Update app.py

app.py

@@ -1,4 +1,5 @@
-# HUGING_FACE_V3.1.1.py (多頁面版本)
+# HUGING_FACE_V2.1.3.py (整合 Bert_predict 版本)
+
 # 系統套件
 import os
 from datetime import datetime, timedelta
@@ -13,23 +14,11 @@ from plotly.subplots import make_subplots
 import re
 from bs4 import BeautifulSoup
 import requests
-import warnings
-from sklearn.preprocessing import MinMaxScaler
-import joblib
-from tensorflow.keras.models import load_model
 
 # 引用您組員的預測器程式
-try:
-    from Bert_predict import BertPredictor
-except ImportError:
-    print("找不到 'Bert_predict.py' 模組，新聞情緒分析功能將無法使用。")
-    BertPredictor = None
-
-# 忽略所有 UserWarning
-warnings.filterwarnings("ignore", category=UserWarning)
+from Bert_predict import BertPredictor
 
-# --- 資料準備與輔助函式 ---
-# 台股代號對應表
+# 台股代號對應表 (移除台指期，因為它現在是獨立區塊)
 TAIWAN_STOCKS = {
     '元大台灣50': '0050.TW', # 新增
     '台積電': '2330.TW',
@@ -46,200 +35,175 @@ TAIWAN_STOCKS = {
     '慧洋-KY': '2637.TW',
     '上銀': '2049.TW',
     '台泥': '1101.TW',
-    '中信金': '2891.TW',
-    '中鋼': '2002.TW',
-    '聯電': '2303.TW',
-    '國泰金': '2882.TW',
-    '華碩': '2357.TW',
-    '友達': '2409.TW',
-    '緯創': '3231.TW',
-    '廣達': '2382.TW',
-    '技嘉': '2376.TW',
-    '英業達': '2356.TW',
-    '光寶科': '2301.TW',
+    '南亞科': '2408.TW',
+    '旺宏': '2337.TW',
+    '譜瑞-KY': '4966.TWO',
+    '貿聯-KY': '3665.TW',
+    '騰雲': '6870.TWO',
+    '穩懋': '3105.TWO'
 }
 
-# 產業分類 (簡化範例)
+# 產業分類
 INDUSTRY_MAPPING = {
-    '電子': ['2330.TW', '2454.TW', '2317.TW', '2308.TW', '3711.TW', '2357.TW', '2409.TW', '3231.TW', '2382.TW', '2376.TW', '2356.TW', '2301.TW'],
-    '金融': ['2881.TW', '2882.TW', '2891.TW'],
-    '塑化': ['1301.TW'],
-    '水泥': ['1101.TW'],
-    '傳產': ['2002.TW', '1216.TW', '2603.TW', '2637.TW', '2049.TW'],
-    '通訊': ['2412.TW'],
-    'ETF': ['0050.TW']
+    '0050.TW': 'ETF', # 新增
+    '2330.TW': '半導體',
+    '2454.TW': '半導體',
+    '2317.TW': '電子組件',
+    '1301.TW': '塑膠',
+    '2412.TW': '電信',
+    '2881.TW': '金融',
+    '2882.TW': '金融',
+    '2308.TW': '電子',
+    '1216.TW': '食品',
+    '3711.TW': '半導體',
+    '2603.TW': '航運',
+    '2637.TW': '散裝航運',
+    '2049.TW': '工具機',
+    '1101.TW': '營建',
+    '2408.TW': 'DRAM',
+    '2337.TW': 'NFLSH',
+    '1101.TW': '營建',
+    '4966.TWO': '高速傳輸',
+    '3665.TW': '連接器',
+    '6870.TWO': '軟體整合',
+    '3105.TWO': 'PA功率'
 }
 
-def get_stock_data(ticker, period='1y'):
-    """從 yfinance 獲取股票資料"""
+def get_stock_data(symbol, period='1y'):
+    """獲取股票資料"""
     try:
-        stock = yf.Ticker(ticker)
+        stock = yf.Ticker(symbol)
         data = stock.history(period=period)
+        if data.empty and symbol == 'TXF=F':
+            stock = yf.Ticker('0050.TW')
+            data = stock.history(period=period)
+            if data.empty:
+                stock = yf.Ticker('^TWII')
+                data = stock.history(period=period)
         return data
-    except Exception as e:
-        print(f"無法獲取 {ticker} 的資料: {e}")
+    except:
         return pd.DataFrame()
 
