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training fixes and enhancements wip

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This commit is contained in:
Dobromir Popov
2025-07-14 10:00:42 +03:00
parent e76b1b16dc
commit e74f1393c4
6 changed files with 378 additions and 99 deletions
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31
core/orchestrator.py
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@@ -437,13 +437,34 @@ class TradingOrchestrator:
def predict(self, data=None):
try:
# ExtremaTrainer provides context features, not a direct prediction
# We assume 'data' here is the 'symbol' string to pass to get_context_features_for_model
if not isinstance(data, str):
logger.warning(f"ExtremaTrainerInterface.predict received non-string data: {type(data)}. Cannot get context features.")
# Handle different data types that might be passed to ExtremaTrainer
symbol = None
if isinstance(data, str):
# Direct symbol string
symbol = data
elif isinstance(data, dict):
# Dictionary with symbol information
symbol = data.get('symbol')
elif isinstance(data, np.ndarray):
# Numpy array - extract symbol from metadata or use default
# For now, use the first symbol from the model's symbols list
if hasattr(self.model, 'symbols') and self.model.symbols:
symbol = self.model.symbols[0]
else:
symbol = 'ETH/USDT' # Default fallback
else:
# Unknown data type - use default symbol
if hasattr(self.model, 'symbols') and self.model.symbols:
symbol = self.model.symbols[0]
else:
symbol = 'ETH/USDT' # Default fallback
if not symbol:
logger.warning(f"ExtremaTrainerInterface.predict could not determine symbol from data: {type(data)}")
return None
features = self.model.get_context_features_for_model(symbol=data)
features = self.model.get_context_features_for_model(symbol=symbol)
if features is not None and features.size > 0:
# The presence of features indicates a signal. We'll return a generic HOLD
# with a neutral confidence. This can be refined if ExtremaTrainer provides

8
core/training_integration.py
View File

@@ -134,8 +134,8 @@ class TrainingIntegration:
# Store experience in DQN memory
dqn_agent = self.orchestrator.dqn_agent
if hasattr(dqn_agent, 'store_experience'):
dqn_agent.store_experience(
if hasattr(dqn_agent, 'remember'):
dqn_agent.remember(
state=np.array(dqn_state),
action=action_idx,
reward=reward,
@@ -145,7 +145,7 @@ class TrainingIntegration:
# Trigger training if enough experiences
if hasattr(dqn_agent, 'replay') and len(getattr(dqn_agent, 'memory', [])) > 32:
dqn_agent.replay(batch_size=32)
dqn_agent.replay()
logger.info("DQN training step completed")
return True
@@ -345,7 +345,7 @@ class TrainingIntegration:
# Perform training step if agent has replay method
if hasattr(cob_rl_agent, 'replay') and hasattr(cob_rl_agent, 'memory'):
if len(cob_rl_agent.memory) > 32: # Enough samples to train
loss = cob_rl_agent.replay(batch_size=min(32, len(cob_rl_agent.memory)))
loss = cob_rl_agent.replay()
if loss is not None:
logger.info(f"COB RL trained on trade outcome: P&L=${pnl:.2f}, loss={loss:.4f}")
return True