wip
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@@ -3264,18 +3264,25 @@ class RealTrainingAdapter:
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else:
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logger.info(f"Live Signal (NOT executed): {signal['action']} @ {signal['price']:.2f} (conf: {signal['confidence']:.2f}) - {self._get_rejection_reason(session, signal)}")
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# Store prediction for visualization
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# Store prediction for visualization WITH predicted_candle data for ghost candles
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if self.orchestrator and hasattr(self.orchestrator, 'store_transformer_prediction'):
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self.orchestrator.store_transformer_prediction(symbol, {
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stored_prediction = {
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'timestamp': datetime.now(timezone.utc).isoformat(),
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'current_price': current_price,
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'predicted_price': current_price * (1.01 if prediction['action'] == 'BUY' else 0.99),
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'predicted_price': prediction.get('predicted_price', current_price * (1.01 if prediction['action'] == 'BUY' else 0.99)),
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'price_change': 1.0 if prediction['action'] == 'BUY' else -1.0,
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'confidence': prediction['confidence'],
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'action': prediction['action'],
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'horizon_minutes': 10,
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'source': 'live_inference'
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})
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}
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# Include predicted_candle for ghost candle visualization
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if 'predicted_candle' in prediction and prediction['predicted_candle']:
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stored_prediction['predicted_candle'] = prediction['predicted_candle']
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stored_prediction['next_candles'] = prediction['predicted_candle'] # Alias for compatibility
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logger.debug(f"Stored prediction with {len(prediction['predicted_candle'])} timeframe candles")
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self.orchestrator.store_transformer_prediction(symbol, stored_prediction)
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# Per-candle training mode
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if train_every_candle:
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