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inference works

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This commit is contained in:
Dobromir Popov
2025-07-27 00:24:32 +03:00
parent 36a8e256a8
commit 13155197f8
6 changed files with 238 additions and 45 deletions
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37
core/orchestrator.py
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@ -332,7 +332,7 @@ class TradingOrchestrator:
self.model_states['dqn']['checkpoint_filename'] = 'none (fresh start)'
logger.info("DQN starting fresh - no checkpoint found")
logger.info(f"DQN Agent initialized: {state_size} state features, {action_size} actions")
logger.info(f"DQN Agent initialized: {actual_state_size} state features, {action_size} actions")
except ImportError:
logger.warning("DQN Agent not available")
self.rl_agent = None
@ -474,6 +474,7 @@ class TradingOrchestrator:
# CRITICAL: Register models with the model registry
logger.info("Registering models with model registry...")
logger.info(f"Model registry before registration: {len(self.model_registry.models)} models")
# Import model interfaces
# These are now imported at the top of the file
@ -482,8 +483,11 @@ class TradingOrchestrator:
if self.rl_agent:
try:
rl_interface = RLAgentInterface(self.rl_agent, name="dqn_agent")
self.register_model(rl_interface, weight=0.2)
logger.info("RL Agent registered successfully")
success = self.register_model(rl_interface, weight=0.2)
if success:
logger.info("RL Agent registered successfully")
else:
logger.error("Failed to register RL Agent - register_model returned False")
except Exception as e:
logger.error(f"Failed to register RL Agent: {e}")
@ -491,8 +495,11 @@ class TradingOrchestrator:
if self.cnn_model:
try:
cnn_interface = CNNModelInterface(self.cnn_model, name="enhanced_cnn")
self.register_model(cnn_interface, weight=0.25)
logger.info("CNN Model registered successfully")
success = self.register_model(cnn_interface, weight=0.25)
if success:
logger.info("CNN Model registered successfully")
else:
logger.error("Failed to register CNN Model - register_model returned False")
except Exception as e:
logger.error(f"Failed to register CNN Model: {e}")
@ -596,6 +603,8 @@ class TradingOrchestrator:
# Normalize weights after all registrations
self._normalize_weights()
logger.info(f"Current model weights: {self.model_weights}")
logger.info(f"Model registry after registration: {len(self.model_registry.models)} models")
logger.info(f"Registered models: {list(self.model_registry.models.keys())}")
except Exception as e:
logger.error(f"Error initializing ML models: {e}")
@ -2080,14 +2089,7 @@ class TradingOrchestrator:
# Store prediction in SQLite database for training
logger.debug(f"Added CNN prediction to database: {prediction}")
# Store CNN prediction as inference record
await self._store_inference_data_async(
model_name="enhanced_cnn",
model_input=base_data,
prediction=prediction,
timestamp=datetime.now(),
symbol=symbol
)
# Note: Inference data will be stored in main prediction loop to avoid duplication
except Exception as e:
logger.error(f"Error using CNN adapter: {e}")
@ -2139,14 +2141,7 @@ class TradingOrchestrator:
)
predictions.append(pred)
# Store CNN fallback prediction as inference record
await self._store_inference_data_async(
model_name=model.name,
model_input=base_data,
prediction=pred,
timestamp=datetime.now(),
symbol=symbol
)
# Note: Inference data will be stored in main prediction loop to avoid duplication
# Capture for dashboard
current_price = self._get_current_price(symbol)