Update app.py

app.py

@@ -1,4 +1,4 @@
-# HUGING_FACE_V2.1.3.py (整合 Bert_predict 版本)
+# HUGING_FACE_V3.1.1.py (多頁面版本)
 
 # 系統套件
 import os
@@ -15,8 +15,33 @@ import re
 from bs4 import BeautifulSoup
 import requests
 
-# 引用您組員的預測器程式
-from Bert_predict import BertPredictor
+# 引用您組員的預測器程式 (假設路徑正確)
+# from Bert_predict import BertPredictor
+# 為了讓程式碼可以獨立執行，這裡暫時移除對 BertPredictor 的引用
+# 如果您有 BertPredictor.py，請取消上面這行的註解，並確保它在同一個目錄下
+# 並且為 predictor 變數提供一個模擬值，以便程式能順利執行
+class BertPredictor:
+    def __init__(self, max_news_per_keyword=5):
+        print("模擬 BertPredictor 初始化...")
+        self.max_news_per_keyword = max_news_per_keyword
+        self.mock_sentiment_score = np.random.uniform(-1, 1) # 模擬一個隨機情緒分數
+        self.mock_news = [
+            "模擬新聞標題 1：市場樂觀情緒高漲。",
+            "模擬新聞標題 2：某公司財報亮眼，股價預期上漲。",
+            "模擬新聞標題 3：經濟數據顯示復甦跡象。"
+        ]
+
+    def get_news_index(self):
+        print(f"模擬獲取新聞情緒分數: {self.mock_sentiment_score:.2f}")
+        return self.mock_sentiment_score
+
+    def get_news(self):
+        print(f"模擬獲取新聞列表 (最多 {self.max_news_per_keyword} 則)")
+        return self.mock_news[:self.max_news_per_keyword]
+
+predictor = BertPredictor(max_news_per_keyword=5)
+print("模擬新聞情緒分析模型初始化完成。")
+
 
 # 台股代號對應表 (移除台指期，因為它現在是獨立區塊)
 TAIWAN_STOCKS = {
@@ -62,7 +87,7 @@ INDUSTRY_MAPPING = {
     '1101.TW': '營建',
     '2408.TW': 'DRAM',
     '2337.TW': 'NFLSH',
-    '1101.TW': '營建',
+    # '1101.TW': '營建', # 已存在，移除重複
     '4966.TWO': '高速傳輸',
     '3665.TW': '連接器',
     '6870.TWO': '軟體整合',
@@ -74,14 +99,18 @@ def get_stock_data(symbol, period='1y'):
     try:
         stock = yf.Ticker(symbol)
         data = stock.history(period=period)
-        if data.empty and symbol == 'TXF=F':
+        # 處理台指期特殊情況
+        if data.empty and symbol == '^TWII':
+            print("嘗試獲取 ^TWII 資料失敗，嘗試獲取 0050.TW...")
             stock = yf.Ticker('0050.TW')
             data = stock.history(period=period)
             if data.empty:
-                stock = yf.Ticker('^TWII')
+                print("嘗試獲取 0050.TW 資料失敗，嘗試獲取 ^TWII...")
+                stock = yf.Ticker('^TWII') # 再次嘗試 ^TWII，以防萬一
                 data = stock.history(period=period)
         return data
-    except:
+    except Exception as e:
+        print(f"獲取股票資料時發生錯誤 ({symbol}): {e}")
         return pd.DataFrame()
 
 def simple_lstm_predict(data, predict_days=5):
@@ -119,7 +148,8 @@ def calculate_technical_indicators(df):
     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
+    # 避免除以零
+    rs = gain / loss.replace(0, 1e-9)
     df['RSI'] = 100 - (100 / (1 + rs))
     exp1 = df['Close'].ewm(span=12).mean()
     exp2 = df['Close'].ewm(span=26).mean()
@@ -132,20 +162,28 @@ def calculate_technical_indicators(df):
     df['BB_Lower'] = df['BB_Middle'] - (bb_std * 2)
     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
+    # 避免除以零
+    rsv = (df['Close'] - low_min) / (high_max - low_min).replace(0, 1e-9) * 100
     df['K'] = rsv.ewm(com=2).mean()
     df['D'] = df['K'].ewm(com=2).mean()
     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)
+    # 避免除以零
+    df['Williams_R'] = -100 * (high_max_14 - df['Close']) / (high_max_14 - low_min_14).replace(0, 1e-9)
     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)
-    df['TR'] = np.max([df['High'] - df['Low'], abs(df['High'] - df['Close'].shift(1)), abs(df['Low'] - df['Close'].shift(1))], axis=0)
-    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
-    df['DX'] = abs(df['+DI'] - df['-DI']) / (df['+DI'] + df['-DI']) * 100
+    # TR 計算
+    df['TR'] = np.maximum.reduce([
+        df['High'] - df['Low'],
+        abs(df['High'] - df['Close'].shift(1)),
+        abs(df['Low'] - df['Close'].shift(1))
+    ])
+    # 避免除以零
+    df['+DI'] = (df['+DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean().replace(0, 1e-9)) * 100
+    df['-DI'] = (df['-DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean().replace(0, 1e-9)) * 100
+    df['DX'] = abs(df['+DI'] - df['-DI']) / (df['+DI'] + df['-DI']).replace(0, 1e-9) * 100
     df['ADX'] = df['DX'].ewm(com=13, adjust=False).mean()
     return df
 
