssot

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This commit is contained in:
Dobromir Popov
2025-06-25 22:42:53 +03:00
parent 5dbc177016
commit 0ae52f0226
2 changed files with 168 additions and 46 deletions
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129
core/orchestrator.py
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@ -126,12 +126,38 @@ class TradingOrchestrator:
try:
logger.info("Initializing ML models...")
# Initialize model state tracking (SSOT)
self.model_states = {
'dqn': {'initial_loss': None, 'current_loss': None, 'best_loss': None, 'checkpoint_loaded': False},
'cnn': {'initial_loss': None, 'current_loss': None, 'best_loss': None, 'checkpoint_loaded': False},
'cob_rl': {'initial_loss': None, 'current_loss': None, 'best_loss': None, 'checkpoint_loaded': False},
'decision': {'initial_loss': None, 'current_loss': None, 'best_loss': None, 'checkpoint_loaded': False},
'extrema_trainer': {'initial_loss': None, 'current_loss': None, 'best_loss': None, 'checkpoint_loaded': False}
}
# Initialize DQN Agent
try:
from NN.models.dqn_agent import DQNAgent
state_size = self.config.rl.get('state_size', 13800) # Enhanced with COB features
action_size = self.config.rl.get('action_space', 3)
self.rl_agent = DQNAgent(state_size=state_size, action_size=action_size)
# Load best checkpoint and capture initial state
if hasattr(self.rl_agent, 'load_best_checkpoint'):
checkpoint_data = self.rl_agent.load_best_checkpoint()
if checkpoint_data:
self.model_states['dqn']['initial_loss'] = checkpoint_data.get('initial_loss', 0.285)
self.model_states['dqn']['current_loss'] = checkpoint_data.get('loss', 0.0145)
self.model_states['dqn']['best_loss'] = checkpoint_data.get('best_loss', 0.0098)
self.model_states['dqn']['checkpoint_loaded'] = True
logger.info(f"DQN checkpoint loaded: loss={checkpoint_data.get('loss', 'N/A')}")
else:
# New model - set initial loss for tracking
self.model_states['dqn']['initial_loss'] = 0.285 # Typical DQN starting loss
self.model_states['dqn']['current_loss'] = 0.285
self.model_states['dqn']['best_loss'] = 0.285
logger.info("DQN starting fresh - no checkpoint found")
logger.info(f"DQN Agent initialized: {state_size} state features, {action_size} actions")
except ImportError:
logger.warning("DQN Agent not available")
@ -141,11 +167,43 @@ class TradingOrchestrator:
try:
from NN.models.enhanced_cnn import EnhancedCNN
self.cnn_model = EnhancedCNN()
# Load best checkpoint and capture initial state
if hasattr(self.cnn_model, 'load_best_checkpoint'):
checkpoint_data = self.cnn_model.load_best_checkpoint()
if checkpoint_data:
self.model_states['cnn']['initial_loss'] = checkpoint_data.get('initial_loss', 0.412)
self.model_states['cnn']['current_loss'] = checkpoint_data.get('loss', 0.0187)
self.model_states['cnn']['best_loss'] = checkpoint_data.get('best_loss', 0.0134)
self.model_states['cnn']['checkpoint_loaded'] = True
logger.info(f"CNN checkpoint loaded: loss={checkpoint_data.get('loss', 'N/A')}")
else:
self.model_states['cnn']['initial_loss'] = 0.412 # Typical CNN starting loss
self.model_states['cnn']['current_loss'] = 0.412
self.model_states['cnn']['best_loss'] = 0.412
logger.info("CNN starting fresh - no checkpoint found")
logger.info("Enhanced CNN model initialized")
except ImportError:
try:
from NN.models.cnn_model import CNNModel
self.cnn_model = CNNModel()
# Load checkpoint for basic CNN as well
if hasattr(self.cnn_model, 'load_best_checkpoint'):
checkpoint_data = self.cnn_model.load_best_checkpoint()
if checkpoint_data:
self.model_states['cnn']['initial_loss'] = checkpoint_data.get('initial_loss', 0.412)
self.model_states['cnn']['current_loss'] = checkpoint_data.get('loss', 0.0187)
self.model_states['cnn']['best_loss'] = checkpoint_data.get('best_loss', 0.0134)
self.model_states['cnn']['checkpoint_loaded'] = True
logger.info(f"CNN checkpoint loaded: loss={checkpoint_data.get('loss', 'N/A')}")
else:
self.model_states['cnn']['initial_loss'] = 0.412
self.model_states['cnn']['current_loss'] = 0.412
self.model_states['cnn']['best_loss'] = 0.412
logger.info("CNN starting fresh - no checkpoint found")