-def get_economic_data(ticker, period='2y'):
-    """獲取總經指標資料，例如PMI"""
-    data = yf.download(ticker, period=period)
-    return data['Close']
-
-def get_business_climate_data():
-    """模擬獲取台灣景氣對策信號分數"""
-    df = pd.DataFrame({
-        'Date': pd.to_datetime(['2024-01', '2024-02', '2024-03', '2024-04', '2024-05', '2024-06', '2024-07']),
-        'Score': [22, 24, 25, 27, 29, 30, 31],
-        'Signal': ['黃藍', '黃藍', '綠', '綠', '綠', '綠', '綠']
-    })
-    return df
+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
+    prices = data['Close'].values
+    ma_short = np.mean(prices[-5:])
+    ma_medium = np.mean(prices[-20:])
+    ma_long = np.mean(prices[-60:])
+    recent_trend = np.polyfit(range(20), prices[-20:], 1)[0]
+    volatility = np.std(prices[-20:]) / np.mean(prices[-20:])
+    base_change = recent_trend * predict_days
+    trend_factor = 1.0
+    if ma_short > ma_medium > ma_long:
+        trend_factor = 1.02
+    elif ma_short < ma_medium < ma_long:
+        trend_factor = 0.98
+    else:
+        trend_factor = 1.0
+    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)
+    }
 
-def add_technical_indicators(df):
-    """計算並新增技術指標"""
-    # RSI
+def calculate_technical_indicators(df):
+    """計���技術指標"""
+    if df.empty: return df
+    df['MA5'] = df['Close'].rolling(window=5).mean()
+    df['MA20'] = df['Close'].rolling(window=20).mean()
     delta = df['Close'].diff()
-    gain = delta.where(delta > 0, 0)
-    loss = -delta.where(delta < 0, 0)
-    avg_gain = gain.ewm(com=13, min_periods=14).mean()
-    avg_loss = loss.ewm(com=13, min_periods=14).mean()
-    rs = avg_gain / avg_loss
+    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
-    exp1 = df['Close'].ewm(span=12, adjust=False).mean()
-    exp2 = df['Close'].ewm(span=26, adjust=False).mean()
+    exp1 = df['Close'].ewm(span=12).mean()
+    exp2 = df['Close'].ewm(span=26).mean()
     df['MACD'] = exp1 - exp2
-    df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()
-    df['MACD_Hist'] = df['MACD'] - df['Signal_Line']
-
-    # Bollinger Bands
-    df['MA20'] = df['Close'].rolling(window=20).mean()
-    df['StdDev'] = df['Close'].rolling(window=20).std()
-    df['Upper_BB'] = df['MA20'] + (df['StdDev'] * 2)
-    df['Lower_BB'] = df['MA20'] - (df['StdDev'] * 2)
-
-    # KD (Stochastic Oscillator)
-    low_14 = df['Low'].rolling(window=14).min()
-    high_14 = df['High'].rolling(window=14).max()
-    df['%K'] = 100 * ((df['Close'] - low_14) / (high_14 - low_14))
-    df['%D'] = df['%K'].rolling(window=3).mean()
-
-    # %R (Williams %R)
-    df['%R'] = -100 * (high_14 - df['Close']) / (high_14 - low_14)
-
-    # DMI
-    df['DMplus'] = df['High'].diff().clip(lower=0)
-    df['DMminus'] = (-df['Low'].diff()).clip(lower=0)
-    df['TR'] = df[['High', 'Low', 'Close']].apply(lambda x: max(x['High'] - x['Low'], abs(x['High'] - x['Close']), abs(x['Low'] - x['Close'])), axis=1)
-    df['ADX'] = df['TR'].ewm(alpha=1/14, adjust=False).mean()
-    df['DIplus'] = df['DMplus'].ewm(alpha=1/14, adjust=False).mean() / df['ADX']
-    df['DIminus'] = df['DMminus'].ewm(alpha=1/14, adjust=False).mean() / df['ADX']
-    df['ADX'] = abs(df['DIplus'] - df['DIminus']) / (df['DIplus'] + df['DIminus']) * 100
-
+    df['MACD_Signal'] = df['MACD'].ewm(span=9).mean()
+    df['MACD_Histogram'] = df['MACD'] - df['MACD_Signal']
+    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)
+    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()
+    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)
+    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
+    df['ADX'] = df['DX'].ewm(com=13, adjust=False).mean()
     return df
 
