Update app.py

app.py

@@ -256,7 +256,6 @@ 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},
@@ -592,1480 +591,17 @@ def update_taiex_prediction(predict_days):
         line=dict(color='#FFA726', width=2)
     ))
 
-     # --- 關鍵修正從這裡開始 ---
-    # 定義所有要顯示的預測天數點
-    all_predict_days = [1, 5, 10, 20, 60]
-    
-    # 過濾出所有小於或等於使用者選擇的預測天數
-    points_to_show = [d for d in all_predict_days if d <= predict_days]
-
-    # 為每個要顯示的預測點創建圖表軌跡
-    for d in points_to_show:
-        # 重新計算每個點的預測值
-        point_prediction = simple_lstm_predict(data, d)
-        if point_prediction:
-            point_predicted_price = point_prediction['predicted_price']
-            point_future_date = recent_data.index[-1] + timedelta(days=d)
-            
-            # 決定點的顏色
-            point_color = '#00C851' if point_predicted_price >= current_price else '#FF4444'
-
-            # 添加預測點
-            fig.add_trace(go.Scatter(
-                x=[point_future_date],
-                y=[point_predicted_price],
-                mode='markers',
-                name=f'{d}日預測點',
-                marker=dict(size=10, color=point_color)
-            ))
-
-    # 為使用者選擇的最終天數添加趨勢線
-    final_future_date = recent_data.index[-1] + timedelta(days=predict_days)
-    fig.add_trace(go.Scatter(
-        x=[recent_data.index[-1], final_future_date],
-        y=[current_price, predicted_price],
-        mode='lines',
-        name='最終預測線',
-        line=dict(color=color, width=3, dash='dash')
-    ))
-
-    # --- 修正結束 ---
-
-    fig.update_layout(
-        title=f'台指期 {predict_days}日預測走勢',
-        xaxis_title='日期',
-        yaxis_title='指數點位',
-        height=350,
-        plot_bgcolor='rgba(0,0,0,0)',
-        paper_bgcolor='rgba(0,0,0,0)',
-        font=dict(color='white')
-    )
-
-    return result_card, fig
-
-# 更新股價資訊卡片
-@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
-
-    # 找出股票中文名稱
-    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-
-    color = 'green' if change >= 0 else 'red'
-
-    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"{'▲' if change >= 0 else '▼'} {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'
-        })
-    ])
-
-# 更新股價圖表
-@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]
-
-    if chart_type == 'candlestick':
-        fig = go.Figure(data=go.Candlestick(
-            x=data.index,
-            open=data['Open'],
-            high=data['High'],
-            low=data['Low'],
-            close=data['Close'],
-            name=stock_name
-        ))
-    else:
-        fig = px.line(data, y='Close', title=f'{stock_name} 股價走勢')
-
-    # 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')))
-    fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], mode='lines', name='MA20', line=dict(color='blue')))
-
-    fig.update_layout(
-        title=f'{stock_name} 股價走勢',
-        xaxis_title='日期',
-        yaxis_title='價格 (TWD)',
-        height=400
-    )
-
-    return fig
-
-# 更新RSI圖表（保持兼容性）
-@app.callback(
-    dash.dependencies.Output('rsi-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value'),
-     dash.dependencies.Input('period-dropdown', 'value')]
-)
-def update_rsi_chart(selected_stock, period):
-    data = get_stock_data(selected_stock, period)
-    if data.empty:
-        return {}
-
-    data = calculate_technical_indicators(data)
-
-    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="red", annotation_text="超買線(70)")
-    fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="超賣線(30)")
-    fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)")
-
-    # 添加超買超賣區域背景
-    fig.add_hrect(y0=70, y1=100, fillcolor="red", opacity=0.1, annotation_text="超買區")
-    fig.add_hrect(y0=0, y1=30, fillcolor="green", opacity=0.1, annotation_text="超賣區")
-
-    fig.update_layout(
-        title='RSI 相對強弱指標',
-        xaxis_title='日期',
-        yaxis_title='RSI',
-        height=400,
-        yaxis=dict(range=[0, 100])
-    )
-
-    return fig
-
-# 新增：進階技術指標圖表
-@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]
-
-    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="red", annotation_text="超買線(70)")
-        fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="超賣線(30)")
-        fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)")
-
-        fig.add_hrect(y0=70, y1=100, fillcolor="red", opacity=0.1)
-        fig.add_hrect(y0=0, y1=30, fillcolor="green", opacity=0.1)
-
-        fig.update_layout(
-            title=f'{stock_name} - RSI 相對強弱指標',
-            xaxis_title='日期',
-            yaxis_title='RSI',
-            height=450,
-            yaxis=dict(range=[0, 100])
-        )
-
-    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=('價格與MACD線', 'MACD柱狀圖'))
-
-        # 上方：價格線
-        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)
-
-        # MACD線和信號線
-        fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], mode='lines', name='MACD',
-                                line=dict(color='blue', width=2)), row=1, col=1)
-        fig.add_trace(go.Scatter(x=data.index, y=data['MACD_Signal'], mode='lines', name='信號線',
-                                line=dict(color='red', width=2)), row=1, col=1)
-
-        # 下方：MACD柱狀圖
-        colors = ['green' if x >= 0 else 'red' for x in data['MACD_Histogram']]
