Update app.py

app.py

@@ -21,12 +21,12 @@ import time # 引用 time 模組以處理時間戳
 # 引用您組員的預測器程式
 from Bert_predict import BertPredictor
 
-# 【【【修改 1】】】: 匯入 XGBoostModel 類別，而不是舊的函式
+# 【修改 1】: 匯入 XGBoostModel 類別
 from model_predictor import XGBoostModel
 # ========================== 引用外部模組 END ==========================
 
 # ========================= 全域設定 START =========================
-# 【【【修改 2】】】: 將開關設為 True 來啟用您的 XGBoost 模型
+# 【修改 2】: 將開關設為 True 來啟用您的 XGBoost 模型
 USE_ADVANCED_MODEL = True
 
 # ========================= CACHE 設定 START =========================
@@ -36,7 +36,7 @@ ANALYSIS_CACHE = {}
 CACHE_DURATION_SECONDS = 8 * 60 * 60
 # ========================== CACHE 設定 END ==========================
 
-# 【【【修改 3】】】: 在應用程式啟動時，預先載入 XGBoost 模型
+# 【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...")
     xgb_model = XGBoostModel(default_model='xgboost_model')
@@ -47,10 +47,9 @@ except Exception as e:
     USE_ADVANCED_MODEL = False
     xgb_model = None
     print("警告：已自動切換回簡易統計模型模式。")
-
 # ========================== 全域設定 END ==========================
 
-# 台股代號對應表 (此處省略，與您原檔案相同)
+# 台股代號對應表
 TAIWAN_STOCKS = {
     '元大台灣50': '0050.TW', '台積電': '2330.TW', '聯發科': '2454.TW',
     '鴻海': '2317.TW', '台達電': '2308.TW', '廣達': '2382.TW', '富邦金': '2881.TW',
@@ -69,7 +68,7 @@ TAIWAN_STOCKS = {
     '譜瑞-KY': '4966.TWO', '貿聯-KY': '3665.TW', '騰雲': '6870.TWO', '穩懋': '3105.TWO'
 }
 
-# 產業分類 (此處省略，與您原檔案相同)
+# 產業分類
 INDUSTRY_MAPPING = {
     '0050.TW': 'ETF', '2330.TW': '半導體', '2454.TW': '半導體', '2317.TW': '電子組件',
     '2308.TW': '電子', '2382.TW': '電子', '2881.TW': '金融', '2891.TW': '金融',
@@ -88,7 +87,6 @@ INDUSTRY_MAPPING = {
     '3665.TW': '連接器', '6870.TWO': '軟體整合', '3105.TWO': 'PA功率'
 }
 
-
 def get_stock_data(symbol, period='1y'):
     """獲取股票資料"""
     try:
@@ -120,7 +118,7 @@ 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)}
 
-# 【【【修改 4】】】: 建立一個新的函式來處理 XGBoost 模型的輸入和輸出
+# 【修改 4】: 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】
@@ -179,7 +177,7 @@ 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 5】】】: 呼叫新的 XGBoost 橋接函式
+        # 【修改 5】: 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:
@@ -191,9 +189,6 @@ def get_prediction(data, predict_days=5):
     print(f"模式: 簡易統計模型 | 預測天期: {predict_days}天")
     return simple_statistical_predict(data, predict_days)
 
-# (後續所有函式，如 calculate_technical_indicators, generate_gemini_analysis 等，都保持不變)
-# ... (此處省略所有未修改的函式，以節省篇幅) ...
-# ... (您的 calculate_technical_indicators, calculate_volume_profile, get_business_climate_data, get_pmi_data, generate_gemini_analysis 等函式放在這裡) ...
 def calculate_technical_indicators(df):
     """計算技術指標"""
     if df.empty: return df
@@ -273,20 +268,27 @@ def get_pmi_data():
         return pd.DataFrame()
 