-def generate_analysis_text(df):
-    """生成股票分析文字"""
-    if df.empty:
-        return {
-            'technical': "找不到技術資料。",
-            'fundamental': "找不到基本面資料。",
-            'outlook': "無法提供市場展望。"
-        }
-    
-    latest = df.iloc[-1]
-    
-    # 技術面分析
-    tech_text = "找不到技術分析資料。"
-    if 'RSI' in df.columns:
-        rsi = latest['RSI']
-        rsi_signal = "超買" if rsi > 70 else "超賣" if rsi < 30 else "中性"
-        tech_text = f"目前RSI為 {rsi:.2f}，顯示市場處於**{rsi_signal}**。近期走勢強勁，但需留意過熱風險。"
-    
-    # 基本面分析（簡化）
-    fundamental_text = "找不到基本面分析資料。"
-    fundamental_text = f"基本面表現穩健，產業前景看好。公司財務狀況良好，建議持續關注。"
-
-    # 市場展望
-    outlook_text = "市場展望樂觀，但仍需留意全球經濟不確定性。建議投資人審慎評估，並隨時關注最新市場動態。"
-    
-    return {
-        'technical': tech_text,
-        'fundamental': fundamental_text,
-        'outlook': outlook_text
-    }
-
-# --- LSTM 預測模型 ---
-def simple_lstm_predict(ticker, n_days=5):
-    """使用簡單 LSTM 預測未來 n 天的收盤價"""
-    model_path = 'lstm_model_taiex.h5'
-    scaler_path = 'scaler_taiex.pkl'
-    
-    # 檢查模型和 scaler 是否存在
-    if not os.path.exists(model_path) or not os.path.exists(scaler_path):
-        return None, "模型或縮放器檔案不存在，無法進行預測。"
+def calculate_volume_profile(df, num_bins=50):
+    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, 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
+    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():
     try:
-        # 載入模型和 scaler
-        model = load_model(model_path)
-        scaler = joblib.load(scaler_path)
-
-        # 獲取歷史資料
-        data = yf.download(ticker, period='60d', interval='1d')
-        if data.empty:
-            return None, "無法獲取歷史數據。"
-
-        # 使用最新的 60 天收盤價作為輸入
-        last_60_days = data['Close'].values[-60:].reshape(-1, 1)
-        last_60_days_scaled = scaler.transform(last_60_days)
-        X_test = last_60_days_scaled.reshape(1, 60, 1)
-
-        # 進行預測
-        future_predictions = []
-        current_input = X_test
-        for _ in range(n_days):
-            predicted_scaled_price = model.predict(current_input, verbose=0)
-            future_predictions.append(predicted_scaled_price[0, 0])
-            current_input = np.append(current_input[:, 1:, :], predicted_scaled_price.reshape(1, 1, 1), axis=1)
-
-        # 反向轉換回原始價格
-        predicted_prices = scaler.inverse_transform(np.array(future_predictions).reshape(-1, 1)).flatten()
-
-        # 建立預測結果 DataFrame
-        last_date = data.index[-1]
-        future_dates = [last_date + timedelta(days=i) for i in range(1, n_days + 1)]
-        pred_df = pd.DataFrame({'Date': future_dates, 'Predicted_Price': predicted_prices})
-        
-        # 建立歷史價格 DataFrame
-        history_df = data.reset_index()
-        history_df = history_df[['Date', 'Close']]
-        history_df.rename(columns={'Close': 'Price'}, inplace=True)
-        
-        return history_df, pred_df
+        if not os.path.exists('business_climate.csv'): 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
+        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'])
+        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():
+    try:
+        if not os.path.exists('taiwan_pmi.csv'): 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'])
+        return df
     except Exception as e:
-        print(f"預測過程中發生錯誤: {e}")
-        return None, f"預測過程中發生錯誤: {e}"
+        print(f"無法獲取 PMI 資料: {str(e)}")
+        return pd.DataFrame()
 
-# --- 主要應用程式 ---
 # 建立 Dash 應用程式
 app = dash.Dash(__name__, suppress_callback_exceptions=True)
 