-        fig.add_trace(go.Bar(x=data.index, y=data['MACD_Histogram'], name='MACD柱狀圖',
-                            marker_color=colors), row=2, col=1)
-
-        fig.add_hline(y=0, line_dash="dash", line_color="gray", row=1, col=1)
-        fig.add_hline(y=0, line_dash="dash", line_color="gray", row=2, col=1)
-
-        fig.update_layout(
-            title=f'{stock_name} - MACD 指數平滑異同移動平均線',
-            height=500
-        )
-
-    elif indicator == 'BB':
-        fig = go.Figure()
-
-        # 價格線
-        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價',
-                                line=dict(color='black', width=2)))
-
-        # 布林通道上���
-        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')))
-
-        # 布林通道中軌
-        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Middle'], mode='lines', name='中軌(MA20)',
-                                line=dict(color='blue', width=1)))
-
-        # 布林通道下軌
-        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')))
-
-        # 填充通道區域
-        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], mode='lines',
-                                line=dict(color='rgba(0,0,0,0)'), showlegend=False))
-        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], mode='lines',
-                                fill='tonexty', fillcolor='rgba(173,216,230,0.2)',
-                                line=dict(color='rgba(0,0,0,0)'), name='布林通道', showlegend=False))
-
-        fig.update_layout(
-            title=f'{stock_name} - 布林通道 (20日, 2σ)',
-            xaxis_title='日期',
-            yaxis_title='價格 (TWD)',
-            height=450
-        )
-
-    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'))
-
-        # 上方：價格線
-        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)
-
-        # 下方：KD線
-        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)
-
-        # KD指標參考線
-        fig.add_hline(y=80, line_dash="dash", line_color="red", annotation_text="超買線(80)", row=2, col=1)
-        fig.add_hline(y=20, line_dash="dash", line_color="green", annotation_text="超賣線(20)", row=2, col=1)
-        fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)", row=2, col=1)
-
-        # 超買超賣區域
-        fig.add_hrect(y0=80, y1=100, fillcolor="red", opacity=0.1, row=2, col=1)
-        fig.add_hrect(y0=0, y1=20, fillcolor="green", opacity=0.1, row=2, col=1)
-
-        fig.update_layout(
-            title=f'{stock_name} - KD 隨機指標 (9,3,3)',
-            height=500
-        )
-        fig.update_yaxes(range=[0, 100], row=2, col=1)
-
-    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'))
-
-        # 上方：價格線
-        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)
-
-        # 下方：威廉指標
-        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)
-
-        # 威廉指標參考線
-        fig.add_hline(y=-20, line_dash="dash", line_color="red", annotation_text="超買線(-20)", row=2, col=1)
-        fig.add_hline(y=-80, line_dash="dash", line_color="green", annotation_text="超賣線(-80)", row=2, col=1)
-        fig.add_hline(y=-50, line_dash="dot", line_color="gray", annotation_text="中線(-50)", row=2, col=1)
-
-        # 超買超賣區域
-        fig.add_hrect(y0=-20, y1=0, fillcolor="red", opacity=0.1, row=2, col=1)
-        fig.add_hrect(y0=-100, y1=-80, fillcolor="green", opacity=0.1, row=2, col=1)
-
-        fig.update_layout(
-            title=f'{stock_name} - 威廉指標 %R (14日)',
-            height=500
-        )
-        fig.update_yaxes(range=[-100, 0], row=2, col=1)
-
-    return fig
-
-# 更新成交量圖表
-@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]
-
-    fig = px.bar(data, y='Volume', title=f'{stock_name} 成交量')
-    fig.update_layout(
-        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):
-    # 獲取多檔股票資料進行產業比較
-    industry_data = []
-
-    for symbol in list(TAIWAN_STOCKS.values())[:10]:  # 取前10檔做示範
-        data = get_stock_data(symbol, '1mo')
-        if not data.empty:
-            stock_name = [name for name, symbol_code in TAIWAN_STOCKS.items() if symbol_code == symbol][0]
-            latest_price = data['Close'].iloc[-1]
-            first_price = data['Close'].iloc[0]
-            return_pct = ((latest_price - first_price) / first_price) * 100
-
-            industry_data.append({
-                '股票': stock_name,
-                '代碼': symbol,
-                '月報酬率(%)': return_pct,
-                '產業': INDUSTRY_MAPPING.get(symbol, '其他')
-            })
-
-    if not industry_data:
-        return {}
-
-    df_industry = pd.DataFrame(industry_data)
-
-    # 建立產業表現圓餅圖
-    fig = px.pie(df_industry, values='月報酬率(%)', names='股票',
-                title='各股票月報酬率比較',
-                color_discrete_sequence=px.colors.qualitative.Set3)
-
-    fig.update_layout(height=400)
-    return fig
-
-# 新增：更新景氣燈號圖表
-@app.callback(
-    dash.dependencies.Output('business-climate-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]  # 雖然不會影響圖表，但需要觸發
-)
-def update_business_climate_chart(selected_stock):
-    df = get_business_climate_data()
-
-    if df.empty:
-        # 如果沒有資料，顯示提示圖表
-        fig = go.Figure()
-        fig.add_annotation(
-            x=0.5, y=0.5,
-            text="無法載入景氣燈號資料<br>請確認 business_climate.csv 檔案是否存在",
