test6

app.py

@@ -115,12 +115,10 @@ def get_top_10_potential_stocks(period, selected_indices):
                 continue
                 
             ts_data = prepare_data_chronos(data)
-            
-            # 創建預測器，使用 'target' 作為目標列名
             predictor = TimeSeriesPredictor(
                 prediction_length=prediction_length,
-                freq="D",  # 使用 'D' 而不是 '1D'
-                target="target"  # 確保與數據框中的列名一致
+                freq="D",
+                target="target"
             )
             
             predictor.fit(
@@ -131,14 +129,25 @@ def get_top_10_potential_stocks(period, selected_indices):
             )
             
             predictions = predictor.predict(ts_data)
-            potential = (predictions.iloc[-1] - data['Close'].iloc[-1]) / data['Close'].iloc[-1]
-            stock_predictions.append((ticker, potential, data['Close'].iloc[-1], predictions.iloc[-1]))
+            
+            # 修改這部分以正確處理預測結果
+            last_actual = float(data['Close'].iloc[-1])
+            last_pred = float(predictions.iloc[-1].values[0])  # 確保取得數值
+            potential = (last_pred - last_actual) / last_actual
+            
+            stock_predictions.append((ticker, potential, last_actual, last_pred))
             
         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]
+    
+    # 確保所有值都是基本數據類型
+    top_10_stocks = sorted(
+        [(str(t), float(p), float(c), float(pred)) for t, p, c, pred in stock_predictions],
+        key=lambda x: x[1],
+        reverse=True
+    )[:10]
+    
     return top_10_stocks
 
 # Gradio interface function