Update model_predictor.py

model_predictor.py

@@ -58,82 +58,97 @@ class StockPredictor:
         self.scalers_path = 'scalers.npz'
 
     def fetch_yfinance_data(self, start_date='2022-09-12', end_date='2025-09-08'):
-        """從yfinance獲取股市數據"""
-        try:
-            # 台積電 (2330.TW) 作為主要目標股票
-            taiwan_stock = yf.Ticker('2330.TW')
-            taiwan_data = taiwan_stock.history(start=start_date, end=end_date)
-
-            if taiwan_data.empty:
-                print("警告: 無法獲取台灣股市數據")
-                return None
+    """從yfinance獲取股市數據"""
+    try:
+        # 台積電 (2330.TW) 作為主要目標股票
+        taiwan_stock = yf.Ticker('2330.TW')
+        taiwan_data = taiwan_stock.history(start=start_date, end=end_date)
+        
+        # 新增: 移除時區，使索引為 tz-naive
+        taiwan_data.index = taiwan_data.index.tz_localize(None)
+
+        if taiwan_data.empty:
+            print("警告: 無法獲取台灣股市數據")
+            return None
 
-            # 獲取美國市場數據
-            symbols = {
-                'DJI': '^DJI',
-                'NAS': '^IXIC',
-                'SOX': '^SOX',
-                'S&P_500': '^GSPC',
-                'TSM_ADR': 'TSM'
-            }
+        # 獲取美國市場數據
+        symbols = {
+            'DJI': '^DJI',
+            'NAS': '^IXIC',
+            'SOX': '^SOX',
+            'S&P_500': '^GSPC',
+            'TSM_ADR': 'TSM'
+        }
 
-            us_data = {}
-            for name, symbol in symbols.items():
-                try:
-                    ticker = yf.Ticker(symbol)
-                    data = ticker.history(start=start_date, end=end_date)
-                    if not data.empty:
-                        us_data[name] = data['Close']
-                    else:
-                        print(f"警告: 無法獲取 {name} 數據")
-                except Exception as e:
-                    print(f"獲取 {name} 數據時發生錯誤: {e}")
+        us_data = {}
+        for name, symbol in symbols.items():
+            try:
+                ticker = yf.Ticker(symbol)
+                data = ticker.history(start=start_date, end=end_date)
+                
+                # 新增: 移除時區，使索引為 tz-naive
+                data.index = data.index.tz_localize(None)
+                
+                if not data.empty:
+                    us_data[name] = data['Close']
+                else:
+                    print(f"警告: 無法獲取 {name} 數據")
+            except Exception as e:
+                print(f"獲取 {name} 數據時發生錯誤: {e}")
 
-            # 合併數據
-            main_df = pd.DataFrame(index=taiwan_data.index)
-            main_df['close'] = taiwan_data['Close']
-            main_df['volume'] = taiwan_data['Volume']
+        # 合併數據
+        main_df = pd.DataFrame(index=taiwan_data.index)  # 現在 index 已 tz-naive
+        main_df['close'] = taiwan_data['Close']
+        main_df['volume'] = taiwan_data['Volume']
 
-            # 計算報酬率
-            main_df['rate'] = main_df['close'].pct_change()
+        # 計算報酬率
+        main_df['rate'] = main_df['close'].pct_change()
 
-            # 添加美國市場數據
-            for name, data in us_data.items():
-                # 重新索引以匹配台灣股市交易日
-                main_df[name] = data.reindex(main_df.index, method='ffill')
+        # 添加美國市場數據
+        for name, data in us_data.items():
+            # 重新索引以匹配台灣股市交易日
+            main_df[name] = data.reindex(main_df.index, method='ffill')
 
-            return main_df
+        return main_df
 
-        except Exception as e:
-            print(f"獲取yfinance數據時發生錯誤: {e}")
-            return None
+    except Exception as e:
+        print(f"獲取yfinance數據時發生錯誤: {e}")
+        return None
 
     def load_external_data(self):
-        """載入外部經濟數據"""
-        business_climate = pd.DataFrame()
-        pmi_data = pd.DataFrame()
+    """載入外部經濟數據"""
+    business_climate = pd.DataFrame()
+    pmi_data = pd.DataFrame()
 
-        # 載入景氣燈號數據
-        try:
-            if os.path.exists('business_climate.csv'):
-                business_climate = pd.read_csv('business_climate.csv')
-                business_climate['Date'] = pd.to_datetime(business_climate['Date'])
-                business_climate.set_index('Date', inplace=True)
-                print("成功載入景氣燈號數據")
-        except Exception as e:
-            print(f"載入景氣燈號數據失敗: {e}")
+    # 載入景氣燈號數據
+    try:
+        if os.path.exists('business_climate.csv'):
+            business_climate = pd.read_csv('business_climate.csv')
+            business_climate['Date'] = pd.to_datetime(business_climate['Date'])
+            business_climate.set_index('Date', inplace=True)
+            
+            # 新增: 確保索引為 tz-naive
+            business_climate.index = business_climate.index.tz_localize(None)
+            
+            print("成功載入景氣燈號數據")
+    except Exception as e:
+        print(f"載入景氣燈號數據失敗: {e}")
 
-        # 載入PMI數據
-        try:
-            if os.path.exists('taiwan_pmi.csv'):
-                pmi_data = pd.read_csv('taiwan_pmi.csv')
-                pmi_data['Date'] = pd.to_datetime(pmi_data['Date'])
-                pmi_data.set_index('Date', inplace=True)
-                print("成功載入PMI數據")
-        except Exception as e:
-            print(f"載入PMI數據失敗: {e}")
+    # 載入PMI數據
+    try:
+        if os.path.exists('taiwan_pmi.csv'):
+            pmi_data = pd.read_csv('taiwan_pmi.csv')
+            pmi_data['Date'] = pd.to_datetime(pmi_data['Date'])
+            pmi_data.set_index('Date', inplace=True)
+            
+            # 新增: 確保索引為 tz-naive
+            pmi_data.index = pmi_data.index.tz_localize(None)
+            
+            print("成功載入PMI數據")
+    except Exception as e:
+        print(f"載入PMI數據失敗: {e}")
 
-        return business_climate, pmi_data
+    return business_climate, pmi_data
 
     def calculate_technical_indicators(self, df):
         """計算技術指標"""