-            xref="paper", yref="paper",
-            showarrow=False,
-            font=dict(size=14)
-        )
-        fig.update_layout(
-            title="台灣景氣燈號",
-            height=300,
-            showlegend=False
-        )
-        return fig
-
-    # 定義燈號顏色
-    def get_light_color(score):
-        if score >= 32:
-            return 'red'      # 紅燈
-        elif score >= 24:
-            return 'orange'   # 黃紅燈
-        elif score >= 17:
-            return 'yellow'   # 黃燈
-        elif score >= 10:
-            return 'lightgreen'  # 黃藍燈
-        else:
-            return 'blue'     # 藍燈
-
-    # 為每個點設定顏色
-    colors = [get_light_color(score) for score in df['Index']]
-
-    fig = go.Figure()
-
-    fig.add_trace(go.Scatter(
-        x=df['Date'],
-        y=df['Index'],
-        mode='lines+markers',
-        name='景氣燈號',
-        line=dict(color='darkblue', width=2),
-        marker=dict(
-            size=8,
-            color=colors,
-            line=dict(width=2, color='darkblue')
-        )
-    ))
-
-    # 添加燈號區間線
-    fig.add_hline(y=32, line_dash="dash", line_color="red", annotation_text="紅燈(32)")
-    fig.add_hline(y=24, line_dash="dash", line_color="orange", annotation_text="黃紅燈(24)")
-    fig.add_hline(y=17, line_dash="dash", line_color="yellow", annotation_text="黃燈(17)")
-    fig.add_hline(y=10, line_dash="dash", line_color="lightgreen", annotation_text="黃藍燈(10)")
-
-    fig.update_layout(
-        title="台灣景氣燈號走勢",
-        xaxis_title='日期',
-        yaxis_title='燈號分數',
-        height=300,
-        yaxis=dict(range=[0, 40])
-    )
-
-    return fig
-
-# 新增：更新分析師觀點
-@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):
-    # 獲取股票資料進行分析
-    data = get_stock_data(selected_stock, period)
-    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-
-    if data.empty:
-        return "無法獲取資料進行分析", "無法獲取資料進行分析", "無法獲取資料進行分析"
-
-    # 計算技術指標
-    data = calculate_technical_indicators(data)
-
-    # 基本數據
-    current_price = data['Close'].iloc[-1]
-    price_change = ((current_price - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
-    volume_avg = data['Volume'].mean()
-    recent_volume = data['Volume'].iloc[-5:].mean()
-    rsi_current = data['RSI'].iloc[-1] if not pd.isna(data['RSI'].iloc[-1]) else 50
-
-    # 新增技術指標數據
-    macd_current = data['MACD'].iloc[-1] if not pd.isna(data['MACD'].iloc[-1]) else 0
-    macd_signal_current = data['MACD_Signal'].iloc[-1] if not pd.isna(data['MACD_Signal'].iloc[-1]) else 0
-    bb_position = data['BB_Position'].iloc[-1] if not pd.isna(data['BB_Position'].iloc[-1]) else 0.5
-    k_current = data['K'].iloc[-1] if not pd.isna(data['K'].iloc[-1]) else 50
-    d_current = data['D'].iloc[-1] if not pd.isna(data['D'].iloc[-1]) else 50
-
-    # 技術面分析
-    technical_text = html.Div([
-        html.P([
-            html.Strong("價格趨勢："),
-            f"近期{period}期間內，{stock_name}呈現",
-            html.Span(f"{'上漲' if price_change > 5 else '下跌' if price_change < -5 else '盤整'}",
-                     style={'color': 'green' if price_change > 5 else 'red' 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': 'red' if rsi_current > 70 else 'green' 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': 'green' if macd_current > macd_signal_current else 'red', 'font-weight': 'bold'}
-            ),
-            f"信號線({macd_signal_current:.3f})，",
-            f"顯示{'多頭' if macd_current > macd_signal_current else '空頭'}格局。"
-        ]),
-        html.P([
-            html.Strong("布林通道："),
-            f"股價位於通道",
-            html.Span(
-                "上半部" if bb_position > 0.8 else "下半部" if bb_position < 0.2 else "中段",
-                style={'color': 'red' if bb_position > 0.8 else 'green' if bb_position < 0.2 else 'blue', 'font-weight': 'bold'}
-            ),
-            f"({bb_position*100:.0f}%)，",
-            f"{'壓力較大' if bb_position > 0.8 else '支撐較強' if bb_position < 0.2 else '整理格局'}。"
-        ]),
-        html.P([
-            html.Strong("KD指標："),
-            f"K值({k_current:.1f})",
-            html.Span(
-                "高於" if k_current > d_current else "低於",
-                style={'color': 'green' if k_current > d_current else 'red', 'font-weight': 'bold'}
-            ),
-            f"D值({d_current:.1f})，",
-            html.Span(
-                "超買警戒" if k_current > 80 else "超賣關注" if k_current < 20 else "正常區間",
-                style={'color': 'red' if k_current > 80 else 'green' if k_current < 20 else 'blue', 'font-weight': 'bold'}
-            ),
-            "。"
-        ]),
-        html.P([
-            html.Strong("成交量分析："),
-            f"近期成交量{'放大' if recent_volume > volume_avg * 1.2 else '萎縮' if recent_volume < volume_avg * 0.8 else '平穩'}，",
-            f"顯示市場{'關注度提升' if recent_volume > volume_avg * 1.2 else '觀望氣氛濃厚' if recent_volume < volume_avg * 0.8 else '交投正常'}。"
-        ])
-    ])
-
-    # 基本面分析
-    industry = INDUSTRY_MAPPING.get(selected_stock, '綜合')
-    fundamental_text = html.Div([
-        html.P([
-            html.Strong("產業地位："),
-            f"{stock_name}屬於{industry}產業，在產業鏈中具有",
-            html.Span("重要地位" if selected_stock in ['2330.TW', '2454.TW', '2317.TW'] else "一定影響力",
-                     style={'font-weight': 'bold'}),
