Update app.py

app.py

@@ -7,7 +7,7 @@ import google.generativeai as genai
 import pandas as pd
 import numpy as np
 import yfinance as yf
-from dash import Dash, dcc, html, callback
+from dash import Dash, dcc, html, callback, Input, Output, State
 import dash
 import plotly.express as px
 import plotly.graph_objects as go
@@ -39,6 +39,7 @@ CACHE_DURATION_SECONDS = 8 * 60 * 60
 # 【修改 3】: 在應用程式啟動時，預先載入 XGBoost 模型
 try:
     print("正在初始化 XGBoost 預測模型...")
+    # 使用 model_predictor.py 中的 XGBoostModel 類別
     xgb_model = XGBoostModel(default_model='xgboost_model')
     print("XGBoost 預測模型初始化成功。")
 except Exception as e:
@@ -118,102 +119,6 @@ def simple_statistical_predict(data, predict_days=5):
     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)}
 
-# 【新增】: 建立一個新的函式來處理 XGBoost 模型的輸入和輸出
-def advanced_xgboost_predict(data, predict_days):
-    """
-    【進階模型橋接函式】
-    - 準備 XGBoost 模型所需的輸入 DataFrame。
-    - 呼叫模型進行預測。
-    - 將模型的輸出格式轉換為主程式所需的格式。
-    """
-    if xgb_model is None or data.empty:
-        return None
-
-    try:
-        # 1. 準備模型所需的特徵 (Features)
-        print("正在準備 XGBoost 模型輸入特徵...")
-        
-        # 獲取外部市場數據
-        external_symbols = {'DJI': '^DJI', 'NAS': '^IXIC', 'SOX': '^SOX', 'S&P_500': '^GSPC', 'TSM_ADR': 'TSM'}
-        df_external = pd.DataFrame(index=data.index)
-        for name, ticker in external_symbols.items():
-            ext_data = yf.download(ticker, start=data.index.min(), end=data.index.max(), progress=False)
-            df_external[name] = ext_data['Close']
-        
-        # 合併所有數據並向前填充缺失值
-        input_df = pd.concat([data, df_external], axis=1)
-        input_df.ffill(inplace=True)
-
-        # 載入並合併景氣燈號和 PMI
-        df_climate = get_business_climate_data()
-        df_pmi = get_pmi_data()
-        input_df = pd.merge(input_df.reset_index(), df_climate, on='Date', how='left', suffixes=('', '_climate')).set_index('Date')
-        input_df = pd.merge(input_df.reset_index(), df_pmi, on='Date', how='left', suffixes=('', '_pmi')).set_index('Date')
-        input_df.rename(columns={'Index': 'business_climate', 'Index_pmi': 'PMI'}, inplace=True)
-        input_df[['business_climate', 'PMI']] = input_df[['business_climate', 'PMI']].ffill()
-
-        # 計算技術指標
-        input_df = calculate_technical_indicators(input_df) # 確保計算指標
-        
-        # 新增新聞情緒分數 (此處為範例，假設從 predictor 取得)
-        # 注意: predictor.get_news_index() 僅返回一個值，需要對齊到時間序列
-        news_score = predictor.get_news_index() if predictor else 0
-        input_df['NEWS'] = news_score if news_score is not None else 0
-        
-        # 新增 'rate' 欄位 (注意：此處為佔位符，您需要提供真實數據源)
-        input_df['rate'] = 1.75 # 範例：假設為固定利率，請替換為真實數據
-
-        # 欄位重新命名以符合模型訓練時的名稱
-        input_df.rename(columns={
-            'Close': 'close', 'Volume': 'volume', 'MACD_Signal': 'MACDsign',
-            'MACD_Histogram': 'MACDvol'
-        }, inplace=True)
-
-        # 確保所有模型需要的欄位都存在
-        columns_to_keep = [
-            'close', 'volume', 'rate', 'DJI', 'NAS', 'SOX', 'S&P_500', 'TSM_ADR', 'NEWS',
-            'RSI', 'MACD', 'MACDsign', 'MACDvol', 'K', 'D', '+DI', '-DI', 'ADX',
-            'business_climate', 'PMI'
-        ]
-        
-        final_input = input_df[columns_to_keep].tail(1)
-
-        if final_input.isnull().values.any():
-            print("警告: 準備好的輸入數據中存在缺失值，無法進行預測。")
-            return None
-
-        # 2. 呼叫模型預測
-        print("呼叫 XGBoost 模型進行預測...")
-        predictions = xgb_model.predict('xgboost_model', final_input)
-
-        # 3. 根據 predict_days 解析輸出
-        day_to_key_map = {
-            1: 'Close_t0_pred', 5: 'Close_t5_pred', 10: 'Close_t10_pred', 20: 'Close_t20_pred'
-        }
-        
-        prediction_key = day_to_key_map.get(predict_days)
-        
-        if prediction_key is None or prediction_key not in predictions:
-            print(f"警告: XGBoost 模型沒有提供 {predict_days} 天的預測結果。")
-            return None
-
-        predicted_price = predictions[prediction_key]
-        current_price = data['Close'].iloc[-1]
-        change_pct = ((predicted_price - current_price) / current_price) * 100
-
-        # 4. 包裝成主程式所需的格式
-        return {
-            'predicted_price': predicted_price,
-            'change_pct': change_pct,
-            'confidence': 0.95 # XGBoost模型通常不直接提供信心度，可給定一個較高的固定值
-        }
-    except Exception as e:
-        print(f"執行 XGBoost 預測時發生錯誤: {e}")
-        return None
-
-
-# 【修改 4】: 建立一個新的函式來處理 XGBoost 模型的輸入和輸出
-# 修正後的 advanced_xgboost_predict 函數
 def advanced_xgboost_predict(data, predict_days):
     """
     【進階模型橋接函式】
@@ -228,45 +133,35 @@ def advanced_xgboost_predict(data, predict_days):
         print("正在準備 XGBoost 模型所需的20個輸入特徵...")
         