-# 註解掉新聞預測器初始化
-# predictor = None
-# try:
-#     if BertPredictor:
-#         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
-
-
-# --- 頁面內容定義 ---
-# 首頁：預測與總經
-homepage_layout = html.Div([
+# --- 【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'}),
     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.Div([
@@ -257,20 +221,20 @@ homepage_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'}),
     
-    # 註解掉情緒分析區塊
-    # html.Div([
-    #     html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
-    #     html.Div([
-    #         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'})
-    #         ], 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)'}),
+    # 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([
+            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'})
+            ], 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)'}),
 
     html.Div([
         html.H3("景氣燈號與 PMI 分析"),
@@ -279,11 +243,7 @@ homepage_layout = html.Div([
             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.Div([
         html.Div([
             html.Label("選擇股票:"),
@@ -300,6 +260,7 @@ stock_page_layout = html.Div([
             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'}),
+
     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([
@@ -352,386 +313,379 @@ stock_page_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),
-    html.H1("台股分析儀表板", style={'text-align': 'center', 'margin-bottom': '10px'}),
-    html.Div([
-        dcc.Link('市場總覽', href='/', style={'margin-right': '20px', 'font-size': '18px'}),
-        dcc.Link('個股分析', href='/stock-analysis', style={'font-size': '18px'}),
-    ], style={'text-align': 'center', 'margin-bottom': '30px'}),
-    html.Hr(),
-    html.Div(id='page-content')
-])
-
-
-# --- 回調函數 (處理頁面導航) ---
+# 台指期獨立預測回調函數
 @app.callback(
-    dash.dependencies.Output('page-content', 'children'),
-    [dash.dependencies.Input('url', 'pathname')]
+    [dash.dependencies.Output('taiex-prediction-results', 'children'),
+     dash.dependencies.Output('taiex-prediction-chart', 'figure')],
+    [dash.dependencies.Input('taiex-prediction-period', 'value')]
 )
-def display_page(pathname):
-    if pathname == '/stock-analysis':
-        return stock_page_layout
-    else:
-        return homepage_layout
-
-# --- 回調函數 (所有原始的 callback 都放在這裡) ---
+def update_taiex_prediction(predict_days):
+    data = get_stock_data('^TWII', '2y')
+    if data.empty: return html.Div("無法獲取台指期資料"), {}
+    final_prediction = simple_lstm_predict(data, predict_days)
+    if final_prediction is None: return html.Div("資料不足，無法進行預測"), {}
+    current_price, last_date = data['Close'].iloc[-1], data.index[-1]
+    predicted_price, change_pct, confidence = final_prediction['predicted_price'], final_prediction['change_pct'], final_prediction['confidence']
+    prediction_paths = {1: [1], 5: [1, 5], 10: [1, 5, 10], 20: [1, 10, 20], 60: [1, 10, 20, 60]}
+    intervals_to_predict = prediction_paths.get(predict_days, [predict_days])
+    prediction_dates, prediction_prices = [last_date], [current_price]
+    for days in intervals_to_predict:
+        interim_prediction = simple_lstm_predict(data, days)
+        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', '📉')
+    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"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)
+    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
 @app.callback(
-    dash.dependencies.Output('taiex-prediction-results', 'children'),
-    dash.dependencies.Output('taiex-prediction-chart', 'figure'),
-    [dash.dependencies.Input('taiex-prediction-period', 'value')]
+    dash.dependencies.Output('stock-info-cards', 'children'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
 )
-def update_taiex_prediction(n_days):
-    """更新台指期預測結果"""
-    history_df, pred_df = simple_lstm_predict('^TWII', n_days)
-    
-    if history_df is None:
-        return html.P(pred_df, style={'color': 'red'}), go.Figure()
-
-    current_price = history_df.iloc[-1]['Price']
-    predicted_price = pred_df.iloc[-1]['Predicted_Price']
-    change = predicted_price - current_price
-    change_percent = (change / current_price) * 100
-    
-    direction = "📈 上漲" if change > 0 else "📉 下跌" if change < 0 else "↔ 持平"
-    color = "green" if change > 0 else "red" if change < 0 else "orange"
-    
-    result_text = html.Div([
-        html.P(f"當前價格: {current_price:.2f}", style={'font-size': '1.2em', 'margin': '5px 0'}),
-        html.P(f"預測 {n_days} 天後價格: {predicted_price:.2f}", style={'font-size': '1.2em', 'margin': '5px 0'}),
-        html.P(f"預測變動: {change:.2f} ({change_percent:.2f}%) {direction}", style={'font-size': '1.5em', 'font-weight': 'bold', 'color': color, 'margin': '10px 0'})
+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")
+    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
+    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
+    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.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'})
     ])
-    
-    # 繪製圖表
-    fig = go.Figure()
-    # 歷史價格
-    fig.add_trace(go.Scatter(x=history_df['Date'], y=history_df['Price'], mode='lines', name='歷史價格', line=dict(color='#8E44AD')))
-    # 預測價格
-    fig.add_trace(go.Scatter(x=pred_df['Date'], y=pred_df['Predicted_Price'], mode='lines', name='預測價格', line=dict(color='#E74C3C', dash='dash')))
-    
-    fig.update_layout(
-        title=f'台指期指數歷史與預測 ({n_days}天)',
-        xaxis_title='日期',
-        yaxis_title='價格',
-        legend_title='圖例',
-        template='plotly_white'
-    )
-    
-    return result_text, fig
-
-# 註解掉情緒分析回調函數
-# @app.callback(
-#     dash.dependencies.Output('sentiment-gauge', 'children'),
-#     dash.dependencies.Output('news-summary', 'children')
-# )
-# def update_sentiment_analysis():
-#     """更新新聞情緒分析"""
-#     if not predictor:
-#         return html.Div("新聞情緒分析模型未初始化。"), html.Div("請檢查 'Bert_predict.py' 檔案是否存在。")
-
-#     try:
-#         sentiment_score, news_list = predictor.get_sentiment_score()
-#     except Exception as e:
-#         sentiment_score = None
-#         news_list = []
-#         print(f"情緒分析獲取失敗: {e}")
-    
-#     # 1. 建立儀表板 (Gauge)
-#     if sentiment_score is not None:
-#         gauge_fig = go.Figure(go.Indicator(
-#             mode="gauge+number",
-#             value=sentiment_score,
-#             title={'text': "市場情緒分數 (0-100)"},
-#             domain={'x': [0, 1], 'y': [0, 1]},
-#             gauge={
-#                 'axis': {'range': [0, 100]},
-#                 'bar': {'color': "#667eea"},
-#                 '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
-#         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)
-
-
-#     # 2. 從 predictor 獲取分數最高的3則新聞
-#     top_news_list = predictor.get_news()
-
-#     # 3. 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',
-#                 'border-left': '3px solid #E74C3C'
-#             }) for news in top_news_list
-#         ])
-#     else:
-#         news_content = html.Div("今日尚無重大新聞摘要。")
-    
-#     return gauge_content, news_content
 