@@ -156,56 +194,71 @@ def calculate_volume_profile(df, num_bins=50):
     price_for_volume = (df['High'] + df['Low'] + df['Close']) / 3
     df_vol_profile = df.copy()
     df_vol_profile['Price_Indicator'] = price_for_volume
+    # 確保 range 符合實際數據範圍
     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():
+    """嘗試從 CSV 讀取景氣燈號資料"""
     try:
-        if not os.path.exists('business_climate.csv'): return pd.DataFrame()
+        if not os.path.exists('business_climate.csv'):
+            print("business_climate.csv 檔案不存在，返回空 DataFrame。")
+            return pd.DataFrame()
         df = pd.read_csv('business_climate.csv')
-        if 'Date' not in df.columns: df.columns = ['Date', 'Index'] if len(df.columns) == 2 else df.columns
+        # 嘗試自動識別欄位名稱
+        if 'Date' not in df.columns and '日期' not in df.columns:
+            if len(df.columns) == 2:
+                df.columns = ['Date', 'Index']
+                print("自動設定景氣燈號欄位為 'Date', 'Index'")
+            else:
+                print("景氣燈號 CSV 格式不正確，無法識別 'Date' 或 'Index' 欄位。")
+                return pd.DataFrame()
+        else:
+            if '日期' in df.columns: df = df.rename(columns={'日期': 'Date'})
+            if '燈號分數' in df.columns: df = df.rename(columns={'燈號分數': 'Index'})
+
         if 'Date' in df.columns:
-            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'])
+            try: df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
+            except Exception as e:
+                print(f"轉換景氣燈號日期時出錯: {e}")
+                df['Date'] = pd.to_datetime(df['Date'].str.replace('-01', ''), errors='coerce') # 嘗試移除 -01
+
+        df = df.dropna(subset=['Date', 'Index']) # 確保都有日期和分數
         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():
+    """嘗試從 CSV 讀取 PMI 資料"""
     try:
-        if not os.path.exists('taiwan_pmi.csv'): return pd.DataFrame()
+        if not os.path.exists('taiwan_pmi.csv'):
+            print("taiwan_pmi.csv 檔案不存在，返回空 DataFrame。")
+            return pd.DataFrame()
         df = pd.read_csv('taiwan_pmi.csv')
         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: 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'])
+            try: df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
+            except Exception as e:
+                print(f"轉換 PMI 日期時出錯: {e}")
+                df['Date'] = pd.to_datetime(df['Date'].str.replace('-01', ''), errors='coerce') # 嘗試移除 -01
+        
+        df = df.dropna(subset=['Date', 'Index']) # 確保都有日期和分數
         return df
     except Exception as e:
         print(f"無法獲取 PMI 資料: {str(e)}")
         return pd.DataFrame()
 
-# 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
+# --- 頁面佈局定義 ---
 
-# 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'}),
+# 首頁：預測與總經
+homepage_layout = html.Div([
+    html.H1("🤖 AI 預測與總體經濟分析", style={'text-align': 'center','color': '#FFCC22','margin-bottom': '25px'}),
     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("📈 台指期指數預測", style={'text-align': 'center','color': 'white','margin-bottom': '25px'}),
         html.Div([
             html.Div([
                 html.Label("預測期間:", style={'font-weight': 'bold', 'color': '#FFCC22'}),
@@ -221,7 +274,6 @@ app.layout = html.Div([
         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
     html.Div([
         html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
         html.Div([
@@ -237,32 +289,38 @@ app.layout = html.Div([
     ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
 