-            "。"
-        ]),
-        html.P([
-            html.Strong("營運展望："),
-            f"考量{industry}產業前景及公司基本面，建議持續關注季報表現及未來指引。"
-        ]),
-        html.P([
-            html.Strong("風險評估："),
-            "注意產業週期性變化、國際競爭及法規環境變化等風險因子。"
-        ])
-    ])
-
-    # 市場展望
-    if price_change > 10:
-        outlook_tone = "謹慎樂觀"
-        outlook_color = "#28a745"
-    elif price_change < -10:
-        outlook_tone = "保守觀望"
-        outlook_color = "#dc3545"
-    else:
-        outlook_tone = "中性持平"
-        outlook_color = "#ffc107"
-
-    market_outlook = html.Div([
-        html.P([
-            html.Strong("整體評估：", style={'font-size': '16px'}),
-            f"基於技術面及基本面分析，對{stock_name}採取",
-            html.Span(f"{outlook_tone}", style={'color': outlook_color, 'font-weight': 'bold', 'font-size': '16px'}),
-            "態度。"
-        ]),
-        html.P([
-            html.Strong("投資建議："),
-            "建議投資人根據自身風險承受能力，採取適當的資產配置策略。短線操作注意技術指標，長線投資關注基本面變化。"
-        ]),
-        html.P([
-            html.Strong("風險提醒："),
-            "股票投資具有風險，過去績效不代表未來表現，投資前請詳閱公開說明書並審慎評估。"
-        ], style={'font-style': 'italic', 'font-size': '13px'})
-    ])
-
-    return technical_text, fundamental_text, market_outlook
-
-# 新增：更新PMI圖表
-@app.callback(
-    dash.dependencies.Output('pmi-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]  # 雖然不會影響圖表，但需要觸發
-)
-def update_pmi_chart(selected_stock):
-    df = get_pmi_data()
-
-    if df.empty:
-        # 如果沒有資料，顯示提示圖表
-        fig = go.Figure()
-        fig.add_annotation(
-            x=0.5, y=0.5,
-            text="無法載入PMI資料<br>請確認 taiwan_pmi.csv 檔案是否存在",
-            xref="paper", yref="paper",
-            showarrow=False,
-            font=dict(size=14)
-        )
-        fig.update_layout(
-            title="台灣PMI指數",
-            height=300,
-            showlegend=False
-        )
-        return fig
-
-    # 定義PMI顏色 (50以上擴張，以下緊縮)
-    def get_pmi_color(value):
-        return 'green' if value >= 50 else 'red'
-
-    colors = [get_pmi_color(value) for value in df['Index']]
-
-    fig = go.Figure()
-
-    fig.add_trace(go.Scatter(
-        x=df['Date'],
-        y=df['Index'],
-        mode='lines+markers',
-        name='PMI指數',
-        line=dict(color='darkblue', width=2),
-        marker=dict(
-            size=8,
-            color=colors,
-            line=dict(width=2, color='darkblue')
-        )
-    ))
-
-    # 添加榮枯線
-    fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線(50)")
-
-    # 添加背景色區域
-    fig.add_hrect(
-        y0=50, y1=60,
-        fillcolor="lightgreen", opacity=0.2,
-        annotation_text="擴張區間", annotation_position="top left"
-    )
-    fig.add_hrect(
-        y0=40, y1=50,
-        fillcolor="lightcoral", opacity=0.2,
-        annotation_text="緊縮區間", annotation_position="bottom left"
-    )
-
-    fig.update_layout(
-        title="台灣PMI指數走勢",
-        xaxis_title='日期',
-        yaxis_title='PMI指數',
-        height=300,
-        yaxis=dict(range=[35, 60])
-    )
-
-    return fig
-
-# 新增：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')]
-)
-def update_comparison_analysis(selected_stocks, period):
-    if not selected_stocks:
-        return {}, html.Div("請選擇要比較的股票")
-
-    # 限制最多5檔
-    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 = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == stock][0]
-
-            # 正規化價格（以期初為基準100）
-            normalized_prices = (data['Close'] / data['Close'].iloc[0]) * 100
-
-            fig.add_trace(go.Scatter(
-                x=data.index,
-                y=normalized_prices,
-                mode='lines',
-                name=stock_name,
-                line=dict(width=2)
-            ))
-
-            # 計算績效數據
-            total_return = ((data['Close'].iloc[-1] / data['Close'].iloc[0]) - 1) * 100
-            volatility = data['Close'].pct_change().std() * np.sqrt(252) * 100  # 年化波動率
-
-            comparison_data.append({
-                'name': stock_name,
-                'return': total_return,
-                'volatility': volatility,
-                'current_price': data['Close'].iloc[-1]
-            })
-
-    fig.update_layout(
-        title=f'股票績效比較 - {period}',
-        xaxis_title='日期',
-        yaxis_title='相對績效 (基期=100)',
-        height=400,
-        hovermode='x unified'
-    )
-
-    # 建立比較表格
-    if comparison_data:
-        table_rows = []
-        for item in sorted(comparison_data, key=lambda x: x['return'], reverse=True):
-            color = 'green' if item['return'] > 0 else 'red'
-            table_rows.append(
-                html.Tr([
-                    html.Td(item['name'], style={'font-weight': 'bold'}),
-                    html.Td(f"{item['return']:+.1f}%", style={'color': color, 'font-weight': 'bold'}),
-                    html.Td(f"{item['volatility']:.1f}%"),
-                    html.Td(f"${item['current_price']:.2f}")
-                ])
-            )
-
-        table = html.Table([
-            html.Thead([
-                html.Tr([
-                    html.Th("股票", style={'text-align': 'center'}),
-                    html.Th("報酬率", style={'text-align': 'center'}),
-                    html.Th("波動率", style={'text-align': 'center'}),