+# 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'),
+     dash.dependencies.Input('period-dropdown', 'value'),
+     dash.dependencies.Input('chart-type', 'value')]
+)
+def update_price_chart(selected_stock, period, chart_type):
+    data = get_stock_data(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 = make_subplots(rows=1, cols=2, shared_yaxes=True, column_widths=[0.8, 0.2], horizontal_spacing=0.01)
+    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)
+    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='��格 (TWD)'), xaxis2=dict(title='成交量', showticklabels=True), yaxis2=dict(showticklabels=False), bargap=0.05)
+    return fig
 
+# 更新進階技術指標圖表
 @app.callback(
-    dash.dependencies.Output('business-climate-chart', 'figure')
+    dash.dependencies.Output('advanced-technical-chart', 'figure'),
+    [dash.dependencies.Input('technical-indicator-selector', 'value'),
+     dash.dependencies.Input('stock-dropdown', 'value'),
+     dash.dependencies.Input('period-dropdown', 'value')]
 )
-def update_business_climate_chart():
-    """更新景氣燈號圖表"""
-    df = get_business_climate_data()
-    
-    color_map = {'藍': '#3498DB', '黃藍': '#F39C12', '綠': '#27AE60', '黃紅': '#E67E22', '紅': '#E74C3C'}
-    df['Color'] = df['Signal'].map(color_map)
-    
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=df['Date'], y=df['Score'], mode='lines+markers', marker=dict(color=df['Color'], size=10), line=dict(color='gray')))
-    
-    fig.update_layout(
-        title='台灣景氣對策信號',
-        xaxis_title='日期',
-        yaxis_title='分數',
-        yaxis=dict(range=[9, 45]),
-        template='plotly_white'
-    )
+def update_advanced_technical_chart(indicator, selected_stock, period):
+    data = get_stock_data(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
+    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.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)
+        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.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.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.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.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.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.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
 @app.callback(
-    dash.dependencies.Output('pmi-chart', 'figure')
+    dash.dependencies.Output('volume-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value'),
+     dash.dependencies.Input('period-dropdown', 'value')]
 )
-def update_pmi_chart():
-    """更新PMI圖表"""
-    # 模擬獲取PMI資料，可替換為真實API
-    pmi = get_economic_data('ISM-MAN_PMI') 
-    
-    fig = px.line(x=pmi.index, y=pmi, title='美國ISM製造業PMI', labels={'x':'日期', 'y':'PMI'})
-    fig.add_hline(y=50, line_dash="dash", line_color="red", annotation_text="50 榮枯線")
-    
-    fig.update_layout(template='plotly_white')
+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))]
+    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
+@app.callback(
+    dash.dependencies.Output('industry-analysis', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
+)
+def update_industry_analysis(selected_stock):
+    industry_data = []
+    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]
+            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)
+    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
 
+# 更新景氣燈號圖表
 @app.callback(
-    [
-        dash.dependencies.Output('stock-info-cards', 'children'),
-        dash.dependencies.Output('price-chart', 'figure'),
-        dash.dependencies.Output('volume-chart', 'figure'),
-        dash.dependencies.Output('industry-analysis', 'figure'),
-        dash.dependencies.Output('technical-analysis-text', 'children'),
-        dash.dependencies.Output('fundamental-analysis-text', 'children'),
-        dash.dependencies.Output('market-outlook-text', 'children')
-    ],
-    [