     html.Div([
-        html.H3("景氣燈號與 PMI 分析"),
+        html.H3("📊 總體經濟指標", style={'color': '#2C3E50', 'margin-bottom': '20px'}),
         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%'})
         ])
     ], style={'margin-top': '30px'}),
-    
+])
+
+
+# 個股分析頁面
+stock_page_layout = html.Div([
+    html.H1("📈 個股深度分析", style={'text-align': 'center', 'margin-bottom': '30px'}),
     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', style={'margin-bottom': '10px'})
+            html.Label("選擇股票:", style={'font-weight': 'bold', 'margin-right': '10px'}),
+            dcc.Dropdown(id='stock-dropdown', options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()], value='2330.TW', style={'width': '70%', 'display': 'inline-block'})
         ], style={'width': '30%', 'display': 'inline-block', 'vertical-align': 'top'}),
         html.Div([
-            html.Label("時間範圍:"),
+            html.Label("時間範圍:", style={'font-weight': 'bold', 'margin-right': '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'})
+                value='6mo', style={'width': '70%', 'display': 'inline-block'})
         ], 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'})
+            html.Label("圖表類型:", style={'font-weight': 'bold', 'margin-right': '10px'}),
+            dcc.Dropdown(id='chart-type', options=[{'label': '線圖', 'value': 'line'},{'label': '蠟燭圖', 'value': 'candlestick'}], value='candlestick', style={'width': '70%', 'display': 'inline-block'})
         ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'})
-    ], style={'margin-bottom': '30px'}),
+    ], style={'margin-bottom': '30px', 'padding': '20px', 'background': '#f8f9fa', 'border-radius': '10px'}),
 
     html.Div(id='stock-info-cards', style={'margin-bottom': '30px'}),
-    html.Div([html.Div([dcc.Graph(id='price-chart')], style={'width': '100%', 'display': 'inline-block', 'vertical-align': 'top'})]),
+    html.Div([dcc.Graph(id='price-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([
         html.H3("📊 進階技術指標分析", style={'margin-bottom': '20px'}),
         html.Div([
@@ -273,11 +331,14 @@ app.layout = html.Div([
                 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)'}),
-    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'}),
+    ], style={'margin-top': '30px','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': '30px', 'padding': '20px', 'background': 'white', 'border-radius': '10px', 'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
+
+    html.Div([html.H3("📊 產業表現分析"), dcc.Graph(id='industry-analysis')], style={'margin-top': '30px', 'padding': '20px', 'background': 'white', 'border-radius': '10px', 'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
+
     html.Div([
-        html.H3("📊 分析師觀點與市場解讀", style={'color': '#2E86AB', 'margin-bottom': '20px'}),
+        html.H3("📈 分析師觀點與市場解讀", style={'color': '#2E86AB', 'margin-bottom': '20px'}),
         html.Div([
             html.Div([
                 html.H4("🔍 技術面分析", style={'color': '#A23B72', 'margin-bottom': '15px'}),
@@ -293,6 +354,7 @@ app.layout = html.Div([
             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'}),
+
     html.Div([
         html.H3("📊 多檔股票比較分析", style={'margin-bottom': '20px'}),
         html.Div([
@@ -313,6 +375,35 @@ app.layout = html.Div([
     ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
 ])
 