         # 1. 獲取外部市場數據
-        # 建立一個與主股票數據相同索引的 DataFrame 以便對齊
         start_date = data.index.min() - pd.Timedelta(days=5) # 提前幾天以確保數據填充
         end_date = data.index.max()
         
-        df_aligned = pd.DataFrame(index=pd.date_range(start=start_date, end=end_date, freq='D'))
-
         external_symbols = {'DJI': '^DJI', 'NAS': '^IXIC', 'SOX': '^SOX', 'S&P_500': '^GSPC', 'TSM_ADR': 'TSM'}
         
         # 使用 yf.download 一次性獲取所有外部數據
         ext_data = yf.download(list(external_symbols.values()), start=start_date, end=end_date, progress=False)['Close']
         ext_data.rename(columns={v: k for k, v in external_symbols.items()}, inplace=True)
         
-        # 2. 合併所有數據源
-        # 將主數據與外部數據合併
+        # 2. 合併主數據與外部數據
         input_df = data.join(ext_data, how='left')
         
-        # 載入並合併景氣燈號和 PMI
-        df_climate = get_business_climate_data() #
-        df_pmi = get_pmi_data() #
+        # 3. 計算技術指標
+        input_df = calculate_technical_indicators(input_df)
         
-        # 將 'Date' 轉為 datetime 物件以進行合併
-        input_df.index = pd.to_datetime(input_df.index)
-        df_climate['Date'] = pd.to_datetime(df_climate['Date']) #
-        df_pmi['Date'] = pd.to_datetime(df_pmi['Date']) #
-
-        input_df = pd.merge(input_df, df_climate.rename(columns={'Index': 'business_climate'}), on='Date', how='left')
-        input_df = pd.merge(input_df, df_pmi.rename(columns={'Index': 'PMI'}), on='Date', how='left')
+        # 4. 新增其他特徵 (利率, 新聞情緒, PMI, 景氣燈號)
+        input_df['rate'] = 1.75 # 注意：此為利率佔位符，請替換為真實數據源
+        news_score = predictor.get_news_index() if predictor else 0
+        input_df['NEWS'] = news_score if news_score is not None else 0
         
+        # 【修改】: 獲取最新月份的單一數值
+        input_df['business_climate'] = get_business_climate_data()
+        input_df['PMI'] = get_pmi_data()
+        
+        # 5. 數據清洗與格式化
         # 向前填充所有缺失值 (例如假日)
         input_df.ffill(inplace=True)
         input_df.bfill(inplace=True) # 向後填充開頭可能存在的缺失值
 
-        # 3. 計算技術指標與新增其他特徵
-        input_df = calculate_technical_indicators(input_df) #
-        news_score = predictor.get_news_index() if predictor else 0
-        input_df['NEWS'] = news_score if news_score is not None else 0
-        input_df['rate'] = 1.75 # 注意：此為利率佔位符，請替換為真實數據源
-
-        # 4. 格式化最終輸入
         input_df.rename(columns={
             'Close': 'close', 'Volume': 'volume', 'MACD_Signal': 'MACDsign',
             'MACD_Histogram': 'MACDvol'
@@ -290,11 +185,21 @@ def advanced_xgboost_predict(data, predict_days):
             print(f"警告: 最終輸入數據中存在缺失值，無法預測。\n{final_input.isnull().sum()}")
             return None
 