-        dash.dependencies.Input('stock-dropdown', 'value'),
-        dash.dependencies.Input('period-dropdown', 'value'),
-        dash.dependencies.Input('chart-type', 'value')
-    ]
+    dash.dependencies.Output('business-climate-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
 )
-def update_all_stock_info(selected_stock, selected_period, chart_type):
-    """根據選取股票更新所有相關圖表與資訊"""
-    
-    # 獲取資料
-    df = get_stock_data(selected_stock, period=selected_period)
-    df = add_technical_indicators(df)
-    
+def update_business_climate_chart(selected_stock):
+    df = get_business_climate_data()
     if df.empty:
-        return [
-            html.Div("找不到股票資訊。"),
-            go.Figure(),
-            go.Figure(),
-            go.Figure(),
-            "找不到技術分析資料。",
-            "找不到基本面分析資料。",
-            "無法提供市場展望。"
-        ]
-
-    # --- 1. 股票資訊卡片 ---
-    latest_data = df.iloc[-1]
-    last_close = df['Close'].iloc[-2] if len(df) > 1 else latest_data['Close']
-    change = latest_data['Close'] - last_close
-    change_percent = (change / last_close) * 100
-    change_color = 'green' if change >= 0 else 'red'
-    
-    info_cards = html.Div([
-        html.Div([
-            html.H5("收盤價", style={'color': '#8e44ad'}),
-            html.H3(f"{latest_data['Close']:.2f} TWD", style={'color': '#8e44ad'})
-        ], className="card", style={'border-left': '5px solid #8e44ad'}),
-        html.Div([
-            html.H5("漲跌幅", style={'color': '#27ae60'}),
-            html.H3(f"{change:.2f} ({change_percent:.2f}%)", style={'color': change_color})
-        ], className="card", style={'border-left': '5px solid #27ae60'}),
-        html.Div([
-            html.H5("成交量", style={'color': '#d35400'}),
-            html.H3(f"{latest_data['Volume']/10000:,.0f} 萬股", style={'color': '#d35400'})
-        ], className="card", style={'border-left': '5px solid #d35400'}),
-    ], style={'display': 'flex', 'justify-content': 'space-around', 'margin-bottom': '20px'})
-
-    # --- 2. 股價圖 ---
-    if chart_type == 'candlestick':
-        price_fig = go.Figure(data=[go.Candlestick(x=df.index,
-            open=df['Open'],
-            high=df['High'],
-            low=df['Low'],
-            close=df['Close'])
-        ])
-    else: # line chart
-        price_fig = px.line(df, x=df.index, y='Close', title='股價走勢圖')
-
-    price_fig.update_layout(xaxis_rangeslider_visible=False, title=f'{selected_stock} 股價走勢圖', template='plotly_white')
-
-    # --- 3. 成交量圖 ---
-    volume_fig = px.bar(df, x=df.index, y='Volume', title='成交量', color='Volume', color_continuous_scale='bluered')
-    volume_fig.update_layout(template='plotly_white', coloraxis_showscale=False)
-    
-    # --- 4. 產業表現分析 ---
-    industry_analysis_fig = go.Figure()
-    industry_stock = ''
-    for industry, stocks in INDUSTRY_MAPPING.items():
-        if selected_stock in stocks:
-            industry_stock = industry
-            for stock_symbol in stocks:
-                stock_data = get_stock_data(stock_symbol, period='1y')
-                if not stock_data.empty:
-                    industry_analysis_fig.add_trace(go.Scatter(
-                        x=stock_data.index, y=stock_data['Close'], mode='lines', name=stock_symbol,
-                        visible='legendonly' if stock_symbol != selected_stock else True
-                    ))
-            break
-    
-    industry_analysis_fig.update_layout(title=f'{industry_stock} 產業表現', template='plotly_white')
-    
-    # --- 5. 分析師觀點文字 ---
-    analysis_texts = generate_analysis_text(df)
-
-    return info_cards, price_fig, volume_fig, industry_analysis_fig, analysis_texts['technical'], analysis_texts['fundamental'], analysis_texts['outlook']
-
+        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
+    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'
+    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]))
+    return fig
 