-                    html.Th("現價", style={'text-align': 'center'})
-                ])
-            ]),
-            html.Tbody(table_rows)
-        ], style={
-            'width': '100%',
-            'border-collapse': 'collapse',
-            'font-size': '12px'
-        })
-
-        return fig, table
-
-    return fig, html.Div("無可比較資料")
-
-# 新增：市場情緒分析
-@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):
-    # 模擬情緒指標（實際應用中可接入新聞API或情緒分析服務）
-    sentiment_score = np.random.uniform(30, 80)  # 模擬情緒分數 0-100
-
-    # 建立情緒指標圓形圖
-    gauge_fig = go.Figure(go.Indicator(
-        mode = "gauge+number+delta",
-        value = sentiment_score,
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        title = {'text': "市場情緒指數"},
-        delta = {'reference': 50},
-        gauge = {
-            'axis': {'range': [None, 100]},
-            'bar': {'color': "darkblue"},
-            'steps': [
-                {'range': [0, 30], 'color': "lightcoral"},
-                {'range': [30, 70], 'color': "lightgray"},
-                {'range': [70, 100], 'color': "lightgreen"}
-            ],
-            'threshold': {
-                'line': {'color': "red", 'width': 4},
-                'thickness': 0.75,
-                'value': 90
-            }
-        }
-    ))
-
-    gauge_fig.update_layout(height=200, margin=dict(l=20, r=20, t=40, b=20))
-
-    # 模擬新聞摘要
-    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-
-    news_items = [
-        f"📈 {stock_name}獲外資調升目標價，看好後續發展前景",
-        f"💼 法人預期{stock_name}下季營收將較上季成長5-10%",
-        f"🌐 國際市場波動對{stock_name}影響有限，基本面穩健",
-        f"⚡ 產業景氣回溫，{stock_name}受惠程度值得關注",
-        f"📊 技術面顯示{stock_name}突破關鍵壓力，短線偏多"
-    ]
-
-    news_content = html.Div([
-        html.P(news, style={
-            'margin': '8px 0',
-            'padding': '8px',
-            'background': '#e8f4f8',
-            'border-radius': '5px',
-            'border-left': '3px solid #17a2b8',
-            'font-size': '13px'
-        }) for news in news_items[:3]  # 顯示前3條
-    ])
-
-    return dcc.Graph(figure=gauge_fig), news_content
-
-# 在 Colab 中執行的設定
-if __name__ == '__main__':
-    # 在執行前先測試檔案讀取
-    print("測試檔案讀取...")
-    business_data = get_business_climate_data()
-    pmi_data = get_pmi_data()
-
-    if not business_data.empty:
-        print(f"景氣燈號資料預覽：\n{business_data.head()}")
-    if not pmi_data.empty:
-        print(f"PMI資料預覽：\n{pmi_data.head()}")
-
-    # 在 Hugging Face Spaces 中執行
-    app.run(host="0.0.0.0", port=7860, debug=False)            if data.empty:
-                # 最後嘗試使用加權指數
-                stock = yf.Ticker('^TWII')
-                data = stock.history(period=period)
-
-        return data
-    except:
-        return pd.DataFrame()
-
-def create_lstm_dataset(data, time_step=60):
-    """建立LSTM訓練資料集"""
-    X, y = [], []
-    for i in range(time_step, len(data)):
-        X.append(data[i-time_step:i, 0])
-        y.append(data[i, 0])
-    return np.array(X), np.array(y)
-
-def simple_lstm_predict(data, predict_days=5):
-    """簡化的LSTM預測模型 (使用統計方法模擬)"""
-    if len(data) < 60:
-        return None
-
-    # 使用移動平均和趨勢分析來模擬深度學習預測
-    prices = data['Close'].values
-
-    # 計算短期和長期移動平均
-    ma_short = np.mean(prices[-5:])
-    ma_medium = np.mean(prices[-20:])
-    ma_long = np.mean(prices[-60:])
-
-    # 計算價格變化趨勢
-    recent_trend = np.polyfit(range(20), prices[-20:], 1)[0]
-    volatility = np.std(prices[-20:]) / np.mean(prices[-20:])
-
-    # 模擬預測邏輯
-    base_change = recent_trend * predict_days
-    trend_factor = 1.0
-
-    if ma_short > ma_medium > ma_long:
-        trend_factor = 1.02  # 上升趨勢
-    elif ma_short < ma_medium < ma_long:
-        trend_factor = 0.98  # 下降趨勢
-    else:
-        trend_factor = 1.0   # 盤整
-
-    # 加入隨機性模擬市場不確定性
-    noise_factor = np.random.normal(1, volatility * 0.1)
-
-    predicted_price = prices[-1] * trend_factor + base_change + (prices[-1] * noise_factor * 0.01)
-    change_pct = ((predicted_price - prices[-1]) / prices[-1]) * 100
-
-    return {
-        'predicted_price': predicted_price,
-        'change_pct': change_pct,
-        'confidence': max(0.6, 1 - volatility * 2)  # 基於波動率的信心度
-    }
-
-def calculate_technical_indicators(df):
-    """計算技術指標"""
-    if df.empty:
-        return df
-
-    # 移動平均線
-    df['MA5'] = df['Close'].rolling(window=5).mean()
-    df['MA20'] = df['Close'].rolling(window=20).mean()
-
-    # RSI
-    delta = df['Close'].diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
-    rs = gain / loss
-    df['RSI'] = 100 - (100 / (1 + rs))
-
-    # MACD (12, 26, 9)
-    exp1 = df['Close'].ewm(span=12).mean()
-    exp2 = df['Close'].ewm(span=26).mean()
-    df['MACD'] = exp1 - exp2
-    df['MACD_Signal'] = df['MACD'].ewm(span=9).mean()
-    df['MACD_Histogram'] = df['MACD'] - df['MACD_Signal']
-
-    # 布林通道 (20日, 2倍標準差)
-    df['BB_Middle'] = df['Close'].rolling(window=20).mean()
-    bb_std = df['Close'].rolling(window=20).std()
-    df['BB_Upper'] = df['BB_Middle'] + (bb_std * 2)
-    df['BB_Lower'] = df['BB_Middle'] - (bb_std * 2)
-    df['BB_Width'] = df['BB_Upper'] - df['BB_Lower']
-    df['BB_Position'] = (df['Close'] - df['BB_Lower']) / (df['BB_Upper'] - df['BB_Lower'])
-
-    # KD指標 (9, 3, 3)