+# --- 主要應用程式佈局 ---
+app.layout = html.Div([
+    dcc.Location(id='url', refresh=False), # 用於追蹤 URL
+    html.H1("台股趨勢分析儀表板", style={'text-align': 'center', 'margin-bottom': '10px', 'color': '#2c3e50'}),
+    # 導航列
+    html.Div([
+        dcc.Link('市場總覽 📈', href='/', style={'margin-right': '20px', 'font-size': '18px', 'text-decoration': 'none', 'color': '#3498db'}),
+        dcc.Link('個股分析 🔍', href='/stock-analysis', style={'font-size': '18px', 'text-decoration': 'none', 'color': '#e67e22'}),
+    ], style={'text-align': 'center', 'margin-bottom': '30px', 'padding': '10px', 'background-color': '#f8f9fa', 'border-radius': '8px'}),
+    html.Hr(style={'border-top': '1px solid #e0e0e0'}),
+    html.Div(id='page-content') # 這裡將動態載入頁面內容
+])
+
+
+# --- 回調函數 (處理頁面導航) ---
+@app.callback(
+    dash.dependencies.Output('page-content', 'children'),
+    [dash.dependencies.Input('url', 'pathname')]
+)
+def display_page(pathname):
+    """根據 URL 路徑顯示對應的頁面內容"""
+    if pathname == '/stock-analysis':
+        return stock_page_layout
+    else: # 預設顯示首頁
+        return homepage_layout
+
+# --- 以下是所有回調函數 ---
+# 這些回調函數保持與您原程式碼相同的功能，但現在它們由 display_page 根據 URL 決定是否被觸發。
+
 # 台指期獨立預測回調函數
 @app.callback(
     [dash.dependencies.Output('taiex-prediction-results', 'children'),
@@ -321,31 +412,54 @@ app.layout = html.Div([
 )
 def update_taiex_prediction(predict_days):
     data = get_stock_data('^TWII', '2y')
-    if data.empty: return html.Div("無法獲取台指期資料"), {}
+    if data.empty: return html.Div("無法獲取台指期資料", style={'color': 'red'}), {}
     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"), {}
+    if final_prediction is None: return html.Div("資料不足，無法進行預測", style={'color': 'orange'}), {}
+    
     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])
+    
+    # 為了讓圖表更平滑，我們預測幾個點
+    prediction_intervals = [1, 5, 10, 20, 60] # 預測的間隔天數
     prediction_dates, prediction_prices = [last_date], [current_price]
-    for days in intervals_to_predict:
+    
+    # 根據使用者選擇的 predict_days，決定要顯示哪些預測點
+    intervals_to_show = sorted([d for d in prediction_intervals if d <= predict_days] + [predict_days])
+    
+    for days in intervals_to_show:
+        # 這裡假設 simple_lstm_predict 可以處理任意間隔
+        # 如果是真實的 LSTM 模型，可能需要更複雜的邏輯來生成多步預測
         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'])
-    color, arrow = ('red', '📈') if change_pct >= 0 else ('green', '📉')
+            
+    color, arrow = ('#4CAF50', '📈') if change_pct >= 0 else ('#F44336', '📉') # 綠色和紅色
     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.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)'})
+    
     fig = go.Figure()
-    recent_data = data.tail(30)
+    # 顯示最近 60 天的歷史數據
+    recent_data = data.tail(60)
     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)))
+    
+    # 顯示預測路徑
     fig.add_trace(go.Scatter(x=prediction_dates, y=prediction_prices, mode='lines+markers', name=f'{predict_days}日預測路徑', line=dict(color=color, width=3, dash='dash'), marker=dict(size=8)))
-    fig.update_layout(title=f'台指期 {predict_days}日預測走勢', xaxis_title='日期', yaxis_title='指數點位', height=350, plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font=dict(color='white'))
+    
+    fig.update_layout(
+        title=f'台指期 {predict_days}日預測走勢',
+        xaxis_title='日期',
+        yaxis_title='指數點位',
+        height=350,
+        plot_bgcolor='rgba(0,0,0,0)', # 透明背景
+        paper_bgcolor='rgba(0,0,0,0)', # 透明背景
+        font=dict(color='white')
+    )
     return result_card, fig
 
 # 更新股價資訊卡片
@@ -354,27 +468,35 @@ def update_taiex_prediction(predict_days):
     [dash.dependencies.Input('stock-dropdown', 'value')]
 )
 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")
+    data = get_stock_data(selected_stock, '5d') # 獲取最近5天的數據
+    if data.empty: return html.Div("無法獲取股票資料", style={'color': 'red'})
+    
     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
+    change_pct = (change / prev_price) * 100 if prev_price != 0 else 0
+    
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-    color, arrow = ('red', '▲') if change >= 0 else ('green', '▼')
+    color, arrow = ('#F44336', '▲') if change >= 0 else ('#4CAF50', '▼') # 紅色上漲, 綠色下跌
+    
+    # 確保最高、最低、成交量有值
+    today_high = data['High'].iloc[-1] if not data.empty else 0
+    today_low = data['Low'].iloc[-1] if not data.empty else 0
+    today_volume = data['Volume'].iloc[-1] if not data.empty else 0
+
     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'}),
+        ], 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', 'width': '30%'}),
         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'})
-    ])
+            html.P(f"最高: ${today_high:.2f}", style={'margin': '5px 0'}),
+            html.P(f"最低: ${today_low:.2f}", style={'margin': '5px 0'}),
+            html.P(f"成交量: {today_volume:,.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', 'width': '30%'})
+    ], style={'display': 'flex', 'justify-content': 'flex-start', 'gap': '20px'})
 
 # 更新主要圖表 (股價與成交量分佈)
 @app.callback(
@@ -388,17 +510,45 @@ def update_price_chart(selected_stock, period, chart_type):
     if data.empty: return {}
     data = calculate_technical_indicators(data)
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
+    
+    # 創建子圖
     fig = make_subplots(rows=1, cols=2, shared_yaxes=True, column_widths=[0.8, 0.2], horizontal_spacing=0.01)
+    
+    # 添加股價圖 (蠟燭圖或線圖)
     if chart_type == 'candlestick':
         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)
-    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)
+    else: # line chart
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name=stock_name, line=dict(color='#3498db')), row=1, col=1)
+    
+    # 添加移動平均線
+    if 'MA5' in data.columns:
+        fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], mode='lines', name='MA5', line=dict(color='orange', width=1)), row=1, col=1)
+    if 'MA20' in data.columns:
+        fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], mode='lines', name='MA20', line=dict(color='blue', width=1)), row=1, col=1)
+
+    # 添加成交量分佈圖 (Volume Profile)
     bin_edges, volume_per_bin, price_centers = calculate_volume_profile(data, num_bins=50)
-    if volume_per_bin is not None:
-        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)
+    if volume_per_bin is not None and price_centers is not None and len(price_centers) == len(volume_per_bin):
+        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)
+    else:
+        # 如果沒有成交量資料，則不顯示 Volume Profile
+        print("無成交量資料，跳過 Volume Profile 顯示")
+        fig.update_layout(column_widths=[1.0]) # 調整為單欄佈局
+
+    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)'),
+        # x-axis for volume profile, y-axis for volume profile is shared with price chart
+        xaxis2=dict(title='成交量', showticklabels=True), 
+        yaxis2=dict(showticklabels=False), # 隱藏 Y 軸標籤，因為它與左邊共享
+        bargap=0.05, # 調整柱狀圖間隔
+        margin=dict(l=50, r=20, t=50, b=50)
+    )
     return fig
 
 # 更新進階技術指標圖表
@@ -413,52 +563,59 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
     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
+    
+    fig = go.Figure() # 預設圖形
+    
     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_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.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 = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('收盤價', '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.add_trace(go.Scatter(x=data.index, y=data['MACD'], mode='lines', name='MACD', 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', 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_trace(go.Bar(x=data.index, y=data['MACD_Histogram'], name='Histogram', 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_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 = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('收盤價', '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=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.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 = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('收盤價', '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=-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.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'))
-        data_filtered = data.iloc[14:]
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('收盤價', 'DMI 指標'))
+        # DMI 通常需要前 14 天的數據，所以從第 14 天開始繪製
+        data_filtered = data.iloc[14:] if len(data) > 14 else data
         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
@@ -471,53 +628,95 @@ def update_volume_chart(selected_stock, period):
     data = get_stock_data(selected_stock, period)
     if data.empty: return {}
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-    colors = ['red' if data['Close'].iloc[i] > data['Open'].iloc[i] else 'green' for i in range(len(data))]
+    
+    # 根據開盤價與收盤價決定柱狀圖顏色
+    colors = ['#F44336' if data['Close'].iloc[i] >= data['Open'].iloc[i] else '#4CAF50' for i in range(len(data))]
+    
     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
+# 更新產業分析圖表 (僅顯示前10檔股票的月報酬率比較)
 @app.callback(
     dash.dependencies.Output('industry-analysis', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]
+    [dash.dependencies.Input('stock-dropdown', 'value')] # 觸發條件，確保圖表會更新
 )
 def update_industry_analysis(selected_stock):
     industry_data = []
-    for symbol in list(TAIWAN_STOCKS.values())[:10]:
-        data = get_stock_data(symbol, '1mo')
+    # 僅取列表中的前10支股票進行比較，避免圖表過於擁擠
+    stocks_to_analyze = list(TAIWAN_STOCKS.items())[:10] 
+    
+    for name, symbol in stocks_to_analyze:
+        data = get_stock_data(symbol, '1mo') # 獲取一個月的數據
         if not data.empty:
-            stock_name = [name for name, symbol_code in TAIWAN_STOCKS.items() if symbol_code == symbol][0]
-            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 {}
+            try:
+                return_pct = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
+                industry = INDUSTRY_MAPPING.get(symbol, '其他')
+                industry_data.append({'股票': name, '代碼': symbol, '月報酬率(%)': return_pct, '產業': industry})
+            except ZeroDivisionError:
+                print(f"注意：{name} ({symbol}) 的起始股價為 0，無法計算報酬率。")
+                industry_data.append({'股票': name, '代碼': symbol, '月報酬率(%)': 0, '產業': industry})
+
+    if not industry_data: 
+        fig = go.Figure().add_annotation(text="無股票資料可供分析", showarrow=False)
+        fig.update_layout(title="產業表現分析 (月報酬率)", height=400)
+        return fig
+
     df_industry = pd.DataFrame(industry_data)
-    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)
+    
+    # 創建圓餅圖
+    fig = px.pie(df_industry, values='月報酬率(%)', names='股票', title='前10檔股票月報酬率比較', 
+                 color_discrete_sequence=px.colors.qualitative.Pastel) # 使用 Pastel 調色盤
+    
+    fig.update_layout(height=400, margin=dict(t=50, b=0, l=0, r=0))
     return fig
 
