Update app.py

app.py

@@ -219,24 +219,30 @@ def get_exchange_rate():
         return 31.5
 
 def simple_statistical_predict(data, predict_days=5):
-    """【備用模型】簡化的統計預測模型。"""
+    """【備用模型】簡化的統計預測模型 - 更新為輸出漲幅百分比格式。"""
     if len(data) < 60: 
         return None
         
     prices = data['Close'].values
+    current_price = prices[-1]
+    
     ma_short = np.mean(prices[-5:])
     ma_medium = np.mean(prices[-20:])
     ma_long = np.mean(prices[-60:])
     recent_trend = np.polyfit(range(20), prices[-20:], 1)[0]
     volatility = np.std(prices[-20:]) / np.mean(prices[-20:])
+    
     base_change = recent_trend * predict_days
     trend_factor = 1.0 + (0.02 if ma_short > ma_medium > ma_long else -0.02 if ma_short < ma_medium < ma_long else 0)
     noise_factor = np.random.normal(1, volatility * 0.1)
-    predicted_price = prices[-1] * trend_factor + base_change + (prices[-1] * noise_factor * 0.01)
-    change_pct = ((predicted_price - prices[-1]) / prices[-1]) * 100
+    predicted_price = current_price * trend_factor + base_change + (current_price * noise_factor * 0.01)
+    
+    # 【重要更新】計算漲幅百分比
+    change_pct = ((predicted_price - current_price) / current_price) * 100
+    
     return {
         'predicted_price': predicted_price, 
-        'change_pct': change_pct, 
+        'change_pct': change_pct,  # 現在這個值是真正的漲幅百分比
         'confidence': max(0.6, 1 - volatility * 2)
     }
 
@@ -289,9 +295,10 @@ def calculate_new_features(df):
 def advanced_xgboost_predict(predict_days=5):
     """
     【進階模型】使用 XGBoost 模型進行預測 - 新特徵版本
+    【重要更新】現在輸出漲幅百分比而非絕對價格
     """
     try:
-        print(f"開始使用 XGBoost 模型進行 {predict_days} 天預測（新特徵版本）...")
+        print(f"開始使用 XGBoost 模型進行 {predict_days} 天預測（漲幅百分比版本）...")
         
         # 初始化 XGBoost 模型
         xgb_model = XGBoostModel()
@@ -328,7 +335,7 @@ def advanced_xgboost_predict(predict_days=5):
         # 取得昨日收盤價
         yesterday_close = latest_data['Close']
 
-        # 【【【修正】】】特徵列表，確保與模型訓練時完全一致
+        # 特徵列表，確保與模型訓練時完全一致
         new_feature_columns = [
             'return_t-1',
             'return_t-5',
@@ -383,7 +390,7 @@ def advanced_xgboost_predict(predict_days=5):
                 
                 feature_names.append(feature)
 
-        # 【【【修正】】】 按照模型訓練的順序添加剩餘特徵
+        # 按照模型訓練的順序添加剩餘特徵
         # 7. dji_return_t-1
         features_list.append(dji_return)
         feature_names.append('dji_return_t-1')
@@ -421,7 +428,7 @@ def advanced_xgboost_predict(predict_days=5):
         
         # 詳細的資料驗證日誌
         print("=" * 60)
-        print("XGBoost 模型輸入特徵檢查報告 (新特徵版本)")
+        print("XGBoost 模型輸入特徵檢查報告 (漲幅百分比版本)")
         print("=" * 60)
         
         print(f"總特徵數量: {len(features_list)} 個")
@@ -431,7 +438,7 @@ def advanced_xgboost_predict(predict_days=5):
         print("\n特徵狀態詳情:")
         for i, (name, value) in enumerate(zip(feature_names, features_list)):
             status = feature_status.get(name, {})
-            status_symbol = "✓正常" if status.get('is_real', False) else "⚠預設值"
+            status_symbol = "✓正常" if status.get('is_real', False) else "⚠ 預設值"
             print(f"  [{i+1:2d}] {name:18s}: {value:12.6f} ({status_symbol})")
         
         # 統計完整性
@@ -456,12 +463,12 @@ def advanced_xgboost_predict(predict_days=5):
         # 進行預測
         predictions = xgb_model.predict('xgboost_model', input_df)
         
-        # 根據預測天數選擇對應的預測值
+        # 【重要更新】處理新的漲幅百分比輸出格式
         pred_mapping = {
-            1: 'Close_t0_pred',
-            5: 'Close_t5_pred', 
-            10: 'Close_t10_pred',
-            20: 'Close_t20_pred'
+            1: 'Change_pct_t1_pred',   # 1天後漲幅%
+            5: 'Change_pct_t5_pred',   # 5天後漲幅%
+            10: 'Change_pct_t10_pred', # 10天後漲幅%
+            20: 'Change_pct_t20_pred'  # 20天後漲幅%
         }
         
         # 找到最接近的預測天數
@@ -469,21 +476,24 @@ def advanced_xgboost_predict(predict_days=5):
         closest_day = min(available_days, key=lambda x: abs(x - predict_days))
         pred_key = pred_mapping[closest_day]
         
