fix pred candles viz
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@@ -2143,7 +2143,7 @@ class RealTrainingAdapter:
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checkpoint_dir = "models/checkpoints/transformer"
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os.makedirs(checkpoint_dir, exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
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checkpoint_path = os.path.join(checkpoint_dir, f"transformer_epoch{epoch+1}_{timestamp}.pt")
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torch.save({
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@@ -2872,7 +2872,7 @@ class RealTrainingAdapter:
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checkpoint_dir = "models/checkpoints/transformer/realtime"
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os.makedirs(checkpoint_dir, exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
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checkpoint_type = "BEST" if improved else "periodic"
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checkpoint_path = os.path.join(checkpoint_dir, f"realtime_{checkpoint_type}_step{step}_{timestamp}.pt")
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@@ -3137,7 +3137,7 @@ class RealTrainingAdapter:
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if prediction:
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# Store signal
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signal = {
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'timestamp': datetime.now().isoformat(),
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'timestamp': datetime.now(timezone.utc).isoformat(),
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'symbol': symbol,
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'model': model_name,
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'action': prediction['action'],
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@@ -3158,7 +3158,7 @@ class RealTrainingAdapter:
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# Store prediction for visualization
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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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'timestamp': datetime.now(),
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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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'price_change': 1.0 if prediction['action'] == 'BUY' else -1.0,
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