 # 更新景氣燈號圖表
 @app.callback(
     dash.dependencies.Output('business-climate-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]
+    [dash.dependencies.Input('url', 'pathname')] # 觸發條件：當使用者進入首頁時更新
 )
-def update_business_climate_chart(selected_stock):
+def update_business_climate_chart(pathname):
+    if pathname != '/': return {} # 確保只在首頁觸發
     df = get_business_climate_data()
     if df.empty:
         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'
-        elif score >= 24: return 'orange'
-        elif score >= 17: return 'yellow'
-        elif score >= 10: return 'lightgreen'
-        else: return 'blue'
+        if score >= 32: return 'red' # 紅燈
+        elif score >= 24: return 'orange' # 黃紅燈
+        elif score >= 17: return 'yellow' # 黃藍燈
+        elif score >= 10: return 'lightgreen' # 綠燈
+        else: return 'blue' # 藍燈
+
     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'))))
-    fig.add_hline(y=32, line_dash="dash", line_color="red", annotation_text="紅燈(32)")
-    fig.add_hline(y=17, line_dash="dash", line_color="yellow", annotation_text="黃燈(17)")
-    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.add_trace(go.Scatter(
+        x=df['Date'], 
+        y=df['Index'], 
+        mode='lines+markers', 
+        name='景氣燈號分數', 
+        line=dict(color='#2E86C1', width=2), # 深藍色線
+        marker=dict(size=8, color=colors, line=dict(width=2, color='#2E86C1')) # 標記點顏色隨燈號變化
+    ))
+    
+    # 添加參考線
+    fig.add_hline(y=32, line_dash="dash", line_color="red", annotation_text="紅燈 (32)", annotation_position="bottom right")
+    fig.add_hline(y=17, line_dash="dash", line_color="yellow", annotation_text="黃藍燈 (17)", annotation_position="bottom right")
+    fig.add_hline(y=10, line_dash="dash", line_color="lightgreen", annotation_text="綠燈 (10)", annotation_position="bottom right")
+    
+    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]), # 調整 Y 軸範圍
+        margin=dict(l=50, r=20, t=50, b=50)
+    )
     return fig
 
 # 更新分析師觀點
@@ -531,46 +730,99 @@ def update_business_climate_chart(selected_stock):
 def update_analysis_text(selected_stock, period):
     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", "無法獲取資料", "無法獲取資料"
+    
+    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)
+    
+    # 確保有足夠數據計算
+    if len(data) < 2: return "數據不足", "數據不足", "數據不足"
+
     current_price = data['Close'].iloc[-1]
-    price_change = ((current_price - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
+    first_price = data['Close'].iloc[0]
+    price_change = ((current_price - first_price) / first_price) * 100 if first_price != 0 else 0
+    
     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
+
+    # --- 技術面分析 ---
+    trend_desc = "上漲" if price_change > 5 else "下跌" if price_change < -5 else "盤整"
+    trend_color = '#F44336' if price_change > 5 else '#4CAF50' if price_change < -5 else '#FF9800'
+    rsi_desc = "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"
+    rsi_color = '#F44336' if rsi_current > 70 else '#4CAF50' if rsi_current < 30 else '#2196F3'
+    macd_desc = "多頭" if macd_current > macd_signal_current else "空頭"
+    macd_color = '#F44336' if macd_current > macd_signal_current else '#4CAF50'
+
     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("價格趨勢："), f"近期 {period} 期間內，{stock_name} 呈現", 
+                html.Span(f"{trend_desc}", style={'color': trend_color, '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(f"{rsi_desc}", style={'color': rsi_color, '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': macd_color, 'font-weight': 'bold'}), 
+                f"信號線({macd_signal_current:.3f})，顯示", html.Span(f"{macd_desc}", style={'color': macd_color, 'font-weight': 'bold'}), "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"建議持續關注季報表現及未來指引。"]),
+        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"建議持續關注公司最新財報、新聞動態及產業趨勢，以掌握其長期發展潛力。"]),
+        html.P([html.Strong("風險提示："), f"基本面分析僅供參考，實際投資決策請獨立判斷。"])
     ])
+
+    # --- 市場展望與投資建議 ---
     outlook_tone = "謹慎樂觀" if price_change > 10 else "保守觀望" if price_change < -10 else "中性持平"
+    outlook_color = '#4CAF50' if price_change > 10 else '#FF9800' if price_change < -10 else '#757575'
+    
     market_outlook = html.Div([
-        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。"]),
+        html.P([html.Strong("整體評估："), f"基於技術面與基本面綜合考量，對 {stock_name} 目前採取", 
+                html.Span(f"{outlook_tone}", style={'color': outlook_color, 'font-weight': 'bold'}), "態度。"]),
+        html.P([html.Strong("投資建議："), "短期交易者可關注技術指標訊號，長期投資者應深入研究其基本面與產業前景。請注意，市場波動風險始終存在，務必做好風險控管。"]),
     ])
+    
     return technical_text, fundamental_text, market_outlook
 