 def generate_gemini_analysis(stock_name, stock_symbol, period, data):
+    """
+    使用 Gemini API 生成基本面和市場展望分析。
+    """
     api_key = os.getenv("GEMINI_API_KEY")
     if not api_key:
         return "無法讀取 GEMINI API 金鑰", "請在系統環境變數中設定您的金鑰"
+
     try:
         genai.configure(api_key=api_key)
         model = genai.GenerativeModel('gemini-1.5-flash')
+
         price_change = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
         rsi_current = data['RSI'].iloc[-1]
         macd_current = data['MACD'].iloc[-1]
         macd_signal_current = data['MACD_Signal'].iloc[-1]
         industry = INDUSTRY_MAPPING.get(stock_symbol, '綜合')
+
         prompt = f"""
         請扮演一位專業、資深的台灣股市金融分析師。
         我將提供一檔台股的即時技術指標數據，請你基於這些數據，結合你對這家公司、其所在產業以及當前市場趨勢的理解，為我生成一段專業的��基本面分析」和一段「市場展望與投資建議」。
+
         **股票資訊:**
         - **公司名稱:** {stock_name} ({stock_symbol})
         - **分析期間:** 最近 {period}
@@ -294,19 +296,23 @@ def generate_gemini_analysis(stock_name, stock_symbol, period, data):
         - **期間價格變動:** {price_change:+.2f}%
         - **目前 RSI 指標:** {rsi_current:.2f}
         - **目前 MACD 指標:** MACD線為 {macd_current:.3f}, 信號線為 {macd_signal_current:.3f}
+
         **你的任務:**
         1.  **基本面分析 (約 150 字):**
             -   評論這家公司的產業地位、近期營運亮點或挑戰。
             -   提及任何可能影響其基本面的關鍵因素 (例如：財報、法說會、政策、供應鏈變化等)。
             -   請用專業、客觀的語氣撰寫。
+
         2.  **市場展望與投資建議 (約 150 字):**
             -   基於上述所有資訊，提供對該股票的短期和中期市場展望。
             -   提出具體的投資建議，例如：適合何種類型的投資人、潛在的風險點。
             -   請直接提供分析內容，不要包含任何問候語。
+
         **輸出格式:**
         請嚴格按照以下格式回傳，使用"$$"作為兩個段落之間的分隔符：
         [基本面分析內容]$$[市場展望與投資建議內容]
         """
+
         response = model.generate_content(prompt)
         parts = response.text.split('$$')
         if len(parts) == 2:
@@ -314,7 +320,9 @@ 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:
         error_message = f"呼叫 Gemini API 時發生錯誤: {str(e)}"
         print(error_message)
@@ -331,8 +339,7 @@ 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 保持不變)
-# ... (此處省略整個 app.layout 區塊，與您原檔案相同) ...
+# 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([
@@ -343,9 +350,8 @@ app.layout = html.Div([
                 dcc.Dropdown(id='taiex-prediction-period',
                     options=[
                         {'label': '1日後預測', 'value': 1},{'label': '5日後預測', 'value': 5},
-                        {'label': '10日後預測', 'value': 10},{'label': '20日後預測', 'value': 20},
-                        # {'label': '60日後預測', 'value': 60} # 您的模型不支援60天，暫時註解
-                        ], value=5,
+                        {'label': '10日後預測', 'value': 10},{'label': '20日後預測', 'value': 20}],
+                    value=5,
                     style={'margin-bottom': '10px', 'color': '#272727'})
             ], style={'width': '30%', 'display': 'inline-block'}),
             html.Div(id='taiex-prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
@@ -353,7 +359,6 @@ app.layout = html.Div([
         html.Div([dcc.Graph(id='taiex-prediction-chart')], style={'margin-top': '20px'})
     ], style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','padding': '25px','border-radius': '15px','box-shadow': '0 8px 25px rgba(0,0,0,0.15)','color': 'white','margin-bottom': '40px'}),
     
-    # ... (後續 layout 省略，與您原檔案相同)
     html.Div([
         html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
         html.Div([
@@ -445,9 +450,6 @@ app.layout = html.Div([
     ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
 ])
 
-
-# (所有 Callback 函式保持不變，除了 update_taiex_prediction 內部的邏輯已透過 get_prediction 更新)
-# ... (此處省略所有未修改的 Callback 函式) ...
 @app.callback(
     [dash.dependencies.Output('taiex-prediction-results', 'children'),
      dash.dependencies.Output('taiex-prediction-chart', 'figure')],
@@ -460,7 +462,7 @@ def update_taiex_prediction(predict_days):
     # === 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("資料不足或模型無法預測此天期"), {}
+    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']
     
@@ -475,6 +477,7 @@ def update_taiex_prediction(predict_days):
             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'}),
@@ -489,7 +492,6 @@ def update_taiex_prediction(predict_days):
     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
 
-# ... (其他所有 callback 函式都無需修改)
 @app.callback(
     dash.dependencies.Output('stock-info-cards', 'children'),
     [dash.dependencies.Input('stock-dropdown', 'value')]
@@ -516,7 +518,229 @@ def update_stock_info(selected_stock):
             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'})
     ])
-# ... 繼續貼上您剩餘的所有 callback functions ...
+
+@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='價格 (TWD)'), xaxis2=dict(title='成交量', showticklabels=True), yaxis2=dict(showticklabels=False), bargap=0.05)
+    return fig
+
+@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':
+        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
+
+@app.callback(
+    dash.dependencies.Output('volume-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value'),
+     dash.dependencies.Input('period-dropdown', 'value')]
+)
+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
+
+@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
+
+@app.callback(
+    dash.dependencies.Output('business-climate-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
+)
+def update_business_climate_chart(selected_stock):
+    df = get_business_climate_data()
+    if df.empty:
+        fig = go.Figure().add_annotation(text="無法載入景氣燈號資料", showarrow=False)
+        fig.update_layout(title="台灣景氣燈號", height=300)
+        return fig
+    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
+
+@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')]
+)
+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 "處於超賣區(<30)" if rsi_current < 30 else "在正常範圍內", style={'color': 'green' if rsi_current > 70 else 'red' if rsi_current < 30 else 'blue', 'font-weight': 'bold'}), "。"]),
+        html.P([html.Strong("MACD 指標："), f"MACD 快線 ({macd_current:.3f}) 目前", html.Span("高於" if macd_current > macd_signal_current else "低於", style={'color': 'red' if macd_current > macd_signal_current else 'green', 'font-weight': 'bold'}), f" Signal 慢線 ({macd_signal_current:.3f})，", f"顯示市場動能偏向{'���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):
+    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()
+    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。
@@ -546,4 +770,94 @@ def summarize_news_with_gemini(news_list: list) -> str:
 
     except Exception as e:
         print(f"呼叫 Gemini API 時發生錯誤: {e}")
-        return f"無法
+        return f"無法生成新聞摘要，請稍後再試。錯誤訊息：{e}"
+
+@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')]
+)
+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")
+
+@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。")
+
+    sentiment_score_raw = predictor.get_news_index()
+
+    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)
+    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)
+
+    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'})
+    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
+
+# 主程式執行
+if __name__ == '__main__':
+    app.run(host="0.0.0.0", port=7860, debug=False)