Update app.py

app.py

@@ -533,7 +533,7 @@ ANALYSIS_CACHE = {}
 STOCK_DATA_CACHE = {}
 CACHE_EXPIRE_SECONDS = 60  # 改為1分鐘，確保數據更及時
 # 快取有效時間（秒），例如：8 小時 = 8 * 60 * 60 = 28800 秒
-CACHE_DURATION_SECONDS = 8 * 60 * 60
+CACHE_DURATION_SECONDS = 60  # 60秒緩存
 # ========================== CACHE 設定 END ==========================
 # ========================== 全域設定 END ==========================
 
@@ -740,44 +740,29 @@ def initialize_app():
     print(f"分析結果緩存時間: {ANALYSIS_CACHE_DURATION}秒")
     
 def get_stock_data(symbol, period='1y'):
-    """改進版獲取股票資料 - 確保及時更新真實數據"""
+    """獲取股票資料 - 改進版，縮短緩存時間"""
     try:
         current_time = time.time()
-        cache_key = f"{symbol}_{period}"
-        
-        # 檢查緩存是否過期
-        if cache_key in STOCK_DATA_CACHE:
-            cached_data, cache_time = STOCK_DATA_CACHE[cache_key]
-            if current_time - cache_time < CACHE_EXPIRE_SECONDS:
-                print(f"使用緩存數據: {symbol} (緩存時間: {int(current_time - cache_time)}秒前)")
-                return cached_data
-            else:
-                print(f"緩存已過期，重新獲取: {symbol}")
+        cache_key = f"{symbol}_{period}_{int(current_time/60)}"  # 每分鐘一個新的cache key
+        
+        print(f"正在獲取股票數據: {symbol}, 時間: {datetime.now().strftime('%H:%M:%S')}")
         
-        print(f"正在獲取最新數據: {symbol}")
         stock = yf.Ticker(symbol)
         data = stock.history(period=period)
         
-        # 備用數據源邏輯
         if data.empty and symbol == 'TXF=F':
-            print("台指期數據為空，嘗試替代數據源...")
             stock = yf.Ticker('0050.TW')
             data = stock.history(period=period)
             if data.empty:
                 stock = yf.Ticker('^TWII')
                 data = stock.history(period=period)
         
-        # 儲存到緩存
         if not data.empty:
-            STOCK_DATA_CACHE[cache_key] = (data.copy(), current_time)
-            print(f"數據獲取成功: {symbol}, 最新日期: {data.index[-1].strftime('%Y-%m-%d')}")
-        else:
-            print(f"警告: 無法獲取 {symbol} 的數據")
+            print(f"數據獲取成功: {symbol}, 最新日期: {data.index[-1].strftime('%Y-%m-%d')}, 最新收盤: {data['Close'].iloc[-1]:.2f}")
         
         return data
-        
     except Exception as e:
-        print(f"獲取股票數據時發生錯誤: {e}")
+        print(f"獲取股票數據錯誤: {e}")
         return pd.DataFrame()
 
 def get_us_market_data():
@@ -819,75 +804,42 @@ def get_exchange_rate():
         return 31.5
 
 def simple_statistical_predict(data, predict_days=5):
-    """改進版統計預測模型 - 基於市場時間的真實變化"""
+    """簡化的統計預測模型 - 加入時間變化因子"""
     if len(data) < 60: 
         return None
         
     prices = data['Close'].values
     current_price = prices[-1]
     
-    # 基本技術指標
     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:])
     
-    # 【關鍵修改】基於當前市場時間的動態因子
-    now = datetime.now()
-    
-    # 市場開盤時間影響因子（台股9:00-13:30）
-    market_hour_factor = 0
-    if 9 <= now.hour <= 13:
-        market_hour_factor = 0.001 * (now.hour - 9) + 0.0001 * now.minute
-    elif now.hour > 13:
-        market_hour_factor = -0.001 * (now.hour - 13)
-    
-    # 周間效應
-    weekday_factor = {0: 0.002, 1: 0.001, 2: 0, 3: -0.001, 4: -0.002}.get(now.weekday(), 0)
-    
-    # 月份效應（基於歷史統計）
-    month_factor = {1: 0.001, 2: -0.001, 3: 0.002, 4: 0.001, 5: -0.001, 6: 0, 
-                   7: 0.001, 8: -0.002, 9: -0.001, 10: 0.002, 11: 0.001, 12: 0.003}.get(now.month, 0)
+    # 使用當前時間作為變化因子
+    current_hour = datetime.now().hour
+    current_minute = datetime.now().minute
     
-    # 計算預測
-    base_change = recent_trend * predict_days
-    
-    # 趨勢判斷
-    if ma_short > ma_medium > ma_long:
-        trend_factor = 1.0 + 0.02  # 多頭趨勢
-    elif ma_short < ma_medium < ma_long:
-        trend_factor = 1.0 - 0.02  # 空頭趨勢
+    # 市場時間調整因子（台股9:00-13:30）
+    if 9 <= current_hour <= 13:
+        time_factor = (current_hour - 9) * 0.001 + current_minute * 0.00001
     else:
-        trend_factor = 1.0  # 盤整
-    
-    # 【重要】使用真實市場因子，而非隨機數
-    market_time_adjustment = market_hour_factor + weekday_factor + month_factor
+        time_factor = current_minute * 0.00001
     
-    # 波動性調整（基於實際歷史波動）
-    volatility_adjustment = volatility * predict_days * 0.5
-    
-    # 預測價格計算
-    predicted_price = current_price * trend_factor + base_change + (current_price * market_time_adjustment)
+    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)
     
-    # 考慮預測天數的不確定性遞增
-    uncertainty_factor = 1 + (predict_days / 100.0) * volatility
-    predicted_price *= uncertainty_factor
+    # 加入時間變化
+    predicted_price = current_price * trend_factor + base_change + (current_price * time_factor)
     
     # 計算漲幅百分比
     change_pct = ((predicted_price - current_price) / current_price) * 100
+    change_pct = np.clip(change_pct, -15.0, 15.0)
     
-    # 合理範圍限制
-    change_pct = np.clip(change_pct, -20.0, 20.0)
-    
-    # 信心度基於數據質量和市場狀況
-    confidence = max(0.5, min(0.9, (1 - volatility * 3) + abs(market_time_adjustment) * 10))
+    confidence = max(0.6, 1 - volatility * 2)
     
-    print(f"統計預測詳情:")
-    print(f"  - 市場時間因子: {market_time_adjustment:.6f}")
-    print(f"  - 趨勢因子: {trend_factor:.3f}")
-    print(f"  - 波動率: {volatility:.4f}")
-    print(f"  - 預測漲幅: {change_pct:+.2f}%")
+    print(f"統計預測 - 時間因子: {time_factor:.6f}, 漲幅: {change_pct:+.2f}%")
     
     return {
         'predicted_price': predicted_price, 
@@ -942,76 +894,43 @@ def calculate_new_features(df):
     return df
 
 def advanced_xgboost_predict(predict_days=5):
-    """改進版 XGBoost 預測 - 增加真實的市場時間特徵"""
+    """使用 XGBoost 模型進行預測 - 強制刷新數據版本"""
     try:
-        print(f"開始使用 XGBoost 模型進行 {predict_days} 天預測...")
+        print(f"開始XGBoost預測 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
         
         xgb_model = XGBoostModel()
-        current_time = datetime.now()
         
-        # 獲取最新的台指期數據
+        # 強制重新獲取台指數據 - 不使用緩存
+        print("正在獲取最新台指數據...")
         taiex_data = get_stock_data('^TWII', '2y')
         if taiex_data.empty or len(taiex_data) < 60:
             print("台指期數據不足，無法進行XGBoost預測")
             return None
         
-        # 計算技術指標
         taiex_data = calculate_technical_indicators(taiex_data)
         taiex_data = calculate_new_features(taiex_data)
         
-        # 【關鍵修改】獲取真實的市場數據，而非使用緩存
         # 強制重新獲取美股數據
-        print("正在獲取最新美股數據...")
-        
-        # 清除美股數據的緩存，確保獲取最新數據
-        us_symbols = ['^DJI', '^SOX']
-        for symbol in us_symbols:
-            cache_key = f"{symbol}_5d"
-            if cache_key in STOCK_DATA_CACHE:
-                del STOCK_DATA_CACHE[cache_key]
-        
-        # 獲取美股數據
-        dji_return = 0
-        sox_return = 0
+        print("正在獲取美股數據...")
+        us_market_data = get_us_market_data()
         
-        try:
-            dji_data = get_stock_data('^DJI', '5d')
-            if not dji_data.empty and len(dji_data) >= 2:
-                dji_return = (dji_data['Close'].iloc[-1] / dji_data['Close'].iloc[-2] - 1)
-                print(f"道瓊報酬率: {dji_return:.4f}")
-        except Exception as e:
-            print(f"無法獲取道瓊數據: {e}")
-            dji_return = 0
-            
-        try:
-            sox_data = get_stock_data('^SOX', '5d') 
-            if not sox_data.empty and len(sox_data) >= 2:
-                sox_return = (sox_data['Close'].iloc[-1] / sox_data['Close'].iloc[-2] - 1)
-                print(f"費半報酬率: {sox_return:.4f}")
-        except Exception as e:
-            print(f"無法獲取費半數據: {e}")
-            sox_return = 0
-        
-        # 獲取最新新聞情緒分數
-        sentiment_score_raw = 0
+        # 獲取新聞情緒分數
         try:
             if predictor is not None:
-                # 清除新聞緩存，確保獲取最新情緒
                 sentiment_score_raw = predictor.get_news_index()
-                print(f"新聞情緒分數: {sentiment_score_raw:.4f}")
                 if sentiment_score_raw is None:
                     sentiment_score_raw = 0
+                print(f"新聞情緒分數: {sentiment_score_raw}")
             else:
                 sentiment_score_raw = 0
-        except Exception as e:
-            print(f"無法獲取新聞情緒: {e}")
+        except:
             sentiment_score_raw = 0
         
         # 準備特徵數據
         latest_data = taiex_data.iloc[-1]
         yesterday_close = latest_data['Close']
-        
-        # 構建特徵向量
+
+        # 特徵列表保持不變
         new_feature_columns = [
             'return_t-1',
             'return_t-5',
@@ -1021,15 +940,34 @@ def advanced_xgboost_predict(predict_days=5):
             'MACD_diff',
         ]
         
+        # 獲取美股報酬率
+        dji_return = 0
+        sox_return = 0
+        
+        try:
+            dji_data = get_stock_data('^DJI', '5d')
+            if not dji_data.empty and len(dji_data) >= 2:
+                dji_return = (dji_data['Close'].iloc[-1] / dji_data['Close'].iloc[-2] - 1)
+                print(f"道瓊報酬率: {dji_return:.4f}")
+        except:
+            pass
+            
+        try:
+            sox_data = get_stock_data('^SOX', '5d') 
+            if not sox_data.empty and len(sox_data) >= 2:
+                sox_return = (sox_data['Close'].iloc[-1] / sox_data['Close'].iloc[-2] - 1)
+                print(f"費半報酬率: {sox_return:.4f}")
+        except:
+            pass
+        
+        # 建立特徵向量
         features_list = []
         feature_names = []
         
-        # 處理技術指標特徵
         for feature in new_feature_columns:
             if feature in latest_data.index:
                 value = latest_data[feature]
                 if pd.isna(value):
-                    # 使用合理的預設值
                     if 'return' in feature: default_value = 0.0
                     elif 'MA' in feature: default_value = latest_data['Close'] if not pd.isna(latest_data['Close']) else 100
                     elif 'volatility' in feature: default_value = 0.02
@@ -1043,28 +981,19 @@ def advanced_xgboost_predict(predict_days=5):
                 
                 feature_names.append(feature)
 
-        # 添加美股數據
-        features_list.extend([dji_return, sox_return])
-        feature_names.extend(['dji_return_t-1', 'sox_return_t-1'])
-        
-        # 添加收盤價和新聞數據
-        features_list.extend([yesterday_close, sentiment_score_raw])
-        feature_names.extend(['close', 'NEWS'])
+        # 添加其他特徵
+        features_list.extend([dji_return, sox_return, yesterday_close, sentiment_score_raw])
+        feature_names.extend(['dji_return_t-1', 'sox_return_t-1', 'close', 'NEWS'])
         
         # 轉換為 DataFrame
         input_df = pd.DataFrame([features_list], columns=feature_names)
         
-        print("=" * 60)
-        print(f"XGBoost 模型輸入特徵 (時間: {current_time.strftime('%Y-%m-%d %H:%M:%S')})")
-        print("=" * 60)
-        print(f"特徵向量: {[f'{v:.6f}' for v in features_list]}")
-        print("=" * 60)
+        print(f"特徵向量: {[f'{f:.4f}' for f in features_list[:5]]}...")  # 只顯示前5個
         
         # 進行預測
         predictions = xgb_model.predict('xgboost_model', input_df)
         
         if predictions is None:
-            print("XGBoost 預測失敗")
             return None
         
         # 處理預測結果
@@ -1083,21 +1012,16 @@ def advanced_xgboost_predict(predict_days=5):
         current_price = latest_data['Close']
         predicted_price = current_price * (1 + predicted_change_pct / 100)
         
-        print(f"XGBoost 預測結果:")
-        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"- 預測漲幅: {predicted_change_pct:+.2f}%")
-        print(f"- 預測價格: {predicted_price:.2f}")
-        print(f"- 數據時間: {taiex_data.index[-1].strftime('%Y-%m-%d')}")
+        print(f"XGBoost預測結果 - 漲幅: {predicted_change_pct:+.2f}%, 時間: {datetime.now().strftime('%H:%M:%S')}")
         
         return {
             'predicted_price': predicted_price,
             'change_pct': predicted_change_pct,
-            'confidence': 0.75  # 基於模型質量的固定信心度
+            'confidence': 0.75
         }
         
     except Exception as e:
-        print(f"XGBoost 預測時發生錯誤: {e}")
+        print(f"XGBoost 預測錯誤: {e}")
         import traceback
         traceback.print_exc()
         return None
@@ -1849,63 +1773,46 @@ def update_business_climate_chart(selected_stock):
     [Input('stock-dropdown', 'value'),
      Input('period-dropdown', 'value')]
 )
-ANALYSIS_CACHE = {}
-ANALYSIS_CACHE_DURATION = 60  # 改為1分鐘
-
 def update_analysis_text(selected_stock, period):
-    """修改版分析文本更新 - 縮短緩存時間"""
+    """修改版分析文本更新"""
+    # 縮短緩存時間為1分鐘
+    cache_duration = 60  
     cache_key = f"{selected_stock}-{period}"
     current_time = time.time()
 
     # 檢查緩存
     if cache_key in ANALYSIS_CACHE:
         cached_data = ANALYSIS_CACHE[cache_key]
-        if current_time - cached_data['timestamp'] < ANALYSIS_CACHE_DURATION:
-            print(f"使用分析緩存: {cache_key} (剩餘: {int(ANALYSIS_CACHE_DURATION - (current_time - cached_data['timestamp']))}秒)")
+        if current_time - cached_data['timestamp'] < cache_duration:
+            print(f"使用分析緩存: {cache_key}")
             return cached_data['technical'], cached_data['fundamental'], cached_data['outlook']
 
-    print(f"重新生成分析: {cache_key}")
-    
-    # 清理舊緩存
-    clear_old_cache()
+    print(f"重新生成分析: {cache_key} - {datetime.now().strftime('%H:%M:%S')}")
     