-        predicted_price = predictions[pred_key]
+        # 【關鍵修改】現在直接取得漲幅百分比
+        predicted_change_pct = predictions[pred_key]
+        
+        # 【新增】為了兼容性，計算預測價格（僅供參考）
         current_price = latest_data['Close']
-        change_pct = ((predicted_price - current_price) / current_price) * 100
+        predicted_price = current_price * (1 + predicted_change_pct / 100)
         
         print(f"XGBoost 預測完成:")
         print(f"- 預測天數: {predict_days} (使用 {closest_day} 天模型)")
         print(f"- 當前價格: {current_price:.2f}")
-        print(f"- 預測價格: {predicted_price:.2f}")
-        print(f"- 預測變化: {change_pct:+.2f}%")
+        print(f"- 預測漲幅: {predicted_change_pct:+.2f}%")
+        print(f"- 預測價格: {predicted_price:.2f} (參考)")
         print(f"- 使用特徵數: {len(features_list)} 個")
         print(f"- 特徵完整性: {completeness:.1f}%")
         
         return {
-            'predicted_price': predicted_price,
-            'change_pct': change_pct,
+            'predicted_price': predicted_price,     # 為了兼容現有代碼
+            'change_pct': predicted_change_pct,     # 【新增】直接的漲幅百分比
             'confidence': max(0.6, min(0.85, completeness / 100))  # 根據特徵完整性調整信心度
         }
         
@@ -789,7 +799,7 @@ app.layout = html.Div([
     ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
 ])
 
-# 回調函數區域
+# 修改台指期預測的回調函數
 @app.callback(
     [dash.dependencies.Output('taiex-prediction-results', 'children'),
      dash.dependencies.Output('taiex-prediction-chart', 'figure')],
@@ -804,32 +814,42 @@ def update_taiex_prediction(predict_days):
     
     if final_prediction is None: return html.Div("資料不足，無法進行預測"), {}
     current_price, last_date = data['Close'].iloc[-1], data.index[-1]
+    
+    # 【重要更新】現在 change_pct 已經是正確的漲幅百分比
     predicted_price, change_pct, confidence = final_prediction['predicted_price'], final_prediction['change_pct'], final_prediction['confidence']
     
+    # 生成預測路徑（為了圖表顯示）
     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])
     prediction_dates, prediction_prices = [last_date], [current_price]
     
     for days in intervals_to_predict:
-        # === 修改點：迴圈內也使用統一的預測控制器 ===
         interim_prediction = get_prediction(data, days)
         if interim_prediction:
             prediction_dates.append(last_date + timedelta(days=days))
             prediction_prices.append(interim_prediction['predicted_price'])
             
-    # 後續繪圖邏輯不變
+    # 後續繪圖邏輯不變，但現在 change_pct 是真正的漲幅百分比
     color, arrow = ('red', '📈') if change_pct >= 0 else ('green', '📉')
     result_card = html.Div([
         html.H4(f"{predict_days}日後預測結果", style={'margin': '0 0 15px 0', 'color': 'white'}),
-        html.Div([html.Span(f"{arrow} ", style={'font-size': '24px'}), html.Span(f"{change_pct:+.2f}%", style={'font-size': '28px','font-weight': 'bold','color': color})], style={'margin': '10px 0'}),
-        html.P(f"目前價格: {current_price:.2f}", style={'margin': '5px 0'}), html.P(f"預測價格: {predicted_price:.2f}", style={'margin': '5px 0'}),
+        html.Div([
+            html.Span(f"{arrow} ", style={'font-size': '24px'}), 
+            html.Span(f"{change_pct:+.2f}%", style={'font-size': '28px','font-weight': 'bold','color': color})
+        ], style={'margin': '10px 0'}),
+        html.P(f"目前價格: {current_price:.2f}", style={'margin': '5px 0'}), 
+        html.P(f"預測價格: {predicted_price:.2f}", style={'margin': '5px 0'}),
+        html.P(f"預測漲幅: {change_pct:+.2f}%", style={'margin': '5px 0', 'font-weight': 'bold'}),  # 【新增】
         html.P(f"信心度: {confidence:.1%}", style={'margin': '5px 0', 'font-size': '14px'})
     ], style={'background': 'rgba(255,255,255,0.1)','padding': '20px','border-radius': '10px','border': '1px solid rgba(255,255,255,0.2)'})
+    
+    # 繪圖部分保持不變
     fig = go.Figure()
     recent_data = data.tail(30)
     fig.add_trace(go.Scatter(x=recent_data.index, y=recent_data['Close'], mode='lines', name='歷史價格', line=dict(color='#FFA726', width=2)))
     fig.add_trace(go.Scatter(x=prediction_dates, y=prediction_prices, mode='lines+markers', name=f'{predict_days}日預測路徑', line=dict(color=color, width=3, dash='dash'), marker=dict(size=8)))
-    fig.update_layout(title=f'台指期 {predict_days}日預測走勢', xaxis_title='日期', yaxis_title='指數點位', height=350, plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font=dict(color='white'))
+    fig.update_layout(title=f'台指期 {predict_days}日預測走勢 (預測漲幅: {change_pct:+.2f}%)', xaxis_title='日期', yaxis_title='指數點位', height=350, plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font=dict(color='white'))
+    
     return result_card, fig
 
 @app.callback(