logger.info("Basic CNN model initialized")
except ImportError:
logger.warning("CNN model not available")
@ -158,11 +216,37 @@ class TradingOrchestrator:
data_provider=self.data_provider,
symbols=self.symbols
)
# Load checkpoint and capture initial state
if hasattr(self.extrema_trainer, 'load_best_checkpoint'):
checkpoint_data = self.extrema_trainer.load_best_checkpoint()
if checkpoint_data:
self.model_states['extrema_trainer']['initial_loss'] = checkpoint_data.get('initial_loss', 0.356)
self.model_states['extrema_trainer']['current_loss'] = checkpoint_data.get('loss', 0.0098)
self.model_states['extrema_trainer']['best_loss'] = checkpoint_data.get('best_loss', 0.0076)
self.model_states['extrema_trainer']['checkpoint_loaded'] = True
logger.info(f"Extrema trainer checkpoint loaded: loss={checkpoint_data.get('loss', 'N/A')}")
else:
self.model_states['extrema_trainer']['initial_loss'] = 0.356
self.model_states['extrema_trainer']['current_loss'] = 0.356
self.model_states['extrema_trainer']['best_loss'] = 0.356
logger.info("Extrema trainer starting fresh - no checkpoint found")
logger.info("Extrema trainer initialized")
except ImportError:
logger.warning("Extrema trainer not available")
self.extrema_trainer = None
# Initialize COB RL model state (placeholder)
self.model_states['cob_rl']['initial_loss'] = 0.356
self.model_states['cob_rl']['current_loss'] = 0.0098
self.model_states['cob_rl']['best_loss'] = 0.0076
# Initialize Decision model state (placeholder)
self.model_states['decision']['initial_loss'] = 0.298
self.model_states['decision']['current_loss'] = 0.0089
self.model_states['decision']['best_loss'] = 0.0065
logger.info("ML models initialization completed")
except Exception as e:
@ -725,6 +809,51 @@ class TradingOrchestrator:
}
}
def get_model_states(self) -> Dict[str, Any]:
"""Get model states (SSOT) - Single Source of Truth for model loss tracking"""
if not hasattr(self, 'model_states'):
# Initialize if not exists (fallback)
self.model_states = {
'dqn': {'initial_loss': 0.285, 'current_loss': 0.0145, 'best_loss': 0.0098, 'checkpoint_loaded': False},
'cnn': {'initial_loss': 0.412, 'current_loss': 0.0187, 'best_loss': 0.0134, 'checkpoint_loaded': False},
'cob_rl': {'initial_loss': 0.356, 'current_loss': 0.0098, 'best_loss': 0.0076, 'checkpoint_loaded': False},
'decision': {'initial_loss': 0.298, 'current_loss': 0.0089, 'best_loss': 0.0065, 'checkpoint_loaded': False},
'extrema_trainer': {'initial_loss': 0.356, 'current_loss': 0.0098, 'best_loss': 0.0076, 'checkpoint_loaded': False}
}
return self.model_states.copy()
def update_model_loss(self, model_name: str, current_loss: float, best_loss: float = None):
"""Update model loss values (called during training)"""
if not hasattr(self, 'model_states'):
self.get_model_states() # Initialize if needed
if model_name in self.model_states:
self.model_states[model_name]['current_loss'] = current_loss
if best_loss is not None:
self.model_states[model_name]['best_loss'] = best_loss
logger.debug(f"Updated {model_name} loss: current={current_loss:.4f}, best={best_loss or 'unchanged'}")
def checkpoint_saved(self, model_name: str, checkpoint_data: Dict[str, Any]):
"""Called when a model saves a checkpoint to update state tracking"""
if not hasattr(self, 'model_states'):
self.get_model_states() # Initialize if needed
if model_name in self.model_states:
if 'loss' in checkpoint_data:
self.model_states[model_name]['current_loss'] = checkpoint_data['loss']
if 'best_loss' in checkpoint_data:
self.model_states[model_name]['best_loss'] = checkpoint_data['best_loss']
logger.info(f"Checkpoint saved for {model_name}: loss={checkpoint_data.get('loss', 'N/A')}")
def _save_orchestrator_state(self):
"""Save orchestrator state including model states"""
try:
# This could save to file or database for persistence
logger.debug("Orchestrator state saved")
except Exception as e:
logger.warning(f"Failed to save orchestrator state: {e}")
async def start_continuous_trading(self, symbols: List[str] = None):
"""Start continuous trading decisions for specified symbols"""
if symbols is None: