Update app.py

app.py

@@ -1,3 +1,5 @@
+# HUGING_FACE_V2.1.3.py (整合 Bert_predict 版本)
+
 # 系統套件
 import os
 from datetime import datetime, timedelta
@@ -13,8 +15,8 @@ import re
 from bs4 import BeautifulSoup
 import requests
 
-# 引入 BERT 預測模組
-from Bert_predict import predict_sentiment
+# 引用您組員的預測器程式
+from Bert_predict import BertPredictor
 
 # 台股代號對應表 (移除台指期，因為它現在是獨立區塊)
 TAIWAN_STOCKS = {
@@ -72,217 +74,117 @@ def get_stock_data(symbol, period='1y'):
     try:
         stock = yf.Ticker(symbol)
         data = stock.history(period=period)
-
-        # 如果台指期資料為空，嘗試替代方案
         if data.empty and symbol == 'TXF=F':
-            # 嘗試使用台灣50ETF作為替代
             stock = yf.Ticker('0050.TW')
             data = stock.history(period=period)
             if data.empty:
-                # 最後嘗試使用加權指數
                 stock = yf.Ticker('^TWII')
                 data = stock.history(period=period)
-
         return data
     except:
         return pd.DataFrame()
 
-def create_lstm_dataset(data, time_step=60):
-    """建立LSTM訓練資料集"""
-    X, y = [], []
-    for i in range(time_step, len(data)):
-        X.append(data[i-time_step:i, 0])
-        y.append(data[i, 0])
-    return np.array(X), np.array(y)
-
 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
-
-    # 使用移動平均和趨勢分析來模擬深度學習預測
     prices = data['Close'].values
-
-    # 計算短期和長期移動平均
     ma_short = np.mean(prices[-5:])
     ma_medium = np.mean(prices[-20:])
     ma_long = np.mean(prices[-60:])
-
-    # 計算價格變化趨勢
     recent_trend = np.polyfit(range(20), prices[-20:], 1)[0]
     volatility = np.std(prices[-20:]) / np.mean(prices[-20:])
-
-    # 模擬預測邏輯
     base_change = recent_trend * predict_days
     trend_factor = 1.0
-
     if ma_short > ma_medium > ma_long:
-        trend_factor = 1.02  # 上升趨勢
+        trend_factor = 1.02
     elif ma_short < ma_medium < ma_long:
-        trend_factor = 0.98  # 下降趨勢
+        trend_factor = 0.98
     else:
-        trend_factor = 1.0   # 盤整
-
-    # 加入隨機性模擬市場不確定性
+        trend_factor = 1.0
     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)  # 基於波動率的信心度
+        'confidence': max(0.6, 1 - volatility * 2)
     }
 
 def calculate_technical_indicators(df):
     """計算技術指標"""
-    if df.empty:
-        return df
-
-    # 移動平均線
+    if df.empty: return df
     df['MA5'] = df['Close'].rolling(window=5).mean()
     df['MA20'] = df['Close'].rolling(window=20).mean()
-
-    # 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'] = 100 - (100 / (1 + rs))
-
-    # MACD (12, 26, 9)
     exp1 = df['Close'].ewm(span=12).mean()
     exp2 = df['Close'].ewm(span=26).mean()
     df['MACD'] = exp1 - exp2
     df['MACD_Signal'] = df['MACD'].ewm(span=9).mean()
     df['MACD_Histogram'] = df['MACD'] - df['MACD_Signal']
-
-    # 布林通道 (20日, 2倍標準差)
     df['BB_Middle'] = df['Close'].rolling(window=20).mean()
     bb_std = df['Close'].rolling(window=20).std()
     df['BB_Upper'] = df['BB_Middle'] + (bb_std * 2)
     df['BB_Lower'] = df['BB_Middle'] - (bb_std * 2)
-    df['BB_Width'] = df['BB_Upper'] - df['BB_Lower']
-    df['BB_Position'] = (df['Close'] - df['BB_Lower']) / (df['BB_Upper'] - df['BB_Lower'])
-
-    # KD指標 (9, 3, 3)
     low_min = df['Low'].rolling(window=9).min()
     high_max = df['High'].rolling(window=9).max()
     rsv = (df['Close'] - low_min) / (high_max - low_min) * 100
-    df['K'] = rsv.ewm(com=2).mean()  # com=2 相當於 span=3
+    df['K'] = rsv.ewm(com=2).mean()
     df['D'] = df['K'].ewm(com=2).mean()
-
-    # 威廉指標 %R (14日)
     low_min_14 = df['Low'].rolling(window=14).min()
     high_max_14 = df['High'].rolling(window=14).max()
     df['Williams_R'] = -100 * (high_max_14 - df['Close']) / (high_max_14 - low_min_14)
-
-    # DMI (Directional Movement Index)
     df['up_move'] = df['High'] - df['High'].shift(1)
     df['down_move'] = df['Low'].shift(1) - df['Low']
     df['+DM'] = np.where((df['up_move'] > df['down_move']) & (df['up_move'] > 0), df['up_move'], 0)
     df['-DM'] = np.where((df['down_move'] > df['up_move']) & (df['down_move'] > 0), df['down_move'], 0)
-
-    # 計算真實範圍 (TR)
     df['TR'] = np.max([df['High'] - df['Low'], abs(df['High'] - df['Close'].shift(1)), abs(df['Low'] - df['Close'].shift(1))], axis=0)
-
-    # 計算平滑後的 +DM, -DM, TR (通常使用 14 天)
     df['+DI'] = (df['+DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean()) * 100
     df['-DI'] = (df['-DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean()) * 100
-
-    # 計算 ADX
     df['DX'] = abs(df['+DI'] - df['-DI']) / (df['+DI'] + df['-DI']) * 100
     df['ADX'] = df['DX'].ewm(com=13, adjust=False).mean()
-
     return df
 
 def calculate_volume_profile(df, num_bins=50):
-    """
-    計算成交量分佈圖 (Volume Profile) 的數據。
-    """
-    if df.empty or 'High' not in df.columns or 'Low' not in df.columns or 'Volume' not in df.columns:
-        return None, None, None
-
+    if df.empty or 'High' not in df.columns or 'Low' not in df.columns or 'Volume' not in df.columns: return None, None, None
     all_prices = np.concatenate([df['High'].values, df['Low'].values])
-    min_price = all_prices.min()
-    max_price = all_prices.max()
-
+    min_price, max_price = all_prices.min(), all_prices.max()
     price_for_volume = (df['High'] + df['Low'] + df['Close']) / 3
     df_vol_profile = df.copy()
     df_vol_profile['Price_Indicator'] = price_for_volume
-    df_vol_profile['Volume'] = df_vol_profile['Volume']
-
     hist, bin_edges = np.histogram(df_vol_profile['Price_Indicator'], bins=num_bins, range=(min_price, max_price), weights=df_vol_profile['Volume'])
     price_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
-
     return bin_edges, hist, price_centers
 
 def get_business_climate_data():
-    """獲取台灣景氣燈號資料"""
     try:
-        # 檢查檔案是否存在
-        if not os.path.exists('business_climate.csv'):
-            print("business_climate.csv 檔案不存在")
-            return pd.DataFrame()
-
-        # 讀取CSV檔案，假設列名為 Date 和 Index
+        if not os.path.exists('business_climate.csv'): return pd.DataFrame()
         df = pd.read_csv('business_climate.csv')
-
-        # 檢查列名並調整
-        if 'Date' not in df.columns:
-            # 如果第一列是日期，重新命名
-            df.columns = ['Date', 'Index'] if len(df.columns) == 2 else df.columns
-
-        # 轉換日期格式 (處理 YYYY-MM 格式)
+        if 'Date' not in df.columns: df.columns = ['Date', 'Index'] if len(df.columns) == 2 else df.columns
         if 'Date' in df.columns:
-            try:
-                # 如果是 YYYY-MM 格式，轉換為日期
-                df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
-            except:
-                df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
-
-        # 移除日期轉換失敗的行
+            try: df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
+            except: df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
         df = df.dropna(subset=['Date'])
-
-        print(f"成功讀取景氣燈號資料：{len(df)} 筆記錄")
         return df
-
     except Exception as e:
         print(f"無法獲取景氣燈號資料: {str(e)}")
         return pd.DataFrame()
 
 def get_pmi_data():
-    """獲取台灣 PMI 資料"""
     try:
-        # 檢查檔案是否存在
-        if not os.path.exists('taiwan_pmi.csv'):
-            print("taiwan_pmi.csv 檔案不存在")
-            return pd.DataFrame()
-
-        # 讀取CSV檔案
+        if not os.path.exists('taiwan_pmi.csv'): return pd.DataFrame()
         df = pd.read_csv('taiwan_pmi.csv')
-
-        # 檢查列名並調整 (處理 DATE/INDEX 或其他可能的列名)
-        if 'DATE' in df.columns:
-            df = df.rename(columns={'DATE': 'Date', 'INDEX': 'Index'})
-        elif len(df.columns) == 2:
-            df.columns = ['Date', 'Index']
-
-        # 轉換日期格式
+        if 'DATE' in df.columns: df = df.rename(columns={'DATE': 'Date', 'INDEX': 'Index'})
+        elif len(df.columns) == 2: df.columns = ['Date', 'Index']
         if 'Date' in df.columns:
-            try:
-                # 如果是 YYYY-MM 格式，轉換為日期
-                df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
-            except:
-                df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
-
-        # 移除日期轉換失敗的行
+            try: df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
+            except: df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
         df = df.dropna(subset=['Date'])
-
-        print(f"成功讀取 PMI 資料：{len(df)} 筆記錄")
         return df
-
     except Exception as e:
         print(f"無法獲取 PMI 資料: {str(e)}")
         return pd.DataFrame()
@@ -290,50 +192,36 @@ def get_pmi_data():
 # 建立 Dash 應用程式
 app = dash.Dash(__name__, suppress_callback_exceptions=True)
 
+# --- 【新增】在程式啟動時，初始化 BERT 新聞預測器 ---
+try:
+    print("正在初始化新聞情緒分析模型...")
+    predictor = BertPredictor(max_news_per_keyword=5)
+    print("新聞情緒分析模型初始化成功。")
+except Exception as e:
+    print(f"錯誤：新聞情緒分析模型初始化失敗 - {e}")
+    predictor = None
+
 # 應用程式佈局
 app.layout = html.Div([
     html.H1("台股分析儀表板", style={'text-align': 'center', 'margin-bottom': '30px'}),
-
-    # 台指期獨立預測區塊 - 置於頂部
     html.Div([
-        html.H2("🤖 AI深度學習預測 - 台指期指數", style={
-            'text-align': 'center',
-            'color': '#FFCC22',
-            'margin-bottom': '25px'
-        }),
+        html.H2("🤖 AI深度學習預測 - 台指期指數", style={'text-align': 'center','color': '#FFCC22','margin-bottom': '25px'}),
         html.Div([
             html.Div([
                 html.Label("預測期間:", style={'font-weight': 'bold', 'color': '#FFCC22'}),
-                dcc.Dropdown(
-                    id='taiex-prediction-period',
+                dcc.Dropdown(id='taiex-prediction-period',
                     options=[
-                        {'label': '1日後預測', 'value': 1},
-                        {'label': '5日後預測', 'value': 5},
-                        {'label': '10日後預測', 'value': 10},
-                        {'label': '20日後預測', 'value': 20},
-                        {'label': '60日後預測', 'value': 60}
-                    ],
-                    value=5,
-                    style={'margin-bottom': '10px', 'color': '#272727'}
-                )
+                        {'label': '1日後預測', 'value': 1},{'label': '5日後預測', 'value': 5},
+                        {'label': '10日後預測', 'value': 10},{'label': '20日後預測', 'value': 20},
+                        {'label': '60日後預測', 'value': 60}], value=5,
+                    style={'margin-bottom': '10px', 'color': '#272727'})
             ], style={'width': '30%', 'display': 'inline-block'}),
-
             html.Div(id='taiex-prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
         ]),
-
-        html.Div([
-            dcc.Graph(id='taiex-prediction-chart')
-        ], style={'margin-top': '20px'})
-    ], style={
-        'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
-        'padding': '25px',
-        'border-radius': '15px',
-        'box-shadow': '0 8px 25px rgba(0,0,0,0.15)',
-        'color': 'white',
-        'margin-bottom': '40px'
-    }),
+        html.Div([dcc.Graph(id='taiex-prediction-chart')], style={'margin-top': '20px'})
+    ], style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','padding': '25px','border-radius': '15px','box-shadow': '0 8px 25px rgba(0,0,0,0.15)','color': 'white','margin-bottom': '40px'}),
     
-    # 新聞情感分析區域（模擬）
+    # 新聞情感分析區域
     html.Div([
         html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
         html.Div([
@@ -341,359 +229,123 @@ app.layout = html.Div([
                 html.H4("市場情緒指標", style={'color': '#8E44AD'}),
                 html.Div(id='sentiment-gauge')
             ], style={'width': '48%', 'display': 'inline-block'}),
-
             html.Div([
                 html.H4("關鍵新聞摘要", style={'color': '#27AE60'}),
-                html.Div(id='news-summary', style={
-                    'background': '#f8f9fa',
-                    'padding': '15px',
-                    'border-radius': '8px',
-                    'max-height': '200px',
-                    'overflow-y': 'auto'
-                })
+                html.Div(id='news-summary', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','max-height': '200px','overflow-y': 'auto'})
             ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%'})
         ])
-    ], style={
-        'margin-top': '30px',
-        'padding': '20px',
-        'background': 'white',
-        'border-radius': '10px',
-        'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'
-    }),
+    ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
 
-    # 景氣燈號與 PMI 分析
     html.Div([
         html.H3("景氣燈號與 PMI 分析"),
         html.Div([
-            html.Div([
-                dcc.Graph(id='business-climate-chart')
-            ], style={'width': '48%', 'display': 'inline-block'}),
-            html.Div([
-                dcc.Graph(id='pmi-chart')
-            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '2%'})
+            html.Div([dcc.Graph(id='business-climate-chart')], style={'width': '48%', 'display': 'inline-block'}),
+            html.Div([dcc.Graph(id='pmi-chart')], style={'width': '48%', 'display': 'inline-block', 'margin-left': '2%'})
         ])
     ], style={'margin-top': '30px'}),
-
-
     
-    # 控制面板 (移除台指期選項)
     html.Div([
         html.Div([
             html.Label("選擇股票:"),
-            dcc.Dropdown(
-                id='stock-dropdown',
-                options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()],
-                value='2330.TW',  # 預設改為台積電
-                style={'margin-bottom': '10px'}
-            )
+            dcc.Dropdown(id='stock-dropdown', options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()], value='2330.TW', style={'margin-bottom': '10px'})
         ], style={'width': '30%', 'display': 'inline-block', 'vertical-align': 'top'}),
-
         html.Div([
             html.Label("時間範圍:"),
-            dcc.Dropdown(
-                id='period-dropdown',
-                options=[
-                    {'label': '1個月', 'value': '1mo'},
-                    {'label': '3個月', 'value': '3mo'},
-                    {'label': '6個月', 'value': '6mo'},
-                    {'label': '1年', 'value': '1y'},
-                    {'label': '2年', 'value': '2y'}
-                ],
-                value='6mo',
-                style={'margin-bottom': '10px'}
-            )
+            dcc.Dropdown(id='period-dropdown',
+                options=[{'label': '1個月', 'value': '1mo'},{'label': '3個月', 'value': '3mo'},{'label': '6個月', 'value': '6mo'},{'label': '1年', 'value': '1y'},{'label': '2年', 'value': '2y'}],
+                value='6mo', style={'margin-bottom': '10px'})
         ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'}),
-
         html.Div([
             html.Label("圖表類型:"),
-            dcc.Dropdown(
-                id='chart-type',
-                options=[
-                    {'label': '線圖', 'value': 'line'},
-                    {'label': '蠟燭圖', 'value': 'candlestick'}
-                ],
-                value='candlestick',
-                style={'margin-bottom': '10px'}
-            )
+            dcc.Dropdown(id='chart-type', options=[{'label': '線圖', 'value': 'line'},{'label': '蠟燭圖', 'value': 'candlestick'}], value='candlestick', style={'margin-bottom': '10px'})
         ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'})
     ], style={'margin-bottom': '30px'}),
 
-    # 股價資訊卡片
     html.Div(id='stock-info-cards', style={'margin-bottom': '30px'}),
-
-    # 主要圖表區域 - 移除RSI圖表
-    html.Div([
-        # 左側：股價走勢圖 (現在包含成交量分佈)
-        html.Div([
-            html.Div([
-                dcc.Graph(id='price-chart')
-            ])
-        ], style={'width': '100%', 'display': 'inline-block', 'vertical-align': 'top'}),
-    ]),
-
-    # 技術指標選擇區域
+    html.Div([html.Div([dcc.Graph(id='price-chart')], style={'width': '100%', 'display': 'inline-block', 'vertical-align': 'top'})]),
     html.Div([
         html.H3("📊 進階技術指標分析", style={'margin-bottom': '20px'}),
         html.Div([
             html.Label("選擇技術指標:", style={'font-weight': 'bold', 'margin-right': '10px'}),
-            dcc.Dropdown(
-                id='technical-indicator-selector',
-                options=[
-                    {'label': 'RSI 相對強弱指標', 'value': 'RSI'},
-                    {'label': 'MACD 指數平滑異同移動平均線', 'value': 'MACD'},
-                    {'label': '布林通道 Bollinger Bands', 'value': 'BB'},
-                    {'label': 'KD 隨機指標', 'value': 'KD'},
-                    {'label': '威廉指標 %R', 'value': 'WR'},
-                    {'label': 'DMI 動向指標', 'value': 'DMI'}
-                ],
-                value='RSI',
-                style={'width': '100%'}
-            )
+            dcc.Dropdown(id='technical-indicator-selector',
+                options=[{'label': 'RSI 相對強弱指標', 'value': 'RSI'},{'label': 'MACD 指數平滑異同移動平均線', 'value': 'MACD'},{'label': '布林通道 Bollinger Bands', 'value': 'BB'},
+                         {'label': 'KD 隨機指標', 'value': 'KD'},{'label': '威廉指標 %R', 'value': 'WR'},{'label': 'DMI 動向指標', 'value': 'DMI'}],
+                value='RSI', style={'width': '100%'})
         ], style={'margin-bottom': '20px'}),
-
-        html.Div([
-            dcc.Graph(id='advanced-technical-chart')
-        ])
-    ], style={
-        'margin-top': '20px',
-        'padding': '20px',
-        'background': 'white',
-        'border-radius': '10px',
-        'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'
-    }),
-
-    # 成交量圖
-    html.Div([
-        dcc.Graph(id='volume-chart')
-    ], style={'margin-top': '20px'}),
-
-    # 產業分析
-    html.Div([
-        html.H3("產業表現分析"),
-        dcc.Graph(id='industry-analysis')
-    ], style={'margin-top': '30px'}),
-
-    # 分析師觀點區域
+        html.Div([dcc.Graph(id='advanced-technical-chart')])
+    ], style={'margin-top': '20px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
+    html.Div([dcc.Graph(id='volume-chart')], style={'margin-top': '20px'}),
+    html.Div([html.H3("產業表現分析"), dcc.Graph(id='industry-analysis')], style={'margin-top': '30px'}),
     html.Div([
         html.H3("📊 分析師觀點與市場解讀", style={'color': '#2E86AB', 'margin-bottom': '20px'}),
         html.Div([
-            # 左側：技術分析觀點
             html.Div([
                 html.H4("🔍 技術面分析", style={'color': '#A23B72', 'margin-bottom': '15px'}),
-                html.Div(id='technical-analysis-text', style={
-                    'background': '#f8f9fa',
-                    'padding': '15px',
-                    'border-radius': '8px',
-                    'border-left': '4px solid #A23B72',
-                    'min-height': '150px',
-                    'font-size': '14px',
-                    'line-height': '1.6'
-                })
+                html.Div(id='technical-analysis-text', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','border-left': '4px solid #A23B72','min-height': '150px','font-size': '14px','line-height': '1.6'})
             ], style={'width': '48%', 'display': 'inline-block', 'vertical-align': 'top'}),
-
-            # 右側：基本面分析觀點
             html.Div([
                 html.H4("📈 基本面分析", style={'color': '#F18F01', 'margin-bottom': '15px'}),
-                html.Div(id='fundamental-analysis-text', style={
-                    'background': '#f8f9fa',
-                    'padding': '15px',
-                    'border-radius': '8px',
-                    'border-left': '4px solid #F18F01',
-                    'min-height': '150px',
-                    'font-size': '14px',
-                    'line-height': '1.6'
-                })
+                html.Div(id='fundamental-analysis-text', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','border-left': '4px solid #F18F01','min-height': '150px','font-size': '14px','line-height': '1.6'})
             ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%', 'vertical-align': 'top'})
         ]),
-
-        # 底部：市場展望
         html.Div([
             html.H4("🎯 市場展望與投資建議", style={'color': '#C73E1D', 'margin-bottom': '15px', 'margin-top': '25px'}),
-            html.Div(id='market-outlook-text', style={
-                'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
-                'color': 'white',
-                'padding': '20px',
-                'border-radius': '10px',
-                'min-height': '100px',
-                'font-size': '15px',
-                'line-height': '1.7',
-                'box-shadow': '0 4px 15px rgba(0,0,0,0.1)'
-            })
+            html.Div(id='market-outlook-text', style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','color': 'white','padding': '20px','border-radius': '10px','min-height': '100px','font-size': '15px','line-height': '1.7','box-shadow': '0 4px 15px rgba(0,0,0,0.1)'})
         ])
-    ], style={
-        'margin-top': '30px',
-        'padding': '25px',
-        'background': 'white',
-        'border-radius': '12px',
-        'box-shadow': '0 4px 20px rgba(0,0,0,0.08)',
-        'border': '1px solid #e9ecef'
-    }),
-
-
-    # ==============================================================================
-    # ===== 修改後的多檔股票比較區域 =====
-    # ==============================================================================
+    ], style={'margin-top': '30px','padding': '25px','background': 'white','border-radius': '12px','box-shadow': '0 4px 20px rgba(0,0,0,0.08)','border': '1px solid #e9ecef'}),
     html.Div([
         html.H3("📊 多檔股票比較分析", style={'margin-bottom': '20px'}),
         html.Div([
             html.Div([
                 html.Label("選擇比較股票（最多5檔）:", style={'font-weight': 'bold'}),
-                dcc.Dropdown(
-                    id='comparison-stocks',
-                    options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()],
-                    value=['0050.TW', '2330.TW', '2454.TW'],  # 修改：預設包含0050
-                    multi=True,
-                    style={'margin-bottom': '5px'} # 調整間距
-                ),
-                # 新增：提示文字
-                html.Small(
-                    '（元大台灣50 (0050.TW) 為固定比較基準，不可移除）',
-                    style={'display': 'block', 'font-style': 'italic', 'color': 'gray'}
-                )
+                dcc.Dropdown(id='comparison-stocks', options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()], value=['0050.TW', '2330.TW', '2454.TW'], multi=True, style={'margin-bottom': '5px'}),
+                html.Small('（元大台灣50 (0050.TW) 為固定比較基準，不可移除）', style={'display': 'block', 'font-style': 'italic', 'color': 'gray'})
             ], style={'width': '60%', 'display': 'inline-block'}),
-
             html.Div([
                 html.Label("比���期間:", style={'font-weight': 'bold'}),
-                dcc.Dropdown(
-                    id='comparison-period',
-                    options=[
-                        {'label': '1個月', 'value': '1mo'},
-                        {'label': '3個月', 'value': '3mo'},
-                        {'label': '6個月', 'value': '6mo'},
-                        {'label': '1年', 'value': '1y'}
-                    ],
-                    value='3mo'
-                )
+                dcc.Dropdown(id='comparison-period', options=[{'label': '1個月', 'value': '1mo'},{'label': '3個月', 'value': '3mo'},{'label': '6個月', 'value': '6mo'},{'label': '1年', 'value': '1y'}], value='3mo')
             ], style={'width': '35%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'})
         ]),
-
         html.Div([
-            html.Div([
-                dcc.Graph(id='comparison-chart')
-            ], style={'width': '65%', 'display': 'inline-block'}),
-
-            html.Div([
-                html.H4("比較結果", style={'color': '#2E86AB'}),
-                html.Div(id='comparison-table')
-            ], style={'width': '33%', 'display': 'inline-block', 'margin-left': '2%', 'vertical-align': 'top'})
+            html.Div([dcc.Graph(id='comparison-chart')], style={'width': '65%', 'display': 'inline-block'}),
+            html.Div([html.H4("比較結果", style={'color': '#2E86AB'}), html.Div(id='comparison-table')], style={'width': '33%', 'display': 'inline-block', 'margin-left': '2%', 'vertical-align': 'top'})
         ])
-    ], style={
-        'margin-top': '30px',
-        'padding': '20px',
-        'background': 'white',
-        'border-radius': '10px',
-        'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'
-    }),
-
-    
+    ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
 ])
 
-# 台指期獨立預測回調函數 (新版本)
+# 台指期獨立預測回調函數
 @app.callback(
     [dash.dependencies.Output('taiex-prediction-results', 'children'),
      dash.dependencies.Output('taiex-prediction-chart', 'figure')],
     [dash.dependencies.Input('taiex-prediction-period', 'value')]
 )
 def update_taiex_prediction(predict_days):
-    # 獲取台指期歷史資料
     data = get_stock_data('^TWII', '2y')
-    if data.empty:
-        return html.Div("無法獲取台指期資料"), {}
-
-    # 執行最終日的預測，用於顯示在結果卡片上
+    if data.empty: return html.Div("無法獲取台指期資料"), {}
     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]
-    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]
-    }
+    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])
-
-    # 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
+    prediction_dates, prediction_prices = [last_date], [current_price]
     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 (維持不變)
-    # 根據台股慣例修改顏色
-    color = 'red' if change_pct >= 0 else 'green'
-    arrow = '📈' if change_pct >= 0 else '📉'
-
+    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"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)'
-    })
-
-    # 建立預測趨勢圖
+    ], style={'background': 'rgba(255,255,255,0.1)','padding': '20px','border-radius': '10px','border': '1px solid rgba(255,255,255,0.2)'})
     fig = go.Figure()
-
-    # 歷史價格 (最近30天)
     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)
-    ))
-
-    # --- 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=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',
-        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.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'))
     return result_card, fig
 
 # 更新股價資訊卡片
@@ -703,53 +355,28 @@ def update_taiex_prediction(predict_days):
 )
 def update_stock_info(selected_stock):
     data = get_stock_data(selected_stock, '5d')
-    if data.empty:
-        return html.Div("無法獲取股票資料")
-
+    if data.empty: return html.Div("無法獲取股票資料")
     current_price = data['Close'].iloc[-1]
     prev_price = data['Close'].iloc[-2] if len(data) > 1 else current_price
     change = current_price - prev_price
     change_pct = (change / prev_price) * 100
-
-    # 找出股票中文名稱
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-
-    # 根據台股慣例修改顏色
-    color = 'red' if change >= 0 else 'green'
-    arrow = '▲' if change >= 0 else '▼'
-
+    color, arrow = ('red', '▲') if change >= 0 else ('green', '▼')
     return html.Div([
         html.Div([
             html.H3(f"{stock_name} ({selected_stock})", style={'margin': '0'}),
             html.H2(f"${current_price:.2f}", style={'margin': '5px 0', 'color': color}),
-            html.P(f"{arrow} {change:+.2f} ({change_pct:+.2f}%)",
-                   style={'margin': '0', 'color': color, 'font-weight': 'bold'})
-        ], style={
-            'background': 'white',
-            'padding': '20px',
-            'border-radius': '10px',
-            'box-shadow': '0 2px 10px rgba(0,0,0,0.1)',
-            'display': 'inline-block',
-            'margin-right': '20px'
-        }),
-
+            html.P(f"{arrow} {change:+.2f} ({change_pct:+.2f}%)", style={'margin': '0', 'color': color, 'font-weight': 'bold'})
+        ], style={'background': 'white','padding': '20px','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)','display': 'inline-block','margin-right': '20px'}),
         html.Div([
             html.H4("今日統計", style={'margin': '0 0 10px 0'}),
             html.P(f"最高: ${data['High'].iloc[-1]:.2f}", style={'margin': '5px 0'}),
             html.P(f"最低: ${data['Low'].iloc[-1]:.2f}", style={'margin': '5px 0'}),
             html.P(f"成交量: {data['Volume'].iloc[-1]:,.0f}", style={'margin': '5px 0'})
-        ], style={
-            'background': 'white',
-            'padding': '20px',
-            'border-radius': '10px',
-            'box-shadow': '0 2px 10px rgba(0,0,0,0.1)',
-            'display': 'inline-block'
-        })
+        ], style={'background': 'white','padding': '20px','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)','display': 'inline-block'})
     ])
 
-# ==============================================================================
-# ===== 修改後的主要圖表回呼函式 (合併股價與成交量分佈) =====
-# ==============================================================================
+# 更新主要圖表 (股價與成交量分佈)
 @app.callback(
     dash.dependencies.Output('price-chart', 'figure'),
     [dash.dependencies.Input('stock-dropdown', 'value'),
@@ -758,98 +385,23 @@ def update_stock_info(selected_stock):
 )
 def update_price_chart(selected_stock, period, chart_type):
     data = get_stock_data(selected_stock, period)
-    if data.empty:
-        return {}
-
+    if data.empty: return {}
     data = calculate_technical_indicators(data)
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-
-    # --- 1. 建立共享 Y 軸的子圖 ---
-    # 建立一個 1x2 的網格，設定欄位寬度比例，並共享 Y 軸
-    fig = make_subplots(
-        rows=1, cols=2,
-        shared_yaxes=True,
-        column_widths=[0.8, 0.2],  # 左側圖佔80%，右側圖佔20%
-        horizontal_spacing=0.01    # 子圖間的水平間距
-    )
-
-    # --- 2. 在左側子圖 (col=1) 繪製股價圖 ---
+    fig = make_subplots(rows=1, cols=2, shared_yaxes=True, column_widths=[0.8, 0.2], horizontal_spacing=0.01)
     if chart_type == 'candlestick':
-        # 根據台股慣例修改顏色
-        fig.add_trace(go.Candlestick(
-            x=data.index,
-            open=data['Open'],
-            high=data['High'],
-            low=data['Low'],
-            close=data['Close'],
-            name=stock_name,
-            increasing_line_color='red',  # 上漲為紅色
-            decreasing_line_color='green' # 下跌為綠色
-        ), row=1, col=1)
+        fig.add_trace(go.Candlestick(x=data.index, open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name=stock_name, increasing_line_color='red', decreasing_line_color='green'), row=1, col=1)
     else:
         fig.add_trace(px.line(data, y='Close').data[0], row=1, col=1)
-
-    # 添加移動平均線到左側子圖
-    fig.add_trace(go.Scatter(
-        x=data.index, y=data['MA5'], mode='lines',
-        name='MA5', line=dict(color='orange')
-    ), row=1, col=1)
-    fig.add_trace(go.Scatter(
-        x=data.index, y=data['MA20'], mode='lines',
-        name='MA20', line=dict(color='blue')
-    ), row=1, col=1)
-
-    # --- 3. 在右側子圖 (col=2) 繪製成交量分佈圖 ---
-    # 計算 Volume Profile 數據
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], mode='lines', name='MA5', line=dict(color='orange')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], mode='lines', name='MA20', line=dict(color='blue')), row=1, col=1)
     bin_edges, volume_per_bin, price_centers = calculate_volume_profile(data, num_bins=50)
-
     if volume_per_bin is not None:
-        # 繪製水平長條圖
-        fig.add_trace(go.Bar(
-            orientation='h',
-            y=price_centers,
-            x=volume_per_bin,
-            name='Volume Profile',
-            text=[f'{vol/1000:.0f}k' for vol in volume_per_bin], # 顯示成交量
-            textposition='auto',
-            marker=dict(
-                color='rgba(173, 216, 230, 0.6)',
-                line=dict(color='rgba(30, 144, 255, 0.8)', width=1)
-            )
-        ), row=1, col=2)
-
-    # --- 4. 更新整體圖表佈局 ---
-    fig.update_layout(
-        title_text=f'{stock_name} 股價走勢與成交量分佈',
-        height=500,
-        showlegend=True,
-
-        # 左側子圖的座標軸設定
-        xaxis1=dict(
-            title='日期',
-            type='date',
-            rangeslider_visible=False # 隱藏範圍滑桿，避免干擾佈局
-        ),
-        yaxis1=dict(
-            title='價格 (TWD)'
-        ),
-
-        # 右側子圖的座標軸設定
-        xaxis2=dict(
-            title='成交量',
-            showticklabels=True # 顯示刻度
-        ),
-        yaxis2=dict(
-            showticklabels=False # 因為共享Y軸，所以隱藏右側的Y軸標籤
-        ),
-
-        bargap=0.05 # 長條圖間的間隙
-    )
-
+        fig.add_trace(go.Bar(orientation='h', y=price_centers, x=volume_per_bin, name='Volume Profile', text=[f'{vol/1000:.0f}k' for vol in volume_per_bin], textposition='auto', marker=dict(color='rgba(173, 216, 230, 0.6)', line=dict(color='rgba(30, 144, 255, 0.8)', width=1))), row=1, col=2)
+    fig.update_layout(title_text=f'{stock_name} 股價走勢與成交量分佈', height=500, showlegend=True, xaxis1=dict(title='日期', type='date', rangeslider_visible=False), yaxis1=dict(title='價格 (TWD)'), xaxis2=dict(title='成交量', showticklabels=True), yaxis2=dict(showticklabels=False), bargap=0.05)
     return fig
 
-
-# 新增：進階技術指標圖表
+# 更新進階技術指標圖表
 @app.callback(
     dash.dependencies.Output('advanced-technical-chart', 'figure'),
     [dash.dependencies.Input('technical-indicator-selector', 'value'),
@@ -858,150 +410,55 @@ def update_price_chart(selected_stock, period, chart_type):
 )
 def update_advanced_technical_chart(indicator, selected_stock, period):
     data = get_stock_data(selected_stock, period)
-    if data.empty:
-        return {}
-
+    if data.empty: return {}
     data = calculate_technical_indicators(data)
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-
+    fig = go.Figure() # Fallback
     if indicator == 'RSI':
         fig = go.Figure()
         fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], mode='lines', name='RSI', line=dict(color='purple', width=2)))
         fig.add_hline(y=70, line_dash="dash", line_color="green", annotation_text="超買線(70)")
         fig.add_hline(y=30, line_dash="dash", line_color="red", annotation_text="超賣線(30)")
         fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)")
-        fig.add_hrect(y0=70, y1=100, fillcolor="green", opacity=0.1)
-        fig.add_hrect(y0=0, y1=30, fillcolor="red", opacity=0.1)
-        fig.update_layout(
-            title=f'{stock_name} - RSI 相對強弱指標',
-            xaxis_title='日期',
-            yaxis_title='RSI',
-            height=450,
-            yaxis=dict(range=[0, 100])
-        )
-
+        fig.update_layout(title=f'{stock_name} - RSI 相對強弱指標', xaxis_title='日期', yaxis_title='RSI', height=450, yaxis=dict(range=[0, 100]))
     elif indicator == 'MACD':
-        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
-                           vertical_spacing=0.1,
-                           row_heights=[0.7, 0.3],
-                           subplot_titles=('價格走勢', 'MACD 指標'))
-        fig.add_trace(go.Scatter(
-            x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1.5)
-        ), row=1, col=1)
-        fig.add_trace(go.Scatter(
-            x=data.index, y=data['MACD'], mode='lines', name='MACD (快線)', line=dict(color='blue', width=2)
-        ), row=2, col=1)
-        fig.add_trace(go.Scatter(
-            x=data.index, y=data['MACD_Signal'], mode='lines', name='Signal (慢線)', line=dict(color='red', width=2)
-        ), row=2, col=1)
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.7, 0.3], subplot_titles=('價格走勢', 'MACD 指標'))
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1.5)), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], mode='lines', name='MACD (快線)', line=dict(color='blue', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['MACD_Signal'], mode='lines', name='Signal (慢線)', line=dict(color='red', width=2)), row=2, col=1)
         colors = ['red' if x >= 0 else 'green' for x in data['MACD_Histogram']]
-        fig.add_trace(go.Bar(
-            x=data.index, y=data['MACD_Histogram'], name='MACD柱狀圖', marker_color=colors
-        ), row=2, col=1)
-        fig.add_hline(y=0, line_dash="dash", line_color="gray", row=2, col=1)
-        fig.update_layout(
-            title_text=f'{stock_name} - MACD 指數平滑異同移動平均線',
-            height=550,
-            legend_title_text='圖例',
-            showlegend=True
-        )
-        fig.update_traces(showlegend=False, selector=dict(type='bar'))
-
+        fig.add_trace(go.Bar(x=data.index, y=data['MACD_Histogram'], name='MACD柱狀圖', marker_color=colors), row=2, col=1)
+        fig.update_layout(title_text=f'{stock_name} - MACD 指數平滑異同移動平均線', height=550)
     elif indicator == 'BB':
         fig = go.Figure()
-        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價',
-                                line=dict(color='black', width=2)))
-        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], mode='lines', name='上軌',
-                                line=dict(color='red', width=1, dash='dash')))
-        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Middle'], mode='lines', name='中軌(MA20)',
-                                line=dict(color='blue', width=1)))
-        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], mode='lines', name='下軌',
-                                line=dict(color='green', width=1, dash='dash')))
-        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], mode='lines',
-                                line=dict(color='rgba(0,0,0,0)'), showlegend=False))
-        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], mode='lines',
-                                fill='tonexty', fillcolor='rgba(173,216,230,0.2)',
-                                line=dict(color='rgba(0,0,0,0)'), name='布林通道', showlegend=False))
-        fig.update_layout(
-            title=f'{stock_name} - 布林通道 (20日, 2σ)',
-            xaxis_title='日期',
-            yaxis_title='價格 (TWD)',
-            height=450
-        )
-
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=2)))
+        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], mode='lines', name='上軌', line=dict(color='red', width=1, dash='dash')))
+        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Middle'], mode='lines', name='中軌(MA20)', line=dict(color='blue', width=1)))
+        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], mode='lines', name='下軌', line=dict(color='green', width=1, dash='dash')))
+        fig.update_layout(title=f'{stock_name} - 布林通道 (20日, 2σ)', xaxis_title='日期', yaxis_title='價格 (TWD)', height=450)
     elif indicator == 'KD':
-        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
-                           vertical_spacing=0.1,
-                           row_heights=[0.6, 0.4],
-                           subplot_titles=('價格走勢', 'KD指標'))
-        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價',
-                                line=dict(color='black', width=1)), row=1, col=1)
-        fig.add_trace(go.Scatter(x=data.index, y=data['K'], mode='lines', name='K線',
-                                line=dict(color='blue', width=2)), row=2, col=1)
-        fig.add_trace(go.Scatter(x=data.index, y=data['D'], mode='lines', name='D線',
-                                line=dict(color='red', width=2)), row=2, col=1)
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('價格走勢', 'KD指標'))
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1)), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['K'], mode='lines', name='K線', line=dict(color='blue', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['D'], mode='lines', name='D線', line=dict(color='red', width=2)), row=2, col=1)
         fig.add_hline(y=80, line_dash="dash", line_color="green", annotation_text="超買線(80)", row=2, col=1)
         fig.add_hline(y=20, line_dash="dash", line_color="red", annotation_text="超賣線(20)", row=2, col=1)
-        fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)", row=2, col=1)
-        fig.add_hrect(y0=80, y1=100, fillcolor="green", opacity=0.1, row=2, col=1)
-        fig.add_hrect(y0=0, y1=20, fillcolor="red", opacity=0.1, row=2, col=1)
-        fig.update_layout(
-            title=f'{stock_name} - KD 隨機指標 (9,3,3)',
-            height=500
-        )
-        fig.update_yaxes(range=[0, 100], row=2, col=1)
-
+        fig.update_layout(title=f'{stock_name} - KD 隨機指標 (9,3,3)', height=500, yaxis2_range=[0, 100])
     elif indicator == 'WR':
-        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
-                           vertical_spacing=0.1,
-                           row_heights=[0.6, 0.4],
-                           subplot_titles=('價格走勢', '威廉指標 %R'))
-        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價',
-                                line=dict(color='black', width=1)), row=1, col=1)
-        fig.add_trace(go.Scatter(x=data.index, y=data['Williams_R'], mode='lines', name='威廉%R',
-                                line=dict(color='purple', width=2)), row=2, col=1)
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('價格走勢', '威廉指標 %R'))
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1)), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['Williams_R'], mode='lines', name='威廉%R', line=dict(color='purple', width=2)), row=2, col=1)
         fig.add_hline(y=-20, line_dash="dash", line_color="green", annotation_text="超買線(-20)", row=2, col=1)
         fig.add_hline(y=-80, line_dash="dash", line_color="red", annotation_text="超賣線(-80)", row=2, col=1)
-        fig.add_hline(y=-50, line_dash="dot", line_color="gray", annotation_text="中線(-50)", row=2, col=1)
-        fig.add_hrect(y0=-20, y1=0, fillcolor="green", opacity=0.1, row=2, col=1)
-        fig.add_hrect(y0=-100, y1=-80, fillcolor="red", opacity=0.1, row=2, col=1)
-        fig.update_layout(
-            title=f'{stock_name} - 威廉指標 %R (14日)',
-            height=500
-        )
-        fig.update_yaxes(range=[-100, 0], row=2, col=1)
-
+        fig.update_layout(title=f'{stock_name} - 威廉指標 %R (14日)', height=500, yaxis2_range=[-100, 0])
     elif indicator == 'DMI':
-        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
-                            vertical_spacing=0.1,
-                            row_heights=[0.6, 0.4],
-                            subplot_titles=('價格走勢', 'DMI 指標'))
-        
-        # 過濾掉不穩定的初始數據（通常為14天）
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('價格走勢', 'DMI 指標'))
         data_filtered = data.iloc[14:]
-
-        # 上方：價格線
-        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['Close'], mode='lines', name='收盤價',
-                                line=dict(color='black', width=1)), row=1, col=1)
-
-        # 下方：DMI 線
-        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['+DI'], mode='lines', name='+DI',
-                                line=dict(color='red', width=2)), row=2, col=1)
-        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['-DI'], mode='lines', name='-DI',
-                                line=dict(color='green', width=2)), row=2, col=1)
-        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['ADX'], mode='lines', name='ADX',
-                                line=dict(color='blue', width=2, dash='dot')), row=2, col=1)
-
-        # DMI 參考線
-        fig.add_hline(y=20, line_dash="dash", line_color="gray", annotation_text="ADX強弱線(20)", row=2, col=1)
-
-        fig.update_layout(
-            title=f'{stock_name} - DMI 動向指標 (14日)',
-            height=500,
-            showlegend=True
-        )
-        fig.update_yaxes(range=[0, 100], row=2, col=1)
-
+        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1)), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['+DI'], mode='lines', name='+DI', line=dict(color='red', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['-DI'], mode='lines', name='-DI', line=dict(color='green', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['ADX'], mode='lines', name='ADX', line=dict(color='blue', width=2, dash='dot')), row=2, col=1)
+        fig.update_layout(title=f'{stock_name} - DMI 動向指標 (14日)', height=500, showlegend=True, yaxis2_range=[0, 100])
     return fig
 
 # 更新成交量圖表
@@ -1012,29 +469,11 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
 )
 def update_volume_chart(selected_stock, period):
     data = get_stock_data(selected_stock, period)
-    if data.empty:
-        return {}
-
+    if data.empty: return {}
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-
-    # 根據漲跌決定顏色 (台股慣例)
     colors = ['red' if data['Close'].iloc[i] > data['Open'].iloc[i] else 'green' for i in range(len(data))]
-
-    fig = go.Figure()
-    fig.add_trace(go.Bar(
-        x=data.index,
-        y=data['Volume'],
-        marker_color=colors,
-        name='成交量'
-    ))
-
-    fig.update_layout(
-        title=f'{stock_name} 成交量',
-        xaxis_title='日期',
-        yaxis_title='成交量',
-        height=300
-    )
-
+    fig = go.Figure(go.Bar(x=data.index, y=data['Volume'], marker_color=colors, name='成交量'))
+    fig.update_layout(title=f'{stock_name} 成交量', xaxis_title='日期', yaxis_title='成交量', height=300)
     return fig
 
 # 更新產業分析圖表
@@ -1043,110 +482,45 @@ def update_volume_chart(selected_stock, period):
     [dash.dependencies.Input('stock-dropdown', 'value')]
 )
 def update_industry_analysis(selected_stock):
-    # 獲取多檔股票資料進行產業比較
     industry_data = []
-
-    for symbol in list(TAIWAN_STOCKS.values())[:10]:  # 取前10檔做示範
+    for symbol in list(TAIWAN_STOCKS.values())[:10]:
         data = get_stock_data(symbol, '1mo')
         if not data.empty:
             stock_name = [name for name, symbol_code in TAIWAN_STOCKS.items() if symbol_code == symbol][0]
-            latest_price = data['Close'].iloc[-1]
-            first_price = data['Close'].iloc[0]
-            return_pct = ((latest_price - first_price) / first_price) * 100
-
-            industry_data.append({
-                '股票': stock_name,
-                '代碼': symbol,
-                '月報酬率(%)': return_pct,
-                '產業': INDUSTRY_MAPPING.get(symbol, '其他')
-            })
-
-    if not industry_data:
-        return {}
-
+            return_pct = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
+            industry_data.append({'股票': stock_name, '代碼': symbol, '月報酬率(%)': return_pct, '產業': INDUSTRY_MAPPING.get(symbol, '其他')})
+    if not industry_data: return {}
     df_industry = pd.DataFrame(industry_data)
-
-    # 建立產業表現圓餅圖
-    fig = px.pie(df_industry, values='月報酬率(%)', names='股票',
-                title='各股票月報酬率比較',
-                color_discrete_sequence=px.colors.qualitative.Set3)
-
+    fig = px.pie(df_industry, values='月報酬率(%)', names='股票', title='各股票月報酬率比較', color_discrete_sequence=px.colors.qualitative.Set3)
     fig.update_layout(height=400)
     return fig
 
-# 新增：更新景氣燈號圖表
+# 更新景氣燈號圖表
 @app.callback(
     dash.dependencies.Output('business-climate-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]  # 雖然不會影響圖表，但需要觸發
+    [dash.dependencies.Input('stock-dropdown', 'value')]
 )
 def update_business_climate_chart(selected_stock):
     df = get_business_climate_data()
-
     if df.empty:
-        # 如果沒有資料，顯示提示圖表
-        fig = go.Figure()
-        fig.add_annotation(
-            x=0.5, y=0.5,
-            text="無法載入景氣燈號資料<br>請確認網路連線或國發會網站是否可存取",
-            xref="paper", yref="paper",
-            showarrow=False,
-            font=dict(size=14)
-        )
-        fig.update_layout(
-            title="台灣景氣燈號",
-            height=300,
-            showlegend=False
-        )
+        fig = go.Figure().add_annotation(text="無法載入景氣燈號資料", showarrow=False)
+        fig.update_layout(title="台灣景氣燈號", height=300)
         return fig
-
-    # 定義燈號顏色
     def get_light_color(score):
-        if score >= 32:
-            return 'red'      # 紅燈
-        elif score >= 24:
-            return 'orange'   # 黃紅燈
-        elif score >= 17:
-            return 'yellow'   # 黃燈
-        elif score >= 10:
-            return 'lightgreen'  # 黃藍燈
-        else:
-            return 'blue'     # 藍燈
-
-    # 為每個點設定顏色
+        if score >= 32: return 'red'
+        elif score >= 24: return 'orange'
+        elif score >= 17: return 'yellow'
+        elif score >= 10: return 'lightgreen'
+        else: return 'blue'
     colors = [get_light_color(score) for score in df['Index']]
-
     fig = go.Figure()
-
-    fig.add_trace(go.Scatter(
-        x=df['Date'],
-        y=df['Index'],
-        mode='lines+markers',
-        name='景氣燈號',
-        line=dict(color='darkblue', width=2),
-        marker=dict(
-            size=8,
-            color=colors,
-            line=dict(width=2, color='darkblue')
-        )
-    ))
-
-    # 添加燈號區間線
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['Index'], mode='lines+markers', name='景氣燈號', line=dict(color='darkblue', width=2), marker=dict(size=8, color=colors, line=dict(width=2, color='darkblue'))))
     fig.add_hline(y=32, line_dash="dash", line_color="red", annotation_text="紅燈(32)")
-    fig.add_hline(y=24, line_dash="dash", line_color="orange", annotation_text="黃紅燈(24)")
     fig.add_hline(y=17, line_dash="dash", line_color="yellow", annotation_text="黃燈(17)")
-    fig.add_hline(y=10, line_dash="dash", line_color="lightgreen", annotation_text="黃藍燈(10)")
-
-    fig.update_layout(
-        title="台灣景氣燈號走勢",
-        xaxis_title='日期',
-        yaxis_title='燈號分數',
-        height=300,
-        yaxis=dict(range=[0, 40])
-    )
-
+    fig.update_layout(title="台灣景氣燈號走勢", xaxis_title='日期', yaxis_title='燈號分數', height=300, yaxis=dict(range=[0, 40]))
     return fig
 
-# 新增：更新分析師觀點
+# 更新分析師觀點
 @app.callback(
     [dash.dependencies.Output('technical-analysis-text', 'children'),
      dash.dependencies.Output('fundamental-analysis-text', 'children'),
@@ -1155,227 +529,51 @@ def update_business_climate_chart(selected_stock):
      dash.dependencies.Input('period-dropdown', 'value')]
 )
 def update_analysis_text(selected_stock, period):
-    # 獲取股票資料進行分析
     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:
-        return "無法獲取資料進行分析", "無法獲取資料進行分析", "無法獲取資料進行分析"
-
-    # 計算技術指標
+    if data.empty: return "無法獲取資料", "無法獲取資料", "無法獲取資料"
     data = calculate_technical_indicators(data)
-
-    # 基本數據
     current_price = data['Close'].iloc[-1]
     price_change = ((current_price - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
-    volume_avg = data['Volume'].mean()
-    recent_volume = data['Volume'].iloc[-5:].mean()
     rsi_current = data['RSI'].iloc[-1] if not pd.isna(data['RSI'].iloc[-1]) else 50
-
-    # 新增技術指標數據
     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
-    bb_position = data['BB_Position'].iloc[-1] if not pd.isna(data['BB_Position'].iloc[-1]) else 0.5
-    k_current = data['K'].iloc[-1] if not pd.isna(data['K'].iloc[-1]) else 50
-    d_current = data['D'].iloc[-1] if not pd.isna(data['D'].iloc[-1]) else 50
-    pdi_current = data['+DI'].iloc[-1] if not pd.isna(data['+DI'].iloc[-1]) else 0
-    ndi_current = data['-DI'].iloc[-1] if not pd.isna(data['-DI'].iloc[-1]) else 0
-    adx_current = data['ADX'].iloc[-1] if not pd.isna(data['ADX'].iloc[-1]) else 0
-
-
-    # 技術面分析
     technical_text = html.Div([
-        html.P([
-            html.Strong("價格趨勢："),
-            f"近期{period}期間內，{stock_name}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"目前為{rsi_current:.1f}，",
-            html.Span(
-                "處於超買區間" if rsi_current > 70 else "處於超賣區間" 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線({macd_current:.3f})",
-            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"信號線({macd_signal_current:.3f})，",
-            f"顯示{'多頭' if macd_current > macd_signal_current else '空頭'}格局。"
-        ]),
-        html.P([
-            html.Strong("布林通道："),
-            f"股價位於通道",
-            html.Span(
-                "上半部" if bb_position > 0.8 else "下半部" if bb_position < 0.2 else "中段",
-                style={'color': 'green' if bb_position > 0.8 else 'red' if bb_position < 0.2 else 'blue', 'font-weight': 'bold'}
-            ),
-            f"({bb_position*100:.0f}%)，",
-            f"{'壓力較大' if bb_position > 0.8 else '支撐較強' if bb_position < 0.2 else '整理格局'}。"
-        ]),
-        html.P([
-            html.Strong("KD指標："),
-            f"K值({k_current:.1f})",
-            html.Span(
-                "高於" if k_current > d_current else "低於",
-                style={'color': 'red' if k_current > d_current else 'green', 'font-weight': 'bold'}
-            ),
-            f"D值({d_current:.1f})，",
-            html.Span(
-                "超買警戒" if k_current > 80 else "超賣關注" if k_current < 20 else "正常區間",
-                style={'color': 'green' if k_current > 80 else 'red' if k_current < 20 else 'blue', 'font-weight': 'bold'}
-            ),
-            "。"
-        ]),
-        html.P([
-            html.Strong("DMI指標："),
-            f"目前+DI ({pdi_current:.1f}) 與 -DI ({ndi_current:.1f})，",
-            html.Span(
-                "呈現多頭趨勢" if pdi_current > ndi_current else "呈現空頭趨勢",
-                style={'color': 'red' if pdi_current > ndi_current else 'green', 'font-weight': 'bold'}
-            ),
-            f"。ADX值為 {adx_current:.1f}，顯示市場趨勢{'強勁' if adx_current > 25 else '不明顯' if adx_current < 20 else '有趨勢'}"
-        ]),
-        html.P([
-            html.Strong("成交量分析："),
-            f"近期成交量{'放大' if recent_volume > volume_avg * 1.2 else '萎縮' if recent_volume < volume_avg * 0.8 else '平穩'}，",
-            f"顯示市場{'關注度提升' if recent_volume > volume_avg * 1.2 else '觀望氣氛濃厚' if recent_volume < volume_avg * 0.8 else '交投正常'}。"
-        ])
+        html.P([html.Strong("價格趨勢："), f"近期{period}期間內，{stock_name}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"目前為{rsi_current:.1f}，", html.Span("處於超買區間" if rsi_current > 70 else "處於超賣區間" 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線({macd_current:.3f})", 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"信號線({macd_signal_current:.3f})，", f"顯示{'多頭' if macd_current > macd_signal_current else '空頭'}格局。"]),
     ])
-
-    # 基本面分析
     industry = INDUSTRY_MAPPING.get(selected_stock, '綜合')
     fundamental_text = html.Div([
-        html.P([
-            html.Strong("產業地位："),
-            f"{stock_name}屬於{industry}產業，在產業鏈中具有",
-            html.Span("重要地位" if selected_stock in ['2330.TW', '2454.TW', '2317.TW'] else "一定影響力",
-                     style={'font-weight': 'bold'}),
-            "。"
-        ]),
-        html.P([
-            html.Strong("營運展望："),
-            f"考量{industry}產業前景及公司基本面，建議持續關注季報表現及未來指引。"
-        ]),
-        html.P([
-            html.Strong("風險評估："),
-            "注意產業週期性變化、國際競爭及法規環境變化等風險因子。"
-        ])
+        html.P([html.Strong("產業地位："), f"{stock_name}屬於{industry}產業，在產業鏈中具有", html.Span("重要地位" if selected_stock in ['2330.TW', '2454.TW', '2317.TW'] else "一定影響力", style={'font-weight': 'bold'}), "。"]),
+        html.P([html.Strong("營運展望："), f"建議持續關注季報表現及未來指引。"]),
     ])
-
-    # 市場展望
-    if price_change > 10:
-        outlook_tone = "謹慎樂觀"
-        outlook_color = "#dc3545"
-    elif price_change < -10:
-        outlook_tone = "保守觀望"
-        outlook_color = "#28a745"
-    else:
-        outlook_tone = "中性持平"
-        outlook_color = "#ffc107"
-
+    outlook_tone = "謹慎樂觀" if price_change > 10 else "保守觀望" if price_change < -10 else "中性持平"
     market_outlook = html.Div([
-        html.P([
-            html.Strong("整體評估：", style={'font-size': '16px'}),
-            f"基於技術面及基本面分析，對{stock_name}採取",
-            html.Span(f"{outlook_tone}", style={'color': outlook_color, 'font-weight': 'bold', 'font-size': '16px'}),
-            "態度。"
-        ]),
-        html.P([
-            html.Strong("投資建議："),
-            "建議投資人根據自身風險承受能力，採取適當的資產配置策略。短線操作注意技術指標，長線投資關注基本面變化。"
-        ]),
-        html.P([
-            html.Strong("風險提醒："),
-            "股票投資具有風險，過去績效不代表未來表現，投資前請詳閱公開說明書並審慎評估。"
-        ], style={'font-style': 'italic', 'font-size': '13px'})
+        html.P([html.Strong("整體評估："), f"基於技術面及基本面分析，對{stock_name}採取", html.Span(f"{outlook_tone}", style={'font-weight': 'bold'}), "態度。"]),
+        html.P([html.Strong("投資建議："), "短線操作注意技術指標，長線投資關注基本面變化。"]),
     ])
-
     return technical_text, fundamental_text, market_outlook
 
-# 新增：更新PMI圖表
+# 更新PMI圖表
 @app.callback(
     dash.dependencies.Output('pmi-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]  # 雖然不會影響圖表，但需要觸發
+    [dash.dependencies.Input('stock-dropdown', 'value')]
 )
 def update_pmi_chart(selected_stock):
     df = get_pmi_data()
-
     if df.empty:
-        # 如果沒有資料，顯示提示圖表
-        fig = go.Figure()
-        fig.add_annotation(
-            x=0.5, y=0.5,
-            text="無法載入PMI資料<br>請確認 taiwan_pmi.csv 檔案是否存在",
-            xref="paper", yref="paper",
-            showarrow=False,
-            font=dict(size=14)
-        )
-        fig.update_layout(
-            title="台灣PMI指數",
-            height=300,
-            showlegend=False
-        )
+        fig = go.Figure().add_annotation(text="無法載入PMI資料", showarrow=False)
+        fig.update_layout(title="台灣PMI指數", height=300)
         return fig
-
-    # 定義PMI顏色 (50以上擴張，以下緊縮)
-    def get_pmi_color(value):
-        return 'red' if value >= 50 else 'green'
-
-    colors = [get_pmi_color(value) for value in df['Index']]
-
+    colors = ['red' if value >= 50 else 'green' for value in df['Index']]
     fig = go.Figure()
-
-    fig.add_trace(go.Scatter(
-        x=df['Date'],
-        y=df['Index'],
-        mode='lines+markers',
-        name='PMI指數',
-        line=dict(color='darkblue', width=2),
-        marker=dict(
-            size=8,
-            color=colors,
-            line=dict(width=2, color='darkblue')
-        )
-    ))
-
-    # 添加榮枯線
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['Index'], mode='lines+markers', name='PMI指數', line=dict(color='darkblue', width=2), marker=dict(size=8, color=colors, line=dict(width=2, color='darkblue'))))
     fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線(50)")
-
-    # 添加背景色區域
-    fig.add_hrect(
-        y0=50, y1=60,
-        fillcolor="lightcoral", opacity=0.2,
-        annotation_text="擴張區間", annotation_position="top left"
-    )
-    fig.add_hrect(
-        y0=40, y1=50,
-        fillcolor="lightgreen", opacity=0.2,
-        annotation_text="緊縮區間", annotation_position="bottom left"
-    )
-
-    fig.update_layout(
-        title="台灣PMI指數走勢",
-        xaxis_title='日期',
-        yaxis_title='PMI指數',
-        height=300,
-        yaxis=dict(range=[35, 60])
-    )
-
+    fig.update_layout(title="台灣PMI指數走勢", xaxis_title='日期', yaxis_title='PMI指數', height=300, yaxis=dict(range=[35, 60]))
     return fig
 
-
-# ==============================================================================
-# ===== 修改後的多檔股票比較回呼函式 =====
-# ==============================================================================
+# 更新多檔股票比較
 @app.callback(
     [dash.dependencies.Output('comparison-chart', 'figure'),
      dash.dependencies.Output('comparison-table', 'children')],
@@ -1383,243 +581,113 @@ def update_pmi_chart(selected_stock):
      dash.dependencies.Input('comparison-period', 'value')]
 )
 def update_comparison_analysis(selected_stocks, period):
-    # --- 新增：確保 0050.TW 始終存在 ---
     fixed_stock = '0050.TW'
-    # 如果列表為空或 None，則只顯示 0050
-    if not selected_stocks:
-        selected_stocks = [fixed_stock]
-    # 如果 0050 不在列表中，則將其插入到最前面
-    elif fixed_stock not in selected_stocks:
-        selected_stocks.insert(0, fixed_stock)
-    # --- 修改結束 ---
-
-    if not selected_stocks:
-        return {}, html.Div("請選擇要比較的股票")
-
-    # 限制最多5檔
+    if not selected_stocks: selected_stocks = [fixed_stock]
+    elif fixed_stock not in selected_stocks: selected_stocks.insert(0, fixed_stock)
     selected_stocks = selected_stocks[:5]
-
     fig = go.Figure()
     comparison_data = []
-
     for stock in selected_stocks:
         data = get_stock_data(stock, period)
         if not data.empty:
-            # 安全地獲取股票名稱，如果找不到則使用代碼本身
             stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == stock), stock)
-
-            # 正規化價格（以期初為基準100）
             normalized_prices = (data['Close'] / data['Close'].iloc[0]) * 100
-
-            fig.add_trace(go.Scatter(
-                x=data.index,
-                y=normalized_prices,
-                mode='lines',
-                name=stock_name,
-                line=dict(width=2)
-            ))
-
-            # 計算績效數據
+            fig.add_trace(go.Scatter(x=data.index, y=normalized_prices, mode='lines', name=stock_name, line=dict(width=2)))
             total_return = ((data['Close'].iloc[-1] / data['Close'].iloc[0]) - 1) * 100
-            volatility = data['Close'].pct_change().std() * np.sqrt(252) * 100  # 年化波動率
-
-            comparison_data.append({
-                'name': stock_name,
-                'return': total_return,
-                'volatility': volatility,
-                'current_price': data['Close'].iloc[-1]
-            })
-
-    fig.update_layout(
-        title=f'股票績效比較 - {period}',
-        xaxis_title='日期',
-        yaxis_title='相對績效 (基期=100)',
-        height=400,
-        hovermode='x unified'
-    )
-
-    # 建立比較表格
+            volatility = data['Close'].pct_change().std() * np.sqrt(252) * 100
+            comparison_data.append({'name': stock_name, 'return': total_return, 'volatility': volatility, 'current_price': data['Close'].iloc[-1]})
+    fig.update_layout(title=f'股票績效比較 - {period}', xaxis_title='日期', yaxis_title='相對績效 (基期=100)', height=400, hovermode='x unified')
     if comparison_data:
         table_rows = []
         for item in sorted(comparison_data, key=lambda x: x['return'], reverse=True):
             color = 'red' if item['return'] > 0 else 'green'
-            table_rows.append(
-                html.Tr([
-                    html.Td(item['name'], style={'font-weight': 'bold'}),
-                    html.Td(f"{item['return']:+.1f}%", style={'color': color, 'font-weight': 'bold'}),
-                    html.Td(f"{item['volatility']:.1f}%"),
-                    html.Td(f"${item['current_price']:.2f}")
-                ])
-            )
-
-        table = html.Table([
-            html.Thead([
-                html.Tr([
-                    html.Th("股票", style={'text-align': 'center'}),
-                    html.Th("報酬率", style={'text-align': 'center'}),
-                    html.Th("波動率", style={'text-align': 'center'}),
-                    html.Th("現價", style={'text-align': 'center'})
-                ])
-            ]),
-            html.Tbody(table_rows)
-        ], style={
-            'width': '100%',
-            'border-collapse': 'collapse',
-            'font-size': '12px'
-        })
-
+            table_rows.append(html.Tr([html.Td(item['name'], style={'font-weight': 'bold'}), html.Td(f"{item['return']:+.1f}%", style={'color': color, 'font-weight': 'bold'}), html.Td(f"{item['volatility']:.1f}%"), html.Td(f"${item['current_price']:.2f}")]))
+        table = html.Table([html.Thead(html.Tr([html.Th("股票"), html.Th("報酬率"), html.Th("波動率"), html.Th("現價")])), html.Tbody(table_rows)], style={'width': '100%'})
         return fig, table
-
     return fig, html.Div("無可比較資料")
 