-        # 5. 呼叫模型預測
+        # 6. 呼叫模型預測
         print("特徵準備完成，呼叫 XGBoost 模型...")
         predictions = xgb_model.predict('xgboost_model', final_input)
 
-        predicted_price = predictions['Close_t5_pred'] # 預設獲取5日預測
+        # 根據預測天期選擇對應的輸出欄位
+        if predict_days <= 1:
+            pred_col = 'Close_t1_pred'
+        elif predict_days <= 5:
+            pred_col = 'Close_t5_pred'
+        elif predict_days <= 10:
+            pred_col = 'Close_t10_pred'
+        else: # predict_days >= 20
+            pred_col = 'Close_t20_pred'
+            
+        predicted_price = predictions[pred_col]
         current_price = data['Close'].iloc[-1]
         change_pct = ((predicted_price - current_price) / current_price) * 100
 
@@ -316,13 +221,11 @@ def get_prediction(data, predict_days=5):
     """
     if USE_ADVANCED_MODEL:
         print(f"模式: 進階XGBoost模型 | 預測天期: {predict_days}天")
-        # 【修改】: 呼叫新的 XGBoost 橋接函式
         prediction = advanced_xgboost_predict(data, predict_days)
-        # 如果進階模型預測失敗，則自動降級使用簡易模型
         if prediction is not None:
             return prediction
         else:
-            print("進階模型預測失敗或無對應天期，自動降級為簡易統計模型。")
+            print("進階模型預測失敗，自動降級為簡易統計模型。")
     
     # 預設或降級時執行簡易模型
     print(f"模式: 簡易統計模型 | 預測天期: {predict_days}天")
@@ -370,7 +273,6 @@ 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
     
-    # 確保沒有 NaN 值
     df_clean = df.dropna(subset=['High', 'Low', 'Close', 'Volume'])
     if df_clean.empty:
         return None, None, None
@@ -381,7 +283,6 @@ def calculate_volume_profile(df, num_bins=50):
     if min_price == max_price:
         return None, None, None
     
-    # 使用典型價格作為價格指標
     price_for_volume = (df_clean['High'] + df_clean['Low'] + df_clean['Close']) / 3
     
     try:
@@ -397,34 +298,28 @@ def calculate_volume_profile(df, num_bins=50):
         print(f"Volume profile 計算錯誤: {e}")
         return None, None, None
 
-def get_business_climate_data():
+def get_business_climate_data(get_latest_value=False):
     try:
-        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'])
+        if not os.path.exists('business_climate.csv'): return None
+        df = pd.read_csv('business_climate.csv', index_col='Date', parse_dates=True)
+        if get_latest_value:
+            return df['Index'].iloc[-1]
         return df
     except Exception as e:
         print(f"無法獲取景氣燈號資料: {str(e)}")
-        return pd.DataFrame()
+        return None if get_latest_value else pd.DataFrame()
 
-def get_pmi_data():
+def get_pmi_data(get_latest_value=False):
     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'])
+        if not os.path.exists('taiwan_pmi.csv'): return None
+        df = pd.read_csv('taiwan_pmi.csv', index_col='Date', parse_dates=True)
+        if get_latest_value:
+            return df['Index'].iloc[-1]
         return df
     except Exception as e:
         print(f"無法獲取 PMI 資料: {str(e)}")
-        return pd.DataFrame()
+        return None if get_latest_value else pd.DataFrame()
+
 
 def generate_gemini_analysis(stock_name, stock_symbol, period, data):
     """
@@ -479,7 +374,6 @@ def generate_gemini_analysis(stock_name, stock_symbol, period, data):
             market_outlook = parts[1].strip()
             return dcc.Markdown(fundamental_analysis), dcc.Markdown(market_outlook)
         else:
-            # Fallback for unexpected response format
             return dcc.Markdown("無法解析 Gemini 回應，請稍後再試。"), dcc.Markdown(response.text)
 
     except Exception as e:
@@ -489,6 +383,7 @@ def generate_gemini_analysis(stock_name, stock_symbol, period, data):
 