+# 更新分析師觀點
 @app.callback(
-    dash.dependencies.Output('advanced-technical-chart', 'figure'),
-    [
-        dash.dependencies.Input('stock-dropdown', 'value'),
-        dash.dependencies.Input('period-dropdown', 'value'),
-        dash.dependencies.Input('technical-indicator-selector', 'value')
-    ]
+    [dash.dependencies.Output('technical-analysis-text', 'children'),
+     dash.dependencies.Output('fundamental-analysis-text', 'children'),
+     dash.dependencies.Output('market-outlook-text', 'children')],
+    [dash.dependencies.Input('stock-dropdown', 'value'),
+     dash.dependencies.Input('period-dropdown', 'value')]
 )
-def update_technical_indicator_chart(selected_stock, selected_period, indicator):
-    """更新技術指標圖表"""
-    df = get_stock_data(selected_stock, period=selected_period)
-    df = add_technical_indicators(df)
-    
-    if df.empty or indicator not in df.columns:
-        return go.Figure()
+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"
+    data = calculate_technical_indicators(data)
+    current_price = data['Close'].iloc[-1]
+    price_change = ((current_price - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
+    rsi_current = data['RSI'].iloc[-1] if not pd.isna(data['RSI'].iloc[-1]) else 50
+    macd_current = data['MACD'].iloc[-1] if not pd.isna(data['MACD'].iloc[-1]) else 0
+    macd_signal_current = data['MACD_Signal'].iloc[-1] if not pd.isna(data['MACD_Signal'].iloc[-1]) else 0
+    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。"]),
+    ])
+    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。"]),
+    ])
+    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："), 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
 
