bug3

app.py

@@ -12,22 +12,33 @@ def get_stock_data(ticker, period):
 # Function to prepare the data for Chronos-Bolt
 
 def prepare_data_chronos(data):
-    # Convert to the correct format
+    # 重設索引並準備數據
     df = data.reset_index()
-    df = df.rename(columns={
-        'Date': 'timestamp',
-        'Close': 'target',
-    })
     
-    # Ensure correct data types
-    df['timestamp'] = pd.to_datetime(df['timestamp'])
-    df['target'] = df['target'].astype('float32')
-    df['item_id'] = 'stock'
+    # 創建符合官方格式的數據框
+    formatted_df = pd.DataFrame({
+        'item_id': ['stock'] * len(df),
+        'timestamp': pd.to_datetime(df['Date']),
+        'value': df['Close'].astype('float32')  # 使用 'value' 而不是 'target'
+    })
     
-    # Create TimeSeriesDataFrame directly
-    ts_df = TimeSeriesDataFrame(df)
+    print("Data types:", formatted_df.dtypes)
+    print("Sample data:", formatted_df.head())
+    # 按照時間戳排序
+    formatted_df = formatted_df.sort_values('timestamp')
     
-    return ts_df
+    try:
+        # 使用 from_data_frame 方法創建 TimeSeriesDataFrame
+        ts_df = TimeSeriesDataFrame.from_data_frame(
+            formatted_df,
+            id_column='item_id',
+            timestamp_column='timestamp',
+            target_column='value'
+        )
+        return ts_df
+    except Exception as e:
+        print(f"Error creating TimeSeriesDataFrame: {str(e)}")
+        raise
 
 
 # Function to fetch stock indices (you already defined these)
@@ -84,16 +95,21 @@ def get_top_10_potential_stocks(period, selected_indices):
     
     for ticker in stock_list:
         try:
-            # Get stock data
+            # 獲取股票數據
             data = get_stock_data(ticker, period)
             if data.empty:
                 continue
                 
-            # Prepare data
+            # 準備數據
             ts_data = prepare_data_chronos(data)
             
-            # Create predictor and fit model
-            predictor = TimeSeriesPredictor(prediction_length=prediction_length)
+            # 創建預測器
+            predictor = TimeSeriesPredictor(
+                prediction_length=prediction_length,
+                target='value'  # 指定目標列名
+            )
+            
+            # 訓練模型
             predictor.fit(
                 ts_data,
                 hyperparameters={
@@ -101,10 +117,10 @@ def get_top_10_potential_stocks(period, selected_indices):
                 }
             )
             
-            # Make predictions
+            # 進行預測
             predictions = predictor.predict(ts_data)
             
-            # Calculate potential
+            # 計算潛力
             potential = (predictions.iloc[-1] - data['Close'].iloc[-1]) / data['Close'].iloc[-1]
             stock_predictions.append((ticker, potential, data['Close'].iloc[-1], predictions.iloc[-1]))