Update app.py

app.py

@@ -6,7 +6,6 @@ from datetime import datetime, timedelta
 import pandas as pd
 import numpy as np
 import yfinance as yf
-import pandas_ta as ta # 新增：技術分析套件
 
 # Dash & Plotly
 from dash import Dash, dcc, html, callback
@@ -15,9 +14,9 @@ import plotly.express as px
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
-# 台股代號對應表
+# 台股代號對應表 (移除台指期，因為它現在是獨立區塊)
 TAIWAN_STOCKS = {
-    '元大台灣50': '0050.TW',
+    '元大台灣50': '0050.TW', # 新增
     '台積電': '2330.TW',
     '聯發科': '2454.TW',
     '鴻海': '2317.TW',
@@ -32,6 +31,8 @@ TAIWAN_STOCKS = {
     '慧洋-KY': '2637.TW',
     '上銀': '2049.TW',
     '台泥': '1101.TW',
+    '南亞科': '2408.TW',
+    '旺宏': '2337.TW',
     '譜瑞-KY': '4966.TWO',
     '貿聯-KY': '3665.TW',
     '騰雲': '6870.TWO',
@@ -40,7 +41,7 @@ TAIWAN_STOCKS = {
 
 # 產業分類
 INDUSTRY_MAPPING = {
-    '0050.TW': 'ETF',
+    '0050.TW': 'ETF', # 新增
     '2330.TW': '半導體',
     '2454.TW': '半導體',
     '2317.TW': '電子組件',
@@ -55,6 +56,9 @@ INDUSTRY_MAPPING = {
     '2637.TW': '散裝航運',
     '2049.TW': '工具機',
     '1101.TW': '營建',
+    '2408.TW': 'DRAM',
+    '2337.TW': 'NFLSH',
+    '1101.TW': '營建',
     '4966.TWO': '高速傳輸',
     '3665.TW': '連接器',
     '6870.TWO': '軟體整合',
@@ -172,10 +176,6 @@ def calculate_technical_indicators(df):
     high_max_14 = df['High'].rolling(window=14).max()
     df['Williams_R'] = -100 * (high_max_14 - df['Close']) / (high_max_14 - low_min_14)
 
-    # 新增：DMI 指標 (使用 pandas_ta)
-    # 這會自動新增 'DMP_14', 'DMN_14', 'ADX_14' 欄位
-    df.ta.dmi(append=True)
-
     return df
 
 def get_business_climate_data():
@@ -401,8 +401,7 @@ app.layout = html.Div([
                     {'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'} # 新增
+                    {'label': '威廉指標 %R', 'value': 'WR'}
                 ],
                 value='RSI',
                 style={'width': '100%'}
@@ -500,7 +499,9 @@ app.layout = html.Div([
         ])
     ], style={'margin-top': '30px'}),
 
-    # 多檔股票比較區域
+    # ==============================================================================
+    # ===== 修改後的多檔股票比較區域 =====
+    # ==============================================================================
     html.Div([
         html.H3("📊 多檔股票比較分析", style={'margin-bottom': '20px'}),
         html.Div([
@@ -509,10 +510,11 @@ app.layout = html.Div([
                 dcc.Dropdown(
                     id='comparison-stocks',
                     options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()],
-                    value=['0050.TW', '2330.TW', '2454.TW'],
+                    value=['0050.TW', '2330.TW', '2454.TW'],  # 修改：預設包含0050
                     multi=True,
-                    style={'margin-bottom': '5px'}
+                    style={'margin-bottom': '5px'} # 調整間距
                 ),
+                # 新增：提示文字
                 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'}
@@ -740,7 +742,9 @@ def update_stock_info(selected_stock):
         })
     ])
 
-# 主要圖表回呼函式 (合併股價與成交量分佈)
+# ==============================================================================
+# ===== 修改後的主要圖表回呼函式 (合併股價與成交量分佈) =====
+# ==============================================================================
 @app.callback(
     dash.dependencies.Output('price-chart', 'figure'),
     [dash.dependencies.Input('stock-dropdown', 'value'),
@@ -756,15 +760,17 @@ def update_price_chart(selected_stock, period, chart_type):
     stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
 