-    # 獲取最新數據
     data = get_stock_data(selected_stock, period)
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-    
     if data.empty or len(data) < 20:
         return "資料不足，無法分析", "資料不足，無法分析", "資料不足，無法分析"
     
     data = calculate_technical_indicators(data)
     
-    # 技術面分析（使用最新數據）
+    # 技術面分析
     price_change = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
     rsi_current = data['RSI'].iloc[-1] if not pd.isna(data['RSI'].iloc[-1]) else 50
     macd_current = data['MACD'].iloc[-1] if not pd.isna(data['MACD'].iloc[-1]) else 0
     macd_signal_current = data['MACD_Signal'].iloc[-1] if not pd.isna(data['MACD_Signal'].iloc[-1]) else 0
     
-    # 添加數據時間戳信息
     latest_date = data.index[-1].strftime('%Y-%m-%d')
+    current_time_str = datetime.now().strftime('%H:%M:%S')
     
     technical_text = html.Div([
-        html.P([html.Strong("數據時間："), f"{latest_date}"]),
-        html.P([html.Strong("價格趋勢："), f"在最近 {period} 期間內，{stock_name} 股價呈現", 
-                html.Span(f"{'上漲' if price_change > 5 else '下跌' if price_change < -5 else '盤整'}", 
-                         style={'color': 'red' if price_change > 5 else 'green' if price_change < -5 else 'orange', 
-                               'font-weight': 'bold'}), 
-                f"走勢，累計變動 {price_change:+.1f}%。"]),
-        html.P([html.Strong("RSI 指標："), f"目前的 RSI 值為 {rsi_current:.1f}，", 
-                html.Span("處於超買區(>70)" if rsi_current > 70 else "處於超賣區(<30)" if rsi_current < 30 else "在正常範圍內", 
-                         style={'color': 'green' if rsi_current > 70 else 'red' if rsi_current < 30 else 'blue', 
-                               'font-weight': 'bold'}), "。"]),
-        html.P([html.Strong("MACD 指標："), f"MACD 快線 ({macd_current:.3f}) 目前", 
-                html.Span("高於" if macd_current > macd_signal_current else "低於", 
-                         style={'color': 'red' if macd_current > macd_signal_current else 'green', 
-                               'font-weight': 'bold'}), 
-                f" Signal 慢線 ({macd_signal_current:.3f})，顯示市場動能偏向{'多頭' if macd_current > macd_signal_current else '空頭'}。"]),
+        html.P([html.Strong("更新時間："), f"{latest_date} {current_time_str}"]),
+        html.P([html.Strong("價格趨勢："), f"在最近 {period} 期間內，{stock_name} 股價呈現", html.Span(f"{'上漲' if price_change > 5 else '下跌' if price_change < -5 else '盤整'}", style={'color': 'red' if price_change > 5 else 'green' if price_change < -5 else 'orange', 'font-weight': 'bold'}), f"走勢，累計變動 {price_change:+.1f}%。"]),
+        html.P([html.Strong("RSI 指標："), f"目前的 RSI 值為 {rsi_current:.1f}，", html.Span("處於超買區(>70)" if rsi_current > 70 else "處於超賣區(<30)" if rsi_current < 30 else "在正常範圍內", style={'color': 'green' if rsi_current > 70 else 'red' if rsi_current < 30 else 'blue', 'font-weight': 'bold'}), "。"]),
+        html.P([html.Strong("MACD 指標："), f"MACD 快線 ({macd_current:.3f}) 目前", html.Span("高於" if macd_current > macd_signal_current else "低於", style={'color': 'red' if macd_current > macd_signal_current else 'green', 'font-weight': 'bold'}), f" Signal 慢線 ({macd_signal_current:.3f})。"]),
     ])
     
-    # AI 分析（基於最新數據）
+    # AI 分析
     fundamental_text, market_outlook_text = generate_gemini_analysis(stock_name, selected_stock, period, data)
     
     # 存入緩存