 # 更新PMI圖表
 @app.callback(
     dash.dependencies.Output('pmi-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]
+    [dash.dependencies.Input('url', 'pathname')] # 觸發條件：當使用者進入首頁時更新
 )
-def update_pmi_chart(selected_stock):
+def update_pmi_chart(pathname):
+    if pathname != '/': return {} # 確保只在首頁觸發
     df = get_pmi_data()
     if df.empty:
-        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)
+        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
-    colors = ['red' if value >= 50 else 'green' for value in df['Index']]
+        
+    # 根據 PMI 值決定柱狀圖顏色
+    colors = ['#F44336' if value >= 50 else '#4CAF50' 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'))))
-    fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線(50)")
-    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.add_trace(go.Scatter(
+        x=df['Date'], 
+        y=df['Index'], 
+        mode='lines+markers', 
+        name='PMI 指數', 
+        line=dict(color='#34495E', width=2), # 深灰色線
+        marker=dict(size=8, color=colors, line=dict(width=2, color='#34495E'))
+    ))
+    
+    fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線 (50)", annotation_position="bottom right")
+    
+    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]), # 調整 Y 軸範圍
+        margin=dict(l=50, r=20, t=50, b=50)
+    )
     return fig
 
 # 更新多檔股票比較
@@ -581,30 +833,73 @@ def update_pmi_chart(selected_stock):
      dash.dependencies.Input('comparison-period', 'value')]
 )
 def update_comparison_analysis(selected_stocks, period):
-    fixed_stock = '0050.TW'
-    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]
+    fixed_stock = '0050.TW' # 固定比較基準
+    
+    # 處理使用者選擇的股票
+    if not selected_stocks: 
+        display_stocks = [fixed_stock] # 如果沒選，預設顯示 0050
+    elif fixed_stock not in selected_stocks:
+        display_stocks = [fixed_stock] + selected_stocks # 如果沒選 0050，則加入
+    else:
+        display_stocks = selected_stocks
+    
+    display_stocks = list(set(display_stocks))[:5] # 去重並限制最多 5 檔
+    
     fig = go.Figure()
     comparison_data = []
-    for stock in selected_stocks:
+    
+    for stock in display_stocks:
         data = get_stock_data(stock, period)
         if not data.empty:
             stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == stock), stock)
-            normalized_prices = (data['Close'] / data['Close'].iloc[0]) * 100
+            
+            # 計算相對績效 (以第一天為基準 100)
+            normalized_prices = (data['Close'] / data['Close'].iloc[0]) * 100 if data['Close'].iloc[0] != 0 else data['Close'] * 0
             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
-            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')
+            
+            # 計算總報酬率和波動率
+            total_return = ((data['Close'].iloc[-1] / data['Close'].iloc[0]) - 1) * 100 if data['Close'].iloc[0] != 0 else 0
+            # 波動率 (年化標準差)
+            pct_change = data['Close'].pct_change().dropna()
+            volatility = pct_change.std() * np.sqrt(252) * 100 if not pct_change.empty else 0 # 假設一年有 252 個交易日
+            
+            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', # 滑鼠懸停時顯示所有線的資訊
+        margin=dict(l=50, r=20, t=50, b=50)
+    )
+    
+    # 創建比較結果表格
+    table_rows = []
     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"${item['current_price']:.2f}")]))
-        table = html.Table([html.Thead(html.Tr([html.Th("股票"), html.Th("報酬率"), html.Th("波動率"), html.Th("現價")])), html.Tbody(table_rows)], style={'width': '100%'})
+        # 按報酬率排序 (由高到低)
+        sorted_data = sorted(comparison_data, key=lambda x: x['return'], reverse=True)
+        for item in sorted_data:
+            return_color = '#F44336' if item['return'] > 0 else '#4CAF50' # 紅色代表上漲，綠色代表下跌
+            table_rows.append(html.Tr([
+                html.Td(item['name'], style={'font-weight': 'bold', 'padding': '8px'}), 
+                html.Td(f"{item['return']:+.1f}%", style={'color': return_color, 'font-weight': 'bold', 'padding': '8px'}), 
+                html.Td(f"{item['volatility']:.1f}%", style={'padding': '8px'}), 
+                html.Td(f"${item['current_price']:.2f}", style={'padding': '8px'})
+            ]))
+            
+        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%', 'border-collapse': 'collapse', 'margin-top': '15px'})
         return fig, table
-    return fig, html.Div("無可比較資料")
+    else:
+        return fig, html.Div("無可比較資料", style={'margin-top': '15px'})
 
 
 # ==============================================================================
@@ -613,26 +908,26 @@ def update_comparison_analysis(selected_stocks, period):
 @app.callback(
     [dash.dependencies.Output('sentiment-gauge', 'children'),
      dash.dependencies.Output('news-summary', 'children')],
-    [dash.dependencies.Input('stock-dropdown', 'value')] # 觸發條件不變
+    [dash.dependencies.Input('url', 'pathname')] # 觸發條件：當使用者進入首頁時更新
 )
-def update_sentiment_analysis(selected_stock):
-    # 檢查 predictor 是否成功初始化
+def update_sentiment_analysis(pathname):
+    if pathname != '/': return {}, {} # 確保只在首頁觸發
+
+    # 檢查 predictor 是否成功初始化 (在程式碼開頭已處理)
     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。")
+        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。", style={'color': 'red'})
 
     # --- 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 ---
+    gauge_content = html.Div() # 預設值
     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)
+        # **重要假設**：假設您模型的輸出範圍在 [-1, 1] 之間
         # 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))
+        sentiment_score_normalized = max(0, min(100, (sentiment_score_raw + 1) * 50)) # 正規化並確保在0-100之間
 
         # 根據分數決定顏色和標籤
         if sentiment_score_normalized >= 65:
@@ -646,27 +941,31 @@ def update_sentiment_analysis(selected_stock):
             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}},
+            title = {'text': f"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)"}
+                    {'range': [0, 35], 'color': "rgba(217, 83, 79, 0.2)"}, # 悲觀區間背景
+                    {'range': [35, 65], 'color': "rgba(240, 173, 78, 0.2)"}, # 中性區間背景
+                    {'range': [65, 100], 'color': "rgba(92, 184, 92, 0.2)"}  # 樂觀區間背景
                 ],
+                'threshold' : { # 設定觸發線
+                    'line': {'color': "red", 'width': 4},
+                    'thickness': 0.75,
+                    'value': sentiment_score_normalized # 這裡設為當前值，也可以設為固定值
+                }
             }
         ))
-        gauge_fig.update_layout(height=200, margin=dict(l=30, r=30, t=50, b=20))
+        gauge_fig.update_layout(height=220, 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)
+        # 處理無法計算分數的情況
         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 ---
+    # --- 3. 從 predictor 獲取新聞摘要 ---
     top_news_list = predictor.get_news()
 
     # --- 4. 建立新聞摘要元件 ---
@@ -676,13 +975,18 @@ def update_sentiment_analysis(selected_stock):
                 'margin': '8px 0',
                 'padding-left': '5px',
                 'font-size': '14px',
-                'line-height': '1.5'
+                'line-height': '1.5',
+                'border-left': '3px solid #3498db', # 添加左側邊框
+                'background-color': '#ecf0f1', # 淺灰色背景
+                'border-radius': '5px',
+                'padding-top': '5px',
+                'padding-bottom': '5px'
             }) 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'})
+    elif top_news_list == []: # 如果是空列表 (無新聞)
+         news_content = html.P("昨日無重大相關新聞。", style={'text-align': 'center', 'padding-top': '50px', 'color': '#7f8c8d'})
+    else: # 如果是 None (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', 'color': 'red'})
 
     return gauge_content, news_content