-# 新增：市場情緒分析
+
+# ==============================================================================
+# ===== 【修改】市場情緒與新聞分析 (使用真實 BERT 模型) =====
+# ==============================================================================
 @app.callback(
     [dash.dependencies.Output('sentiment-gauge', 'children'),
      dash.dependencies.Output('news-summary', 'children')],
-    [dash.dependencies.Input('stock-dropdown', 'value')]
+    [dash.dependencies.Input('stock-dropdown', 'value')] # 觸發條件不變
 )
 def update_sentiment_analysis(selected_stock):
-    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-    
-    # --- 修改部分：從 CSV 檔案讀取新聞 ---
-    # 假設 CSV 檔案名稱是 'news_YYYY-MM-DD.csv' 並且與主程式在同一目錄
-    # 您需要確保新聞檔案的命名規則與此一致，或者修改檔案名稱的獲取方式
-    today_str = datetime.now().strftime('%Y-%m-%d') # 獲取今天的日期
-    news_csv_filename = f'news_{today_str}.csv' # 例如: news_2025-09-12.csv
-    
-    news_items_content = []
-    try:
-        # 檢查檔案是否存在
-        if not os.path.exists(news_csv_filename):
-            print(f"警告：新聞檔案 '{news_csv_filename}' 不存在。將使用預設新聞。")
-            # 如果檔案不存在，可以選擇顯示錯誤訊息或使用預設模擬新聞
-            news_items_content = [
-                f"⚠️ 無法載入新聞檔案 '{news_csv_filename}'，顯示預設內容。",
-                f"📈 {stock_name} 股價受到市場關注。",
-                f"📉 投資者情緒趨於謹慎。",
-            ]
+    # 檢查 predictor 是否成功初始化
+    if predictor is None:
+        error_fig = go.Figure().add_annotation(text="情緒指標模型載入失敗", showarrow=False)
+        error_fig.update_layout(height=200)
+        return dcc.Graph(figure=error_fig), html.P("新聞分析模型載入失敗，請檢查後台日誌。")
+
+    # --- 1. 從 predictor 獲取新聞情緒平均分數 ---
+    sentiment_score_raw = predictor.get_news_index()
+
+    # --- 2. 建立情緒指標儀表板 ---
+    if sentiment_score_raw is not None:
+        # **重要假設**：假設您模型的輸出範圍在 [-1, 1] 之間 (負相關映到-1, 正相關映到1)
+        # 我們需要將其正規化到儀表板的 [0, 100] 範圍內
+        # 公式: normalized_score = (raw_score + 1) * 50
+        sentiment_score_normalized = (sentiment_score_raw + 1) * 50
+        # 確保分數不會超出 0-100 的範圍
+        sentiment_score_normalized = max(0, min(100, sentiment_score_normalized))
+
+        # 根據分數決定顏色和標籤
+        if sentiment_score_normalized >= 65:
+            bar_color, level_text = "#5cb85c", "樂觀" # 綠色
+        elif sentiment_score_normalized >= 35:
+            bar_color, level_text = "#f0ad4e", "中性" # 黃色
         else:
-            news_df = pd.read_csv(news_csv_filename)
-            # 確保 CSV 檔案中存在 '內容' 欄位
-            if '內容' in news_df.columns:
-                news_items_content = news_df['內容'].tolist()
-            else:
-                print(f"警告：CSV 檔案 '{news_csv_filename}' 中未找到 '內容' 欄位。")
-                news_items_content = [
-                    f"⚠️ CSV 檔案 '{news_csv_filename}' 格式錯誤，未找到 '內容' 欄位。",
-                    f"📈 {stock_name} 股價動態。",
-                    f"📉 市場消息更新。",
-                ]
-    except FileNotFoundError:
-        print(f"警告：新聞檔案 '{news_csv_filename}' 讀取時發生 FileNotFoundError。")
-        news_items_content = [
-            f"⚠️ 讀取新聞檔案 '{news_csv_filename}' 時發生錯誤。",
-            f"📈 {stock_name} 股價分析。",
-            f"📉 投資者情緒參考。",
-        ]
-    except Exception as e:
-        print(f"讀取 CSV 檔案 '{news_csv_filename}' 時發生未預期錯誤：{e}")
-        news_items_content = [
-            f"⚠️ 讀取新聞檔案 '{news_csv_filename}' 時發生錯誤：{e}",
-            f"📈 {stock_name} 股價資訊。",
-            f"📉 市場趨勢分析。",
-        ]
-
-    # --- 接著對讀取的新聞內容進行情緒分析 ---
-    total_sentiment_score = 0
-    analyzed_news_html = []
-    sentiment_mapping = {'negative': 0, 'neutral': 50, 'positive': 100} # BERT 輸出的映射
-
-    if not news_items_content: # 如果 news_items_content 為空
-        analyzed_news_html.append(html.P("目前沒有新聞可供分析。", style={'font-style': 'italic'}))
-        avg_sentiment_score = 50 # 預設為中性
-    else:
-        for news in news_items_content:
-            # 使用 BERT 模型進行預測
-            # predict_sentiment 應返回 (sentiment_label, probability_score)
-            # 例如：('positive', 0.95)
-            sentiment_label, probability_score = predict_sentiment(news)
-            
-            # 將情緒標籤轉換為數值分數
-            numeric_score = sentiment_mapping.get(sentiment_label, 50) # 預設為中性
-            
-            total_sentiment_score += numeric_score
-
-            # 設置顯示顏���和表情符號
-            sentiment_emoji = '⚪'
-            sentiment_color = 'gray'
-            if sentiment_label == 'positive':
-                sentiment_emoji = '🟢'
-                sentiment_color = 'green'
-            elif sentiment_label == 'neutral':
-                sentiment_emoji = '🟡'
-                sentiment_color = 'orange'
-            elif sentiment_label == 'negative':
-                sentiment_emoji = '🔴'
-                sentiment_color = 'red'
+            bar_color, level_text = "#d9534f", "悲觀" # 紅色
             
-            # 建立新聞摘要的 HTML 元素
-            analyzed_news_html.append(
-                html.P([
-                    html.Span(f"{sentiment_emoji} ", style={'font-size': '1.2em'}),
-                    html.A(news, href="#", style={
-                        'color': sentiment_color,
-                        'text-decoration': 'none',
-                        'font-weight': 'bold'
-                    }),
-                    html.Br(),
-                    html.Small(f"情緒: {sentiment_label}, 信賴度: {probability_score:.2%}", style={'margin-left': '20px', 'color': 'gray'})
-                ], style={
-                    'margin': '8px 0',
-                    'padding': '8px',
-                    'background': '#f8f9fa',
-                    'border-radius': '5px',
-                    'border-left': f'3px solid {sentiment_color}',
-                    'font-size': '13px'
-                })
-            )
-
-        # 計算平均情緒分數
-        avg_sentiment_score = total_sentiment_score / len(news_items_content)
-
-    # --- 建立情緒指標圓形圖 (Gauge Chart) ---
-    gauge_fig = go.Figure(go.Indicator(
-        mode = "gauge+number",
-        value = avg_sentiment_score,
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        title = {'text': "市場情緒指數"},
-        gauge = {
-            'axis': {'range': [0, 100], 'tickvals': [0, 25, 50, 75, 100], 'ticktext': ['極度負面', '', '中性', '', '極度正面']},
-            'bar': {'color': "#667eea"}, # 預設的 bar 顏色，可選
-            'steps': [
-                {'range': [0, 30], 'color': "#e74c3c"},     # 紅色 (負面)
-                {'range': [30, 70], 'color': "#f1c40f"},    # 黃色 (中性)
-                {'range': [70, 100], 'color': "#2ecc71"}    # 綠色 (正面)
-            ],
-            # 您可以在這裡調整 threshold 的設定，如果需要的話
-            # 'threshold': {
-            #     'line': {'color': "red", 'width': 4},
-            #     'thickness': 0.75,
-            #     'value': 75 # 例如，設定一個門檻值
-            # }
-        }
-    ))
+        gauge_fig = go.Figure(go.Indicator(
+            mode = "gauge+number",
+            value = sentiment_score_normalized,
+            domain = {'x': [0, 1], 'y': [0, 1]},
+            title = {'text': f"昨日市場情緒: {level_text}", 'font': {'size': 18}},
+            gauge = {
+                'axis': {'range': [0, 100], 'tickwidth': 1, 'tickcolor': "darkblue"},
+                'bar': {'color': bar_color, 'thickness': 0.8},
+                'steps': [
+                    {'range': [0, 35], 'color': "rgba(217, 83, 79, 0.2)"},
+                    {'range': [35, 65], 'color': "rgba(240, 173, 78, 0.2)"},
+                    {'range': [65, 100], 'color': "rgba(92, 184, 92, 0.2)"}
+                ],
+            }
+        ))
+        gauge_fig.update_layout(height=200, margin=dict(l=30, r=30, t=50, b=20))
+        gauge_content = dcc.Graph(figure=gauge_fig)
+    else:
+        # 處理無法計算分數的情況 (例如 API 失敗或沒有新聞)
+        error_fig = go.Figure().add_annotation(text="今日尚無情緒分數", showarrow=False)
+        error_fig.update_layout(height=200)
+        gauge_content = dcc.Graph(figure=error_fig)
+
+
+    # --- 3. 從 predictor 獲取分數最高的3則新聞 ---
+    top_news_list = predictor.get_news()
+
+    # --- 4. 建立新聞摘要元件 ---
+    if top_news_list: # 如果列表不為空
+        news_content = html.Div([
+            html.P(f"• {news}", style={
+                'margin': '8px 0',
+                'padding-left': '5px',
+                'font-size': '14px',
+                'line-height': '1.5'
+            }) for news in top_news_list
+        ])
+    elif top_news_list == []: # 如果是空列表
+         news_content = html.P("昨日無重大相關新聞。", style={'text-align': 'center', 'padding-top': '50px'})
+    else: # 如果是 None (代表讀取檔案出錯)
+        news_content = html.P("讀取新聞時發生錯誤。", style={'text-align': 'center', 'padding-top': '50px'})
 
-    gauge_fig.update_layout(height=200, margin=dict(l=20, r=20, t=40, b=20))
+    return gauge_content, news_content
 
-    return dcc.Graph(figure=gauge_fig), html.Div(analyzed_news_html)
 
-# 在 Colab 中執行的設定
+# 主程式執行
 if __name__ == '__main__':
-    # 在執行前先測試檔案讀取
-    print("測試檔案讀取...")
-    business_data = get_business_climate_data()
-    pmi_data = get_pmi_data()
-
-    if not business_data.empty:
-        print(f"景氣燈號資料預覽：\n{business_data.head()}")
-    if not pmi_data.empty:
-        print(f"PMI資料預覽：\n{pmi_data.head()}")
-
     # 在 Hugging Face Spaces 中執行
     app.run(host="0.0.0.0", port=7860, debug=False)