 # 建立 Dash 應用程式
 app = dash.Dash(__name__, suppress_callback_exceptions=True)
+server = app.server
 
 try:
     print("正在初始化新聞情緒分析模型...")
@@ -516,10 +411,18 @@ app.layout = html.Div([
             html.Div(id='taiex-prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
         ]),
         html.Div([dcc.Graph(id='taiex-prediction-chart')], style={'margin-top': '20px'})
-    ], style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','padding': '25px','border-radius': '15px','box-shadow': '0 8px 25px rgba(0,0,0,0.15)','color': 'white','margin-bottom': '40px'}),
-    
+    ], style={
+        'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
+        'padding': '25px',
+        'border-radius': '15px',
+        'box-shadow': '0 8px 25px rgba(0,0,0,0.15)',
+        'color': 'white',
+        'margin-bottom': '40px'
+    }),
+
     html.Div([
         html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
+        dcc.Interval(id='news-interval-component', interval=30*60*1000, n_intervals=0), # 30分鐘更新一次
         html.Div([
             html.Div([
                 html.H4("市場情緒指標", style={'color': '#8E44AD'}),
@@ -539,544 +442,224 @@ app.layout = html.Div([
             html.Div([dcc.Graph(id='pmi-chart')], style={'width': '48%', 'display': 'inline-block', 'margin-left': '2%'})
         ])
     ], style={'margin-top': '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='0050.TW', style={'margin-bottom': '10px'})
+            dcc.Dropdown(id='stock-dropdown',
+                options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()],
+                value='0050.TW',
+                style={'margin-bottom': '10px'})
         ], style={'width': '30%', 'display': 'inline-block', 'vertical-align': 'top'}),
         html.Div([
             html.Label("時間範圍:"),
             dcc.Dropdown(id='period-dropdown',
-                options=[{'label': '1個月', 'value': '1mo'},{'label': '3個月', 'value': '3mo'},{'label': '6個月', 'value': '6mo'},{'label': '1年', 'value': '1y'},{'label': '2年', 'value': '2y'}],
-                value='1mo', style={'margin-bottom': '10px'})
+                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='1mo',
+                style={'margin-bottom': '10px'})
         ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'}),
         html.Div([
             html.Label("圖表類型:"),
-            dcc.Dropdown(id='chart-type', options=[{'label': '線圖', 'value': 'line'},{'label': '蠟燭圖', 'value': 'candlestick'}], value='candlestick', style={'margin-bottom': '10px'})
-        ], 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([
-        html.H3("📊 進階技術指標分析", style={'margin-bottom': '20px'}),
-        html.Div([
-            html.Label("選擇技術指標:", style={'font-weight': 'bold', 'margin-right': '10px'}),
-            dcc.Dropdown(id='technical-indicator-selector',
-                options=[{'label': 'RSI 相對強弱指標', 'value': 'RSI'},{'label': 'MACD 指數平滑異同移動平均線', 'value': 'MACD'},{'label': '布林通道 Bollinger Bands', 'value': 'BB'},
-                         {'label': 'KD 隨機指標', 'value': 'KD'},{'label': '威廉指標 %R', 'value': 'WR'},{'label': 'DMI 動向指標', 'value': 'DMI'}],
-                value='RSI', style={'width': '100%'})
-        ], 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'}),
-    html.Div([
-        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'}),
-                html.Div(id='technical-analysis-text', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','border-left': '4px solid #A23B72','min-height': '150px','font-size': '14px','line-height': '1.6'})
-            ], style={'width': '48%', 'display': 'inline-block', 'vertical-align': 'top'}),
-            html.Div([
-                html.H4("📈 基本面分析 (AI 生成)", style={'color': '#F18F01', 'margin-bottom': '15px'}),
-                html.Div(id='fundamental-analysis-text', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','border-left': '4px solid #F18F01','min-height': '150px','font-size': '14px','line-height': '1.6'})
-            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%', 'vertical-align': 'top'})
-        ]),
-        html.Div([
-            html.H4("🎯 市場展望與投資建議 (AI 生成)", style={'color': '#C73E1D', 'margin-bottom': '15px', 'margin-top': '25px'}),
-            html.Div(id='market-outlook-text', style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','color': 'white','padding': '20px','border-radius': '10px','min-height': '100px','font-size': '15px','line-height': '1.7','box-shadow': '0 4px 15px rgba(0,0,0,0.1)'})
-        ])
-    ], 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'}),
+            dcc.Dropdown(id='chart-type-dropdown',
+                options=[
+                    {'label': 'K線圖', 'value': 'candlestick'},
+                    {'label': '折線圖', 'value': 'line'}],
+                value='candlestick',
+                style={'margin-bottom': '10px'})
+        ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'}),
+    ]),
+    
     html.Div([
-        html.H3("📊 多檔股票比較分析", style={'margin-bottom': '20px'}),
+        dcc.Graph(id='stock-chart'),
         html.Div([
-            html.Div([
-                html.Label("選擇比較股票（最多5檔）:", style={'font-weight': 'bold'}),
-                dcc.Dropdown(id='comparison-stocks', options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()], value=['0050.TW', '2330.TW', '2454.TW'], multi=True, style={'margin-bottom': '5px'}),
-                html.Small('（元大台灣50 (0050.TW) 為固定比較基準，不可移除）', style={'display': 'block', 'font-style': 'italic', 'color': 'gray'})
-            ], style={'width': '60%', 'display': 'inline-block'}),
-            html.Div([
-                html.Label("比較期間:", style={'font-weight': 'bold'}),
-                dcc.Dropdown(id='comparison-period', options=[{'label': '1個月', 'value': '1mo'},{'label': '3個月', 'value': '3mo'},{'label': '6個月', 'value': '6mo'},{'label': '1年', 'value': '1y'}], value='3mo')
-            ], style={'width': '35%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'})
-        ]),
-        html.Div([
-            html.Div([dcc.Graph(id='comparison-chart')], style={'width': '65%', 'display': 'inline-block'}),
-            html.Div([html.H4("比較結果", style={'color': '#2E86AB'}), html.Div(id='comparison-table')], style={'width': '33%', 'display': 'inline-block', 'margin-left': '2%', 'vertical-align': 'top'})
-        ])
-    ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
+            html.H4("AI 智慧分析", style={'text-align': 'center', 'margin-top': '20px', 'margin-bottom': '10px'}),
+            html.Div(id='gemini-fundamental-analysis', style={'width': '48%', 'display': 'inline-block', 'background': '#f8f9fa', 'padding': '15px', 'border-radius': '8px'}),
+            html.Div(id='gemini-market-outlook', style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%', 'background': '#f8f9fa', 'padding': '15px', 'border-radius': '8px'})
+        ], style={'margin-top': '20px'})
+    ]),
 ])
 
+# 更新台指期預測
 @app.callback(
-    [dash.dependencies.Output('taiex-prediction-results', 'children'),
-     dash.dependencies.Output('taiex-prediction-chart', 'figure')],
-    [dash.dependencies.Input('taiex-prediction-period', 'value')]
+    [Output('taiex-prediction-results', 'children'),
+     Output('taiex-prediction-chart', 'figure')],
+    [Input('taiex-prediction-period', 'value')]
 )
 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"), {}
-    
-    # === 呼叫 get_prediction 控制器，它會自動選擇模型 ===
-    final_prediction = get_prediction(data, predict_days)
-    
-    if final_prediction is None: return html.Div("資料不足，無法進行預測"), {}
-    current_price, last_date = data['Close'].iloc[-1], data.index[-1]
-    predicted_price, change_pct, confidence = final_prediction['predicted_price'], final_prediction['change_pct'], final_prediction['confidence']
-    
-    prediction_paths = {1: [1], 5: [1, 5], 10: [1, 5, 10], 20: [1, 10, 20], 60: [1, 10, 20, 60]}
-    intervals_to_predict = prediction_paths.get(predict_days, [predict_days])
-    prediction_dates, prediction_prices = [last_date], [current_price]
-    
-    for days in intervals_to_predict:
-        # === 迴圈內也使用統一的預測控制器 ===
-        interim_prediction = get_prediction(data, days)
-        if interim_prediction:
-            prediction_dates.append(last_date + timedelta(days=days))
-            prediction_prices.append(interim_prediction['predicted_price'])
-            
-    # (後續繪圖邏輯不變)
-    color, arrow = ('red', '📈') if change_pct >= 0 else ('green', '📉')
-    result_card = html.Div([
-        html.H4(f"{predict_days}日後預測結果", style={'margin': '0 0 15px 0', 'color': 'white'}),
-        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
+    data = get_stock_data('TXF=F', '2y')
+    if data.empty:
+        return "無法獲取台指期數據", go.Figure()
 
-@app.callback(
-    dash.dependencies.Output('stock-info-cards', 'children'),
-    [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")
-    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'})
-    ])
-
-@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')]
-)
-# 修正後的 update_price_chart callback 函數的相關部分
-def update_price_chart_fixed(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(go.Scatter(
-            x=data.index, 
-            y=data['Close'], 
-            mode='lines', 
-            name=stock_name
-        ), row=1, col=1)
-    
-    # 添加移動平均線
-    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)
-    
-    # 修正後的 Volume Profile 計算
-    bin_edges, volume_per_bin, price_centers = calculate_volume_profile(data, num_bins=50)
-    
-    if volume_per_bin is not None and price_centers 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
-    )
-    
-    return fig
+    prediction = get_prediction(data, predict_days)
 
-@app.callback(
-    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_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()
-    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':
+    if prediction:
+        current_price = data['Close'].iloc[-1]
+        predicted_price = prediction['predicted_price']
+        change_pct = prediction['change_pct']
+        confidence = prediction['confidence']
+        
+        results_text = f"""
+        預測 {predict_days} 日後指數: **{predicted_price:.2f}**
+        (相較於目前 {current_price:.2f}，預計變動 **{change_pct:+.2f}%**，信心指數: {confidence:.0%})
+        """
+        
+        future_date = data.index[-1] + timedelta(days=predict_days)
         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
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='歷史指數'))
+        fig.add_trace(go.Scatter(x=[data.index[-1], future_date], 
+                                 y=[current_price, predicted_price], 
+                                 mode='lines+markers', name='預測趨勢',
+                                 line=dict(dash='dot', color='orange')))
+        fig.update_layout(title_text=f"台指期未來 {predict_days} 日趨勢預測", 
+                          xaxis_title="日期", yaxis_title="指數",
+                          legend=dict(x=0.01, y=0.99))
+        return dcc.Markdown(results_text), fig
+    else:
+        return "模型預測失敗", go.Figure()
 
