Update app.py

app.py

@@ -533,7 +533,7 @@ app.layout = html.Div([
     })
 ])
 
-# 台指期獨立預測回調函數
+# 台指期獨立預測回調函數 (新版本)
 @app.callback(
     [dash.dependencies.Output('taiex-prediction-results', 'children'),
      dash.dependencies.Output('taiex-prediction-chart', 'figure')],
@@ -545,17 +545,41 @@ def update_taiex_prediction(predict_days):
     if data.empty:
         return html.Div("無法獲取台指期資料"), {}
 
-    # 執行預測
-    prediction = simple_lstm_predict(data, predict_days)
-    if prediction is None:
+    # 執行最終日的預測，用於顯示在結果卡片上
+    final_prediction = simple_lstm_predict(data, predict_days)
+    if final_prediction is None:
         return html.Div("資料不足，無法進行預測"), {}
 
     current_price = data['Close'].iloc[-1]
-    predicted_price = prediction['predicted_price']
-    change_pct = prediction['change_pct']
-    confidence = prediction['confidence']
+    last_date = data.index[-1]
+    predicted_price = final_prediction['predicted_price']
+    change_pct = final_prediction['change_pct']
+    confidence = final_prediction['confidence']
+
+    # --- 主要修改處：計算預測路徑 ---
+    # 1. 定義不同預測天期所包含的中間節點
+    prediction_paths = {
+        1: [1],
+        5: [1, 5],
+        10: [1, 5, 10],
+        20: [1, 10, 20],
+        60: [1, 10, 20, 60]
+    }
+    intervals_to_predict = prediction_paths.get(predict_days, [predict_days])
+
+    # 2. 準備儲存預測路徑的座標點 (起始點為目前價格)
+    prediction_dates = [last_date]
+    prediction_prices = [current_price]
+
+    # 3. 循環計算路徑上每個點的預測值
+    for days in intervals_to_predict:
+        interim_prediction = simple_lstm_predict(data, days)
+        if interim_prediction:
+            prediction_dates.append(last_date + timedelta(days=days))
+            prediction_prices.append(interim_prediction['predicted_price'])
+    # --- 修改結束 ---
 
-    # 預測結果卡片
+    # 預測結果卡片 (維持不變)
     color = '#00C851' if change_pct >= 0 else '#FF4444'
     arrow = '📈' if change_pct >= 0 else '📉'
 
@@ -592,16 +616,17 @@ def update_taiex_prediction(predict_days):
         line=dict(color='#FFA726', width=2)
     ))
 
-    # 預測點
-    future_date = recent_data.index[-1] + timedelta(days=predict_days)
+    # --- 修改處：使用新的座標點繪製預測線 ---
+    # 4. 繪製由多個預測點連接而成的路徑
     fig.add_trace(go.Scatter(
-        x=[recent_data.index[-1], future_date],
-        y=[current_price, predicted_price],
+        x=prediction_dates,  # 使用包含多個日期的列表
+        y=prediction_prices, # 使用包含多個預測價格的列表
         mode='lines+markers',
-        name=f'{predict_days}日預測',
+        name=f'{predict_days}日預測路徑',
         line=dict(color=color, width=3, dash='dash'),
         marker=dict(size=8)
     ))
+    # --- 修改結束 ---
 
     fig.update_layout(
         title=f'台指期 {predict_days}日預測走勢',