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ACTUAL TRAINING WORKING (WIP)

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This commit is contained in:
Dobromir Popov
2025-07-25 14:08:25 +03:00
parent 1f60c80d67
commit 26eeb9b35b
1 changed files with 10 additions and 4 deletions
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14
core/orchestrator.py
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@ -1107,7 +1107,7 @@ class TradingOrchestrator:
if model_input is not None and cnn_predictions:
# Store inference data for each CNN prediction
for cnn_pred in cnn_predictions:
await self._store_inference_data_async(model_name, model_input, cnn_pred, current_time)
await self._store_inference_data_async(model_name, model_input, cnn_pred, current_time, symbol)
elif isinstance(model, RLAgentInterface):
# Get RL prediction
@ -1118,7 +1118,7 @@ class TradingOrchestrator:
# Store input data for RL
model_input = input_data.get('rl_input')
if model_input is not None:
await self._store_inference_data_async(model_name, model_input, prediction, current_time)
await self._store_inference_data_async(model_name, model_input, prediction, current_time, symbol)
else:
# Generic model interface
@ -1129,7 +1129,7 @@ class TradingOrchestrator:
# Store input data for generic model
model_input = input_data.get('generic_input')
if model_input is not None:
await self._store_inference_data_async(model_name, model_input, prediction, current_time)
await self._store_inference_data_async(model_name, model_input, prediction, current_time, symbol)
except Exception as e:
logger.error(f"Error getting prediction from {model_name}: {e}")
@ -1206,15 +1206,21 @@ class TradingOrchestrator:
logger.error(f"Error collecting standardized model input data: {e}")
return {}
async def _store_inference_data_async(self, model_name: str, model_input: Any, prediction: Prediction, timestamp: datetime):
async def _store_inference_data_async(self, model_name: str, model_input: Any, prediction: Prediction, timestamp: datetime, symbol: str = None):
"""Store inference data per-model with async file operations and memory optimization"""
try:
# Only log first few inference records to avoid spam
if len(self.inference_history.get(model_name, [])) < 3:
logger.debug(f"Storing inference data for {model_name}: {prediction.action} (confidence: {prediction.confidence:.3f})")
# Extract symbol from prediction if not provided
if symbol is None:
symbol = getattr(prediction, 'symbol', 'ETH/USDT') # Default to ETH/USDT if not available
# Create comprehensive inference record
inference_record = {
'timestamp': timestamp.isoformat(),
'symbol': symbol,
'model_name': model_name,
'model_input': model_input,
'prediction': {