-    low_min = df['Low'].rolling(window=9).min()
-    high_max = df['High'].rolling(window=9).max()
-    rsv = (df['Close'] - low_min) / (high_max - low_min) * 100
-    df['K'] = rsv.ewm(com=2).mean()  # com=2 相當於 span=3
-    df['D'] = df['K'].ewm(com=2).mean()
-
-    # 威廉指標 %R (14日)
-    low_min_14 = df['Low'].rolling(window=14).min()
-    high_max_14 = df['High'].rolling(window=14).max()
-    df['Williams_R'] = -100 * (high_max_14 - df['Close']) / (high_max_14 - low_min_14)
-
-    return df
-
-def get_business_climate_data():
-    """獲取台灣景氣燈號資料"""
-    try:
-        # 檢查檔案是否存在
-        if not os.path.exists('business_climate.csv'):
-            print("business_climate.csv 檔案不存在")
-            return pd.DataFrame()
-
-        # 讀取CSV檔案，假設列名為 Date 和 Index
-        df = pd.read_csv('business_climate.csv')
-
-        # 檢查列名並調整
-        if 'Date' not in df.columns:
-            # 如果第一列是日期，重新命名
-            df.columns = ['Date', 'Index'] if len(df.columns) == 2 else df.columns
-
-        # 轉換日期格式 (處理 YYYY-MM 格式)
-        if 'Date' in df.columns:
-            try:
-                # 如果是 YYYY-MM 格式，轉換為日期
-                df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
-            except:
-                df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
-
-        # 移除日期轉換失敗的行
-        df = df.dropna(subset=['Date'])
-
-        print(f"成功讀取景氣燈號資料：{len(df)} 筆記錄")
-        return df
-
-    except Exception as e:
-        print(f"無法獲取景氣燈號資料: {str(e)}")
-        return pd.DataFrame()
-
-def get_pmi_data():
-    """獲取台灣 PMI 資料"""
-    try:
-        # 檢查檔案是否存在
-        if not os.path.exists('taiwan_pmi.csv'):
-            print("taiwan_pmi.csv 檔案不存在")
-            return pd.DataFrame()
-
-        # 讀取CSV檔案
-        df = pd.read_csv('taiwan_pmi.csv')
-
-        # 檢查列名並調整 (處理 DATE/INDEX 或其他可能的列名)
-        if 'DATE' in df.columns:
-            df = df.rename(columns={'DATE': 'Date', 'INDEX': 'Index'})
-        elif len(df.columns) == 2:
-            df.columns = ['Date', 'Index']
-
-        # 轉換日期格式
-        if 'Date' in df.columns:
-            try:
-                # 如果是 YYYY-MM 格式，轉換為日期
-                df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
-            except:
-                df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
-
-        # 移除日期轉換失敗的行
-        df = df.dropna(subset=['Date'])
-
-        print(f"成功讀取 PMI 資料：{len(df)} 筆記錄")
-        return df
-
-    except Exception as e:
-        print(f"無法獲取 PMI 資料: {str(e)}")
-        return pd.DataFrame()
-
-# 建立 Dash 應用程式
-app = dash.Dash(__name__, suppress_callback_exceptions=True)
-
-# 應用程式佈局
-app.layout = html.Div([
-    html.H1("台股分析儀表板", style={'text-align': 'center', 'margin-bottom': '30px'}),
-
-    # 台指期獨立預測區塊 - 置於頂部
-    html.Div([
-        html.H2("🤖 AI深度學習預測 - 台指期指數", style={
-            'text-align': 'center', 
-            'color': '#FFCC22',
-            'margin-bottom': '25px'
-        }),
-        html.Div([
-            html.Div([
-                html.Label("預測期間:", style={'font-weight': 'bold', 'color': '#FFCC22'}),
-                dcc.Dropdown(
-                    id='taiex-prediction-period',
-                    options=[
-                        {'label': '1日後預測', 'value': 1},
-                        {'label': '5日後預測', 'value': 5},
-                        {'label': '10日後預測', 'value': 10},
-                        {'label': '20日後預測', 'value': 20},
-                        {'label': '60日後預測', 'value': 60}
-                    ],
-                    value=5,
-                    style={'margin-bottom': '10px', 'color': '#272727'}
-                )
-            ], style={'width': '30%', 'display': 'inline-block'}),
-
-            html.Div(id='taiex-prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
-        ]),
-
-        html.Div([
-            dcc.Graph(id='taiex-prediction-chart')
-        ], style={'margin-top': '20px'})
-    ], style={
-        'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
-        'padding': '25px',
-        'border-radius': '15px',
-        'box-shadow': '0 8px 25px rgba(0,0,0,0.15)',
-        'color': 'white',
-        'margin-bottom': '40px'
-    }),
-
-    # 控制面板 (移除台指期選項)
-    html.Div([
-        html.Div([
-            html.Label("選擇股票:"),
-            dcc.Dropdown(
-                id='stock-dropdown',
-                options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()],
-                value='2330.TW',  # 預設改為台積電
-                style={'margin-bottom': '10px'}
-            )
-        ], style={'width': '30%', 'display': 'inline-block', 'vertical-align': 'top'}),
-
-        html.Div([
-            html.Label("時間範圍:"),
-            dcc.Dropdown(
-                id='period-dropdown',
-                options=[
-                    {'label': '1個月', 'value': '1mo'},
-                    {'label': '3個月', 'value': '3mo'},
-                    {'label': '6個月', 'value': '6mo'},
-                    {'label': '1年', 'value': '1y'},
-                    {'label': '2年', 'value': '2y'}
-                ],
-                value='6mo',
-                style={'margin-bottom': '10px'}
-            )
-        ], 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'}),
-
-    # 股價資訊卡片