+# 更新新聞情緒和摘要
 @app.callback(
-    dash.dependencies.Output('volume-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value'),
-     dash.dependencies.Input('period-dropdown', 'value')]
+    [Output('sentiment-gauge', 'children'),
+     Output('news-summary', 'children')],
+    [Input('news-interval-component', 'n_intervals')]
 )
-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
+def update_news_sentiment(n):
+    if not predictor:
+        return "情緒模型未載入", "新聞摘要無法使用"
+
+    sentiment_score = predictor.get_news_index()
+    if sentiment_score is not None:
+        level_text = "極度恐慌" if sentiment_score < 20 else "恐慌" if sentiment_score < 40 else "中性" if sentiment_score < 60 else "樂觀" if sentiment_score < 80 else "極度樂觀"
+        bar_color = "red" if sentiment_score < 40 else "orange" if sentiment_score < 60 else "green"
+        
+        gauge_fig = go.Figure(go.Indicator(
+            mode = "gauge+number",
+            value = sentiment_score,
+            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]}, 'bar': {'color': bar_color},
+                     '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:
+        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)
 
-@app.callback(
-    dash.dependencies.Output('industry-analysis', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]
-)
-def update_industry_analysis(selected_stock):
-    performance_data = []
-    for name, symbol in TAIWAN_STOCKS.items():
-        data = get_stock_data(symbol, '1mo')
-        if not data.empty and len(data) > 1:
-            return_pct = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
-            performance_data.append({
-                '股票': name,
-                '代碼': symbol,
-                '月報酬率(%)': return_pct,
-                '絕對波動': abs(return_pct)
-            })
-    if not performance_data:
-        fig = go.Figure().add_annotation(text="無法計算產業資料", showarrow=False)
-        fig.update_layout(title="近一月市場波動最大標的", height=400)
-        return fig
-    df_performance = pd.DataFrame(performance_data)
-    df_top_movers = df_performance.sort_values(by='絕對波動', ascending=False).head(10)
-    fig = px.pie(
-        df_top_movers,
-        values='絕對波動',
-        names='股票',
-        title='近一月市場波動最大 Top 10 標的',
-        hover_data={'月報酬率(%)': ':.2f'}
-    )
-    fig.update_traces(
-        textposition='inside',
-        textinfo='percent+label',
-        hovertemplate="<b>%{label}</b><br>月報酬率: %{customdata[0]:.2f}%<extra></extra>"
-    )
-    fig.update_layout(height=400, showlegend=False)
-    return fig
+    top_news_list = predictor.get_news()
+    news_content = None
+
+    if top_news_list and isinstance(top_news_list, list):
+        summary_text = "\n\n".join([f"- [{news['title']}]({news['link']})" for news in top_news_list])
+        news_content = dcc.Markdown(summary_text)
+    else:
+        news_content = html.P("無法獲取新聞摘要。")
+        
+    return gauge_content, news_content
 
+
+# 更新景氣燈號圖表
 @app.callback(
-    dash.dependencies.Output('business-climate-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]
+    Output('business-climate-chart', 'figure'),
+    [Input('stock-dropdown', 'value')] # 隨便觸發即可
 )
-def update_business_climate_chart(selected_stock):
+def update_business_climate_chart(_):
     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)
+    if not df.empty:
+        fig = px.line(df, x=df.index, y='Index', title='台灣景氣對策信號')
         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
+    return go.Figure()
 
