Update app.py

app.py

@@ -17,16 +17,28 @@ from bs4 import BeautifulSoup
 import requests
 import time # 引用 time 模組以處理時間戳
 
+# ========================= 引用外部模組 START =========================
 # 引用您組員的預測器程式
 from Bert_predict import BertPredictor
 
+# 引用新的模型預測器
+from model_predictor import advanced_lstm_predict
+# ========================== 引用外部模組 END ==========================
+
+# ========================= 全域設定 START =========================
+# 【【【模型切換開關】】】
+# False: 使用簡易統計模型 (預設)
+# True:  使用 model_predictor.py 中的進階 LSTM 模型 (未來啟用)
+USE_ADVANCED_MODEL = False
+
+
 # ========================= CACHE 設定 START =========================
 # 分析結果的快取字典
 ANALYSIS_CACHE = {}
 # 快取有效時間（秒），例如：4 小時 = 4 * 60 * 60 = 14400 秒
 CACHE_DURATION_SECONDS = 8 * 60 * 60
 # ========================== CACHE 設定 END ==========================
-
+# ========================== 全域設定 END ==========================
 
 # 台股代號對應表 (移除台指期，因為它現在是獨立區塊)
 TAIWAN_STOCKS = {
@@ -171,10 +183,9 @@ def get_stock_data(symbol, period='1y'):
     except:
         return pd.DataFrame()
 
-def simple_lstm_predict(data, predict_days=5):
-    """簡化的LSTM預測模型 (使用統計方法模擬)"""
-    if len(data) < 60:
-        return None
+def simple_statistical_predict(data, predict_days=5):
+    """【備用模型】簡化的統計預測模型。"""
+    if len(data) < 60: return None
     prices = data['Close'].values
     ma_short = np.mean(prices[-5:])
     ma_medium = np.mean(prices[-20:])
@@ -182,21 +193,29 @@ def simple_lstm_predict(data, predict_days=5):
     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
-    if ma_short > ma_medium > ma_long:
-        trend_factor = 1.02
-    elif ma_short < ma_medium < ma_long:
-        trend_factor = 0.98
-    else:
-        trend_factor = 1.0
+    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
-    return {
-        'predicted_price': predicted_price,
-        'change_pct': change_pct,
-        'confidence': max(0.6, 1 - volatility * 2)
-    }
+    return {'predicted_price': predicted_price, 'change_pct': change_pct, 'confidence': max(0.6, 1 - volatility * 2)}
+
+def get_prediction(data, predict_days=5):
+    """
+    【【模型預測控制器】】
+    根據 USE_ADVANCED_MODEL 的設定，呼叫對應的預測模型。
+    """
+    if USE_ADVANCED_MODEL:
+        print(f"模式: 進階LSTM模型 | 預測天期: {predict_days}天")
+        prediction = advanced_lstm_predict(predict_days)
+        # 如果進階模型預測失敗，則自動降級使用簡易模型
+        if prediction is not None:
+            return prediction
+        else:
+            print("進階模型預測失敗，自動降級為簡易統計模型。")
+    
+    # 預設或降級時執行簡易模型
+    print(f"模式: 簡易統計模型 | 預測天期: {predict_days}天")
+    return simple_statistical_predict(data, predict_days)
 
 def calculate_technical_indicators(df):
     """計算技術指標"""
@@ -467,18 +486,26 @@ app.layout = html.Div([
 def update_taiex_prediction(predict_days):
     data = get_stock_data('^TWII', '2y')
     if data.empty: return html.Div("無法獲取台指期資料"), {}
-    final_prediction = simple_lstm_predict(data, predict_days)
+    
+    # === 修改點：統一呼叫 get_prediction 控制器 ===
+    final_prediction = get_prediction(data, 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]
     predicted_price, change_pct, confidence = final_prediction['predicted_price'], final_prediction['change_pct'], final_prediction['confidence']
+    
     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:
-        interim_prediction = simple_lstm_predict(data, days)
+        # === 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)
     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'}),