Update app.py

app.py

@@ -854,7 +854,7 @@ def simple_statistical_predict(data, predict_days=5):
 
 def calculate_new_features(df):
     """
-    計算新的技術指標特徵 - 針對新特徵需求
+    計算新的技術指標特徵 - 針對新特徵需求 (已修正)
     """
     if df.empty:
         return df
@@ -868,49 +868,26 @@ def calculate_new_features(df):
     # 3. MA5_close – 5 日移動平均價
     df['MA5_close'] = df['Close'].rolling(window=5).mean()
     
-    
-    # 5. volatility_5d – 5 日報酬標準差（短期波動）
+    # 4. volatility_5d – 5 日報酬標準差（短期波動）
     df['volatility_5d'] = df['return_t-1'].rolling(window=5).std()
     
-    # 6. volume_ratio_5d – 今日成交量 ÷ 5 日均量
+    # 5. volume_ratio_5d – 今日成交量 ÷ 5 日均量
+    # Note: Use 'Volume' with a capital V
     df['volume_5d_avg'] = df['Volume'].rolling(window=5).mean()
     df['volume_ratio_5d'] = df['Volume'] / df['volume_5d_avg']
     
-    # 7. RSI_14 – 14 日 RSI 指標
-    delta = df['Close'].diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
-    rs = gain / loss
-    df['RSI_14'] = 100 - (100 / (1 + rs))
-    
-    # 8. MACD_diff – MACD - signal（趨勢強弱）
-    exp1 = df['Close'].ewm(span=12).mean()
-    exp2 = df['Close'].ewm(span=26).mean()
+    # 6. MACD_diff – MACD - signal（趨勢強弱）
+    # This is already calculated as 'MACD_Histogram' in calculate_technical_indicators, but we'll recalculate to be safe.
+    exp1 = df['Close'].ewm(span=12, adjust=False).mean()
+    exp2 = df['Close'].ewm(span=26, adjust=False).mean()
     macd_line = exp1 - exp2
-    signal_line = macd_line.ewm(span=9).mean()
+    signal_line = macd_line.ewm(span=9, adjust=False).mean()
     df['MACD_diff'] = macd_line - signal_line
 
-    # 9. MACDvol
-    df['MACDvol'] = df['MACDvol']
-
-    # 10. RSI_14 – 14 日 RSI 指標 (如果原資料沒有RSI，需要計算)
-    if 'RSI' in df.columns:
-        df['RSI_14'] = df['RSI']  # 使用現有的RSI
-    else:
-        # 計算 RSI
-        delta = df['close'].diff()
-        gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
-        loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
-        rs = gain / loss
-        df['RSI_14'] = 100 - (100 / (1 + rs))
-        
-    # 14. ADX
-    df['ADX'] = df['ADX']
+    # 7. volume_weighted_return - 成交量加權報酬率
+    # 【【ERROR FIX】】 Use 'Volume' with a capital V instead of 'volume'
+    df['volume_weighted_return'] = abs(df['return_t-1']) * df['Volume']
 
-
-    # 15. 當日報酬率絕對值 × 當日成交量 (成交量加權報酬率)
-    df['volume_weighted_return'] = abs(df['return_t-1']) * df['volume']
-    
     # 移除輔助欄位
     if 'volume_5d_avg' in df.columns:
         df = df.drop('volume_5d_avg', axis=1)
@@ -937,10 +914,10 @@ def advanced_xgboost_predict(predict_days=5):
         
         
         # 新增 volume_weighted_return 計算
-        if 'return_t-1' in taiex_data.columns and 'Volume' in taiex_data.columns:
-             taiex_data['volume_weighted_return'] = abs(taiex_data['return_t-1']) * taiex_data['Volume']
-        else:
-             taiex_data['volume_weighted_return'] = 0
+        # if 'return_t-1' in taiex_data.columns and 'Volume' in taiex_data.columns:
+        #      taiex_data['volume_weighted_return'] = abs(taiex_data['return_t-1']) * taiex_data['Volume']
+        # else:
+        #      taiex_data['volume_weighted_return'] = 0
 
         print("正在獲取美股數據...")
         us_market_data = get_us_market_data()