+# 更新PMI圖表
 @app.callback(
-    [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')]
+    Output('pmi-chart', 'figure'),
+    [Input('stock-dropdown', 'value')] # 隨便觸發即可
 )
-def update_analysis_text(selected_stock, period):
-    cache_key = f"{selected_stock}-{period}"
-    current_time = time.time()
-
-    if cache_key in ANALYSIS_CACHE:
-        cached_data = ANALYSIS_CACHE[cache_key]
-        if current_time - cached_data['timestamp'] < CACHE_DURATION_SECONDS:
-            print(f"從快取載入分析: {cache_key}")
-            return cached_data['technical'], cached_data['fundamental'], cached_data['outlook']
-
-    print(f"重新生成分析: {cache_key}")
-    data = get_stock_data(selected_stock, period)
-    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-    if data.empty or len(data) < 20:
-        return "資料不足，無法分析", "資料不足，無法分析", "資料不足，無法分析"
-    
-    data = calculate_technical_indicators(data)
-    
-    price_change = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
-    rsi_current = data['RSI'].iloc[-1] if not pd.isna(data['RSI'].iloc[-1]) else 50
-    macd_current = data['MACD'].iloc[-1] if not pd.isna(data['MACD'].iloc[-1]) else 0
-    macd_signal_current = data['MACD_Signal'].iloc[-1] if not pd.isna(data['MACD_Signal'].iloc[-1]) else 0
-    
-    technical_text = html.Div([
-        html.P([html.Strong("價格趨勢："), f"在最近 {period} 期間內，{stock_name} 股價呈現", html.Span(f"{'上漲' if price_change > 5 else '下跌' if price_change < -5 else '盤整'}", style={'color': 'red' if price_change > 5 else 'green' if price_change < -5 else 'orange', 'font-weight': 'bold'}), f"走勢，累計變動 {price_change:+.1f}%。"]),
-        html.P([html.Strong("RSI 指標："), f"目前的 RSI 值為 {rsi_current:.1f}，", html.Span("處於超買區(>70)" if rsi_current > 70 else "處於超賣��(<30)" if rsi_current < 30 else "在正常範圍內", style={'color': 'green' if rsi_current > 70 else 'red' if rsi_current < 30 else 'blue', 'font-weight': 'bold'}), "。"]),
-        html.P([html.Strong("MACD 指標："), f"MACD 快線 ({macd_current:.3f}) 目前", html.Span("高於" if macd_current > macd_signal_current else "低於", style={'color': 'red' if macd_current > macd_signal_current else 'green', 'font-weight': 'bold'}), f" Signal 慢線 ({macd_signal_current:.3f})，", f"顯示市場動能偏向{'多頭' if macd_current > macd_signal_current else '空頭'}。"]),
-    ])
-    
-    fundamental_text, market_outlook_text = generate_gemini_analysis(stock_name, selected_stock, period, data)
-    
-    ANALYSIS_CACHE[cache_key] = {
-        'technical': technical_text,
-        'fundamental': fundamental_text,
-        'outlook': market_outlook_text,
-        'timestamp': current_time
-    }
-
-    return technical_text, fundamental_text, market_outlook_text
-
-@app.callback(
-    dash.dependencies.Output('pmi-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]
-)
-def update_pmi_chart(selected_stock):
+def update_pmi_chart(_):
     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)
+    if not df.empty:
+        fig = px.line(df, x=df.index, y='Index', title='台灣PMI指數')
+        fig.add_hline(y=50, line_dash="dot", annotation_text="榮枯線 (50)", annotation_position="bottom right")
         return fig
-    colors = ['red' if value >= 50 else 'green' for value in df['Index']]
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=df['Date'], y=df['Index'], mode='lines+markers', name='PMI指數', line=dict(color='darkblue', width=2), marker=dict(size=8, color=colors, line=dict(width=2, color='darkblue'))))
-    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
-
-def summarize_news_with_gemini(news_list: list) -> str:
-    """
-    使用 Gemini API 將英文新聞標題列表摘要成一段繁體中文。
-    """
-    api_key = os.getenv("GEMINI_API_KEY")
-    if not api_key:
-        return "錯誤：找不到 GEMINI_API_KEY。請在 Hugging Face Secrets 中設定。"
-
-    try:
-        genai.configure(api_key=api_key)
-        model = genai.GenerativeModel('gemini-1.5-flash')
-
-        formatted_news = "\n".join([f"- {news}" for news in news_list])
-
-        prompt = f"""
-        請扮演一位專業的金融市場分析師。
-        以下是幾則最新的英文財經新聞標題，請將它們整合成一段簡潔、流暢、約 200 字的繁體中文市場動態摘要，與利多哪些產業，利空哪些產業。
-        提供3段重點，
-        請專注於可能影響市場情緒和股價的關鍵資訊，並直接提供摘要內容，不要包含任何額外的問候語或說明。
-
-        英文新聞標題如下：
-        {formatted_news}
-        """
-
-        response = model.generate_content(prompt)
-        return response.text
-
-    except Exception as e:
-        print(f"呼叫 Gemini API 時發生錯誤: {e}")
-        return f"無法生成新聞摘要，請稍後再試。錯誤訊息：{e}"
+    return go.Figure()
 
