add print

model.py

@@ -114,11 +114,18 @@ def train_model(timeframe):
     price_data = pd.read_csv(training_price_data_path)
     df = load_frame(price_data, timeframe)
 
+
+    if df.empty:
+        raise ValueError("No data available after loading and formatting. Check the data source or timeframe.")
+
     print(df.tail())
 
     y_train = df['close'].shift(-1).dropna().values
     X_train = df[:-1]
 
+    if X_train.empty or len(y_train) == 0:
+        raise ValueError("Training data is empty. Ensure there is enough data for training.")
+
     print(f"Training data shape: {X_train.shape}, {y_train.shape}")
 
     # Define the model