-    html.Div(id='stock-info-cards', style={'margin-bottom': '30px'}),
-
-    # 主要圖表區域
-    html.Div([
-        # 左側：股價走勢圖和技術指標
-        html.Div([
-            html.Div([
-                dcc.Graph(id='price-chart')
-            ], style={'margin-bottom': '20px'}),
-
-            html.Div([
-                dcc.Graph(id='rsi-chart')
-            ])
-        ], style={'width': '65%', 'display': 'inline-block', 'vertical-align': 'top'}),
-
-        # 右側：分析資訊面板
-        html.Div([
-            html.Div(id='analysis-panel')
-        ], style={'width': '33%', 'display': 'inline-block', 'margin-left': '2%', 'vertical-align': 'top'})
-    ]),
-
-    # 技術指標選擇區域
-    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'}
-                ],
-                value='RSI',
-                style={'width': '100%'}
-            )
-        ], style={'margin-bottom': '20px'}),
-
-        html.Div([
-            dcc.Graph(id='advanced-technical-chart')
-        ])
-    ], style={
-        'margin-top': '20px',
-        'padding': '20px',
-        'background': 'white',
-        'border-radius': '10px',
-        'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'
-    }),
-
-    # 成交量圖
-    html.Div([
-        dcc.Graph(id='volume-chart')
-    ], style={'margin-top': '20px'}),
-
-    # 產業分析
-    html.Div([
-        html.H3("產業表現分析"),
-        dcc.Graph(id='industry-analysis')
-    ], style={'margin-top': '30px'}),
-
-    # 分析師觀點區域
-    html.Div([
-        html.H3("📊 分析師觀點與市場解讀", style={'color': '#2E86AB', 'margin-bottom': '20px'}),
-        html.Div([
-            # 左側：技術分析觀點
-            html.Div([
-                html.H4("🔍 技術面分析", style={'color': '#A23B72', 'margin-bottom': '15px'}),
-                html.Div(id='technical-analysis-text', style={
-                    'background': '#f8f9fa',
-                    'padding': '15px',
-                    'border-radius': '8px',
-                    'border-left': '4px solid #A23B72',
-                    'min-height': '150px',
-                    'font-size': '14px',
-                    'line-height': '1.6'
-                })
-            ], style={'width': '48%', 'display': 'inline-block', 'vertical-align': 'top'}),
-
-            # 右側：基本面分析觀點
-            html.Div([
-                html.H4("📈 基本面分析", style={'color': '#F18F01', 'margin-bottom': '15px'}),
-                html.Div(id='fundamental-analysis-text', style={
-                    'background': '#f8f9fa',
-                    'padding': '15px',
-                    'border-radius': '8px',
-                    'border-left': '4px solid #F18F01',
-                    'min-height': '150px',
-                    'font-size': '14px',
-                    'line-height': '1.6'
-                })
-            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%', 'vertical-align': 'top'})
-        ]),
-
-        # 底部：市場展望
-        html.Div([
-            html.H4("🎯 市場展望與投資建議", style={'color': '#C73E1D', 'margin-bottom': '15px', 'margin-top': '25px'}),
-            html.Div(id='market-outlook-text', style={
-                'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
-                'color': 'white',
-                'padding': '20px',
-                'border-radius': '10px',
-                'min-height': '100px',
-                'font-size': '15px',
-                'line-height': '1.7',
-                'box-shadow': '0 4px 15px rgba(0,0,0,0.1)'
-            })
-        ])
-    ], 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'
-    }),
-
-    # 景氣燈號與 PMI 分析
-    html.Div([
-        html.H3("景氣燈號與 PMI 分析"),
-        html.Div([
-            html.Div([
-                dcc.Graph(id='business-climate-chart')
-            ], style={'width': '48%', 'display': 'inline-block'}),
-            html.Div([
-                dcc.Graph(id='pmi-chart')
-            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '2%'})
-        ])
-    ], style={'margin-top': '30px'}),
-
-    # 多檔股票比較區域
-    html.Div([
-        html.H3("📊 多檔股票比較分析", style={'margin-bottom': '20px'}),
-        html.Div([
-            html.Div([
-                html.Label("選擇比較股票（最多5檔）:", style={'font-weight': 'bold'}),
-                dcc.Dropdown(
-                    id='comparison-stocks',
-                    options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()],
-                    value=['2330.TW', '2454.TW', '2317.TW'],  # 預設選擇
-                    multi=True,
-                    style={'margin-bottom': '15px'}
-                )
-            ], 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%'})
-        ]),
-
-        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)'
-    }),
-
-    # 新聞情感分析區域（模擬）
-    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)'
-    })
-])
-# 創建一個列表來儲存所有的預測點數據
-all_predictions_data = []
-# 台指期獨立預測回調函數
-@app.callback(
-    [dash.dependencies.Output('taiex-prediction-results', 'children'),
-     dash.dependencies.Output('taiex-prediction-chart', 'figure')],
-    [dash.dependencies.Input('taiex-prediction-period', 'value')]
-)
-def update_taiex_prediction(predict_days):
-    # 獲取台指期歷史資料
-    data = get_stock_data('^TWII', '2y')
-    if data.empty:
-        return html.Div("無法獲取台指期資料"), {}
-
-# 獲取最後的歷史數據點