+# 更新個股圖表和Gemini分析
 @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')]
+    [Output('stock-chart', 'figure'),
+     Output('gemini-fundamental-analysis', 'children'),
+     Output('gemini-market-outlook', 'children')],
+    [Input('stock-dropdown', 'value'),
+     Input('period-dropdown', 'value'),
+     Input('chart-type-dropdown', '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]
-    fig = go.Figure()
-    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("無可比較資料")
+def update_main_chart_and_analysis(stock_symbol, period, chart_type):
+    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == stock_symbol][0]
+    data = get_stock_data(stock_symbol, period)
 
-@app.callback(
-    [dash.dependencies.Output('sentiment-gauge', 'children'),
-     dash.dependencies.Output('news-summary', 'children')],
-    [dash.dependencies.Input('stock-dropdown', 'value')]
-)
-def update_sentiment_analysis(selected_stock):
-    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。")
+    if data.empty:
+        return go.Figure().update_layout(title=f"無法獲取 {stock_name} 的資料"), "無法生成分析", "無法生成分析"
 
-    sentiment_score_raw = predictor.get_news_index()
+    data = calculate_technical_indicators(data)
+    
+    # 圖表製作
+    fig = make_subplots(rows=4, cols=1, shared_xaxes=True, vertical_spacing=0.03,
+                        row_heights=[0.5, 0.15, 0.15, 0.2])
 
-    if sentiment_score_raw is not None:
-        sentiment_score_normalized = (sentiment_score_raw + 1) * 50
-        sentiment_score_normalized = max(0, min(100, sentiment_score_normalized))
-        if sentiment_score_normalized >= 65:
-            bar_color, level_text = "#5cb85c", "樂觀"
-        elif sentiment_score_normalized >= 35:
-            bar_color, level_text = "#f0ad4e", "中性"
-        else:
-            bar_color, level_text = "#d9534f", "悲觀"
-        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]}, 'bar': {'color': bar_color},
-                     '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)
+    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='K線'), row=1, col=1)
     else:
-        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)
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價'), row=1, col=1)
 
-    top_news_list = predictor.get_news()
-    news_content = None
-
-    if top_news_list and isinstance(top_news_list, list):
-        summary_text = summarize_news_with_gemini(top_news_list)
-        news_content = dcc.Markdown(summary_text, style={
-            'margin': '8px 0', 'padding-left': '5px',
-            'font-size': '15px', 'line-height': '1.7'
-        })
-    elif top_news_list == []:
-         news_content = html.P("昨日無重大相關新聞。", style={'text-align': 'center', 'padding-top': '50px'})
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], mode='lines', name='5日均線', line=dict(width=1)), row=1, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], mode='lines', name='20日均線', line=dict(width=1)), row=1, col=1)
+    
+    # 交易量
+    fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='交易量'), row=2, col=1)
+
+    # RSI
+    fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], mode='lines', name='RSI'), row=3, col=1)
+    fig.add_hline(y=70, line_dash="dot", row=3, col=1, line_color="red")
+    fig.add_hline(y=30, line_dash="dot", row=3, col=1, line_color="green")
+
+    # MACD
+    fig.add_trace(go.Bar(x=data.index, y=data['MACD_Histogram'], name='MACD柱'), row=4, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], mode='lines', name='MACD線'), row=4, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['MACD_Signal'], mode='lines', name='信號線'), row=4, col=1)
+
+    fig.update_layout(title_text=f"{stock_name} ({stock_symbol}) - 技術分析",
+                      xaxis_rangeslider_visible=False,
+                      height=700)
+    fig.update_yaxes(title_text="價格", row=1, col=1)
+    fig.update_yaxes(title_text="交易量", row=2, col=1)
+    fig.update_yaxes(title_text="RSI", row=3, col=1)
+    fig.update_yaxes(title_text="MACD", row=4, col=1)
+    
+    # Gemini 分析
+    cache_key = f"{stock_symbol}-{period}"
+    current_time = time.time()
+    
+    if cache_key in ANALYSIS_CACHE and (current_time - ANALYSIS_CACHE[cache_key]['timestamp']) < CACHE_DURATION_SECONDS:
+        print(f"從快取載入 {stock_name} 的分析...")
+        fundamental_analysis = ANALYSIS_CACHE[cache_key]['fundamental']
+        market_outlook = ANALYSIS_CACHE[cache_key]['outlook']
     else:
-        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
+        print(f"為 {stock_name} 生成新的 Gemini 分析...")
+        fundamental_analysis, market_outlook = generate_gemini_analysis(stock_name, stock_symbol, period, data)
+        ANALYSIS_CACHE[cache_key] = {
+            'fundamental': fundamental_analysis,
+            'outlook': market_outlook,
+            'timestamp': current_time
+        }
+        
+    return fig, fundamental_analysis, market_outlook
 
-# 主程式執行
+# 啟動伺服器
 if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860, debug=False)
+    app.run_server(debug=True)