照官方文件改

app.py

@@ -12,26 +12,22 @@ def get_stock_data(ticker, period):
 # Function to prepare the data for Chronos-Bolt
 
 def prepare_data_chronos(data):
-    # 確保索引重置並重命名欄位
-    data = data.reset_index()
-    data = data.rename(columns={"Date": "timestamp", "Close": "target"})
+    # Convert to the correct format
+    df = data.reset_index()
+    df = df.rename(columns={
+        'Date': 'timestamp',
+        'Close': 'target',
+    })
     
-    # 只保留需要的欄位並設定正確的資料類型
-    data = data[["timestamp", "target"]]
-    data["item_id"] = "stock"
+    # Ensure correct data types
+    df['timestamp'] = pd.to_datetime(df['timestamp'])
+    df['target'] = df['target'].astype('float32')
+    df['item_id'] = 'stock'
     
-    # 設定正確的資料類型
-    data["timestamp"] = pd.to_datetime(data["timestamp"])
-    data["target"] = data["target"].astype('float32')
+    # Create TimeSeriesDataFrame directly
+    ts_df = TimeSeriesDataFrame(df)
     
-    # 建立 TimeSeriesDataFrame，只使用必要的參數
-    ts_data = TimeSeriesDataFrame.from_data_frame(
-        data,
-        id_column="item_id",
-        timestamp_column="timestamp"
-    )
-    
-    return ts_data
+    return ts_df
 
 
 # Function to fetch stock indices (you already defined these)
@@ -85,25 +81,37 @@ def get_top_10_potential_stocks(period, selected_indices):
 
     stock_predictions = []
     prediction_length = 10
-
+    
     for ticker in stock_list:
         try:
+            # Get stock data
             data = get_stock_data(ticker, period)
             if data.empty:
                 continue
-
+                
+            # Prepare data
             ts_data = prepare_data_chronos(data)
+            
+            # Create predictor and fit model
             predictor = TimeSeriesPredictor(prediction_length=prediction_length)
-            predictor.fit(ts_data, hyperparameters={"Chronos": {"model_path": "amazon/chronos-bolt-base"}})
-
+            predictor.fit(
+                ts_data,
+                hyperparameters={
+                    "Chronos": {"model_path": "autogluon/chronos-bolt-base"}
+                }
+            )
+            
+            # Make predictions
             predictions = predictor.predict(ts_data)
+            
+            # Calculate potential
             potential = (predictions.iloc[-1] - data['Close'].iloc[-1]) / data['Close'].iloc[-1]
             stock_predictions.append((ticker, potential, data['Close'].iloc[-1], predictions.iloc[-1]))
-
+            
         except Exception as e:
             print(f"Stock {ticker} error: {str(e)}")
             continue
-
+            
     top_10_stocks = sorted(stock_predictions, key=lambda x: x[1], reverse=True)[:10]
     return top_10_stocks