+# 更新PMI圖表
+@app.callback(
+    dash.dependencies.Output('pmi-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
+)
+def update_pmi_chart(selected_stock):
+    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)
+        return fig
+    colors = ['red' if value >= 50 else 'green' for value in df['Index']]
     fig = go.Figure()
-    
-    if indicator == 'RSI':
-        fig = px.line(df, x=df.index, y='RSI', title='RSI 相對強弱指標')
-        fig.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="超買線")
-        fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="超賣線")
-        fig.update_yaxes(range=[0, 100])
-    
-    elif indicator == 'MACD':
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(x=df.index, y=df['MACD'], name='MACD', mode='lines', line=dict(color='blue')))
-        fig.add_trace(go.Scatter(x=df.index, y=df['Signal_Line'], name='Signal', mode='lines', line=dict(color='red')))
-        colors = ['green' if val > 0 else 'red' for val in df['MACD_Hist']]
-        fig.add_trace(go.Bar(x=df.index, y=df['MACD_Hist'], name='Histogram', marker_color=colors))
-        fig.update_layout(title='MACD 指數平滑異同移動平均線')
-        
-    elif indicator == 'BB':
-        fig = px.line(df, x=df.index, y=['Close', 'Upper_BB', 'Lower_BB'], title='布林通道 Bollinger Bands')
-    
-    elif indicator == 'KD':
-        fig = px.line(df, x=df.index, y=['%K', '%D'], title='KD 隨機指標')
-        fig.add_hline(y=80, line_dash="dash", line_color="red", annotation_text="超買線")
-        fig.add_hline(y=20, line_dash="dash", line_color="green", annotation_text="超賣線")
-        fig.update_yaxes(range=[0, 100])
-
-    elif indicator == 'WR':
-        fig = px.line(df, x=df.index, y='%R', title='威廉指標 %R')
-        fig.add_hline(y=-20, line_dash="dash", line_color="red", annotation_text="超買線")
-        fig.add_hline(y=-80, line_dash="dash", line_color="green", annotation_text="超賣線")
-        fig.update_yaxes(range=[-100, 0])
-
-    elif indicator == 'DMI':
-        fig = px.line(df, x=df.index, y=['DIplus', 'DIminus', 'ADX'], title='DMI 動向指標')
-        fig.add_hline(y=20, line_dash="dash", line_color="orange", annotation_text="趨勢強弱參考線")
-
-    fig.update_layout(template='plotly_white')
+    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]))
     return fig
 
-
+# 更新多檔股票比較
 @app.callback(
-    [
-        dash.dependencies.Output('comparison-chart', 'figure'),
-        dash.dependencies.Output('comparison-table', 'children')
-    ],
-    [
-        dash.dependencies.Input('comparison-stocks', 'value'),
-        dash.dependencies.Input('comparison-period', 'value')
-    ]
+    [dash.dependencies.Output('comparison-chart', 'figure'),
+     dash.dependencies.Output('comparison-table', 'children')],
+    [dash.dependencies.Input('comparison-stocks', 'value'),
+     dash.dependencies.Input('comparison-period', 'value')]
 )
-def update_comparison_chart(tickers, period):
-    if not tickers:
-        return go.Figure(), html.P("請至少選擇一檔股票。")
-    
-    df_dict = {}
-    for ticker in tickers:
-        df = yf.download(ticker, period=period)
-        if not df.empty:
-            df['Normalized'] = df['Close'] / df['Close'].iloc[0] * 100
-            df_dict[ticker] = df['Normalized']
-    
-    if not df_dict:
-        return go.Figure(), html.P("找不到任何股票資料。")
-        
-    normalized_df = pd.DataFrame(df_dict)
-    
+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]
     fig = go.Figure()
-    for col in normalized_df.columns:
-        stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == col), col)
-        fig.add_trace(go.Scatter(x=normalized_df.index, y=normalized_df[col], name=stock_name, mode='lines'))
-        
-    fig.update_layout(
-        title='股票相對漲跌幅比較',
-        xaxis_title='日期',
-        yaxis_title='相對漲跌幅 (%) (基期=100)',
-        template='plotly_white'
-    )
-    
-    # 建立表格
-    summary_data = []
-    for ticker, df in df_dict.items():
-        stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == ticker), ticker)
-        start_price = df.iloc[0]
-        end_price = df.iloc[-1]
-        change_percent = (end_price - start_price)
-        summary_data.append({
-            '股票': stock_name,
-            '漲跌幅': f'{change_percent:.2f}%'
-        })
-    
-    summary_df = pd.DataFrame(summary_data)
-    table = html.Table([
-        html.Thead(html.Tr([html.Th(col) for col in summary_df.columns])),
-        html.Tbody([
-            html.Tr([
-                html.Td(summary_df.iloc[i][col]) for col in summary_df.columns
-            ]) for i in range(len(summary_df))
+    comparison_data = []
+    for stock in selected_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
+            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')
+    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%'})
+        return fig, table
+    return fig, html.Div("���可比較資料")
+
+
+# ==============================================================================
+# ===== 【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')] # 觸發條件不變
+)
+def update_sentiment_analysis(selected_stock):
+    # 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:
+            bar_color, level_text = "#d9534f", "悲觀" # 紅色
+            
+        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
         ])
-    ])
-    
-    return fig, table
+    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'})
+
+    return gauge_content, news_content
+
 
 # 主程式執行
 if __name__ == '__main__':