-    last_historical_date = data.index[-1]
-    current_price = data['Close'].iloc[-1]
-
-    # --- 關鍵修改開始 ---
-
-    # 1. 收集所有需要的預測點
-    all_predict_options = [1, 5, 10, 20, 60]
-    
-    # 篩選出使用者選擇天數或更短的所有預測選項
-    points_to_calculate = [d for d in all_predict_options if d <= predict_days]
-
-    # 清空舊的預測數據，重新計算
-    all_predictions_data.clear()
-
-    # 添加歷史的最後一個點作為起始點
-    all_predictions_data.append({
-        'date': last_historical_date,
-        'price': current_price,
-        'days': 0, # 0天代表歷史數據
-        'is_historical': True
-    })
-
-    # 計算並儲存每個預測點
-    for d in points_to_calculate:
-        prediction = simple_lstm_predict(data, d)
-        if prediction:
-            future_date = last_historical_date + timedelta(days=d)
-            all_predictions_data.append({
-                'date': future_date,
-                'price': prediction['predicted_price'],
-                'days': d,
-                'is_historical': False,
-                'change_pct': prediction['change_pct'] # 為了顏色判斷
-            })
-
-    # 將預測點數據轉換為 DataFrame，方便繪圖
-    predictions_df = pd.DataFrame(all_predictions_data)
-    predictions_df = predictions_df.sort_values(by='date') # 確保日期排序正確
-
-    # 找到最後一個預測點的顏色
-    final_prediction_info = predictions_df[predictions_df['days'] == predict_days].iloc[0] if predict_days in predictions_df['days'].values else None
-    final_color = '#00C851' if final_prediction_info and final_prediction_info['change_pct'] >= 0 else '#FF4444'
-    arrow = '📈' if final_color == '#00C851' else '📉'
-
-    # 建立預測趨勢圖
-    fig = go.Figure()
-
-    # 添加歷史價格線 (最近30天)
-    recent_data = data.tail(30)
-    fig.add_trace(go.Scatter(
-        x=recent_data.index,
-        y=recent_data['Close'],
-        mode='lines',
-        name='歷史價格',
-        line=dict(color='#FFA726', width=2)
-    ))
-
-    # 添加連接所有預測點的趨勢線
-    fig.add_trace(go.Scatter(
-        x=predictions_df['date'],
-        y=predictions_df['price'],
-        mode='lines+markers', # 同時顯示線和點
-        name='預測趨勢',
-        line=dict(color=final_color, width=3, dash='dash'), # 使用最終預測的顏色
-        marker=dict(size=8)
-    ))
-
-    # 為了區分不同天數的點，我們可以再添加一次點，但這次只添加 marker
-    # 這樣可以為每個點設置不同的顏色和大小（如果需要）
-    for index, row in predictions_df.iterrows():
-        if not row['is_historical']:
-            point_color = '#00C851' if row['change_pct'] >= 0 else '#FF4444'
-            fig.add_trace(go.Scatter(
-                x=[row['date']],
-                y=[row['price']],
-                mode='markers',
-                name=f'{row["days"]}日預測點',
-                marker=dict(size=10, color=point_color, line=dict(width=2, color='DarkSlateGrey')), # 稍微加點邊框
-                legendgroup=f'points', # 將所有點分到同一個圖例組
-                showlegend=True if row['days'] in [1, 5, 10, 20, 60] else False # 只顯示主要天數的點在圖例
-            ))
-
-    # --- 關鍵修改結束 ---
-
-    # 預測結果卡片
-    color = '#00C851' if change_pct >= 0 else '#FF4444'
-    arrow = '📈' if change_pct >= 0 else '📉'
-
-    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)'
-    })
-
-    # 建立預測趨勢圖
-    fig = go.Figure()
-
-    # 歷史價格 (最近30天)
-    recent_data = data.tail(30)
-    fig.add_trace(go.Scatter(
-        x=recent_data.index,
-        y=recent_data['Close'],
-        mode='lines',
-        name='歷史價格',
-        line=dict(color='#FFA726', width=2)
-    ))
-
-     # --- 關鍵修正從這裡開始 ---
-    # 定義所有要顯示的預測天數點
-    all_predict_days = [1, 5, 10, 20, 60]
-    
-    # 過濾出所有小於或等於使用者選擇的預測天數
-    points_to_show = [d for d in all_predict_days if d <= predict_days]
-
-    # 為每個要顯示的預測點創建圖表軌跡
-    for d in points_to_show:
-        # 重新計算每個點的預測值
-        point_prediction = simple_lstm_predict(data, d)
-        if point_prediction:
-            point_predicted_price = point_prediction['predicted_price']
-            point_future_date = recent_data.index[-1] + timedelta(days=d)
-            
-            # 決定點的顏色
-            point_color = '#00C851' if point_predicted_price >= current_price else '#FF4444'
-
-            # 添加預測點
-            fig.add_trace(go.Scatter(
-                x=[point_future_date],
-                y=[point_predicted_price],
-                mode='markers',
-                name=f'{d}日預測點',
-                marker=dict(size=10, color=point_color)
-            ))
-
-    # 為使用者選擇的最終天數添加趨勢線
-    final_future_date = recent_data.index[-1] + timedelta(days=predict_days)
+    # 預測點
+    future_date = recent_data.index[-1] + timedelta(days=predict_days)
     fig.add_trace(go.Scatter(
-        x=[recent_data.index[-1], final_future_date],
+        x=[recent_data.index[-1], future_date],
         y=[current_price, predicted_price],
-        mode='lines',
-        name='最終預測線',
-        line=dict(color=color, width=3, dash='dash')
+        mode='lines+markers',
+        name=f'{predict_days}日預測',
+        line=dict(color=color, width=3, dash='dash'),
+        marker=dict(size=8)
     ))
 
-    # --- 修正結束 ---
-
     fig.update_layout(
         title=f'台指期 {predict_days}日預測走勢',
         xaxis_title='日期',
@@ -2859,4 +1395,4 @@ if __name__ == '__main__':
         print(f"PMI資料預覽：\n{pmi_data.head()}")
 
     # 在 Hugging Face Spaces 中執行
-    app.run(host="0.0.0.0", port=7860, debug=TRUE)
+    app.run(host="0.0.0.0", port=7860, debug=False)