     # --- 1. 建立共享 Y 軸的子圖 ---
+    # 建立一個 1x2 的網格，設定欄位寬度比例，並共享 Y 軸
     fig = make_subplots(
         rows=1, cols=2,
         shared_yaxes=True,
-        column_widths=[0.8, 0.2],
-        horizontal_spacing=0.01
+        column_widths=[0.8, 0.2],  # 左側圖佔80%，右側圖佔20%
+        horizontal_spacing=0.01    # 子圖間的水平間距
     )
 
     # --- 2. 在左側子圖 (col=1) 繪製股價圖 ---
     if chart_type == 'candlestick':
+        # 根據台股慣例修改顏色
         fig.add_trace(go.Candlestick(
             x=data.index,
             open=data['Open'],
@@ -772,12 +778,13 @@ def update_price_chart(selected_stock, period, chart_type):
             low=data['Low'],
             close=data['Close'],
             name=stock_name,
-            increasing_line_color='red',
-            decreasing_line_color='green'
+            increasing_line_color='red',  # 上漲為紅色
+            decreasing_line_color='green' # 下跌為綠色
         ), row=1, col=1)
     else:
         fig.add_trace(px.line(data, y='Close').data[0], row=1, col=1)
 
+    # 添加移動平均線到左側子圖
     fig.add_trace(go.Scatter(
         x=data.index, y=data['MA5'], mode='lines',
         name='MA5', line=dict(color='orange')
@@ -788,15 +795,17 @@ def update_price_chart(selected_stock, period, chart_type):
     ), row=1, col=1)
 
     # --- 3. 在右側子圖 (col=2) 繪製成交量分佈圖 ---
+    # 計算 Volume Profile 數據
     bin_edges, volume_per_bin, price_centers = calculate_volume_profile(data, num_bins=50)
 
     if volume_per_bin is not None:
+        # 繪製水平長條圖
         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],
+            text=[f'{vol/1000:.0f}k' for vol in volume_per_bin], # 顯示成交量
             textposition='auto',
             marker=dict(
                 color='rgba(173, 216, 230, 0.6)',
@@ -809,28 +818,33 @@ def update_price_chart(selected_stock, period, chart_type):
         title_text=f'{stock_name} 股價走勢與成交量分佈',
         height=500,
         showlegend=True,
+
+        # 左側子圖的座標軸設定
         xaxis1=dict(
             title='日期',
             type='date',
-            rangeslider_visible=False
+            rangeslider_visible=False # 隱藏範圍滑桿，避免干擾佈局
         ),
         yaxis1=dict(
             title='價格 (TWD)'
         ),
+
+        # 右側子圖的座標軸設定
         xaxis2=dict(
             title='成交量',
-            showticklabels=True
+            showticklabels=True # 顯示刻度
         ),
         yaxis2=dict(
-            showticklabels=False
+            showticklabels=False # 因為共享Y軸，所以隱藏右側的Y軸標籤
         ),
-        bargap=0.05
+
+        bargap=0.05 # 長條圖間的間隙
     )
 
     return fig
 
 
-# 進階技術指標圖表
+# 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'),
@@ -848,11 +862,15 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
     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)))
+        # 根據台股慣例修改顏色
         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)")
+
+        # 根據台股慣例修改顏色
         fig.add_hrect(y0=70, y1=100, fillcolor="green", opacity=0.1)
         fig.add_hrect(y0=0, y1=30, fillcolor="red", opacity=0.1)
+
         fig.update_layout(
             title=f'{stock_name} - RSI 相對強弱指標',
             xaxis_title='日期',
@@ -862,10 +880,13 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
         )
 
     elif indicator == 'MACD':
+        # 建立兩個垂直排列的子圖，並共享X軸
         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'))
+                           vertical_spacing=0.1,  # 子圖間的垂直間距
+                           row_heights=[0.7, 0.3], # 上方圖佔70%，下方圖佔30%
+                           subplot_titles=('價格走勢', 'MACD 指標')) # 設定子圖標題
+
+        # --- 上方子圖 (row=1)：只繪製價格走勢 ---
         fig.add_trace(go.Scatter(
             x=data.index,
             y=data['Close'],
@@ -873,6 +894,9 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
             name='收盤價',
             line=dict(color='black', width=1.5)
         ), row=1, col=1)
+
+        # --- 下方子圖 (row=2)：繪製所有MACD相關指標 ---
+        # 1. MACD 快線 (DIF)
         fig.add_trace(go.Scatter(
             x=data.index,
             y=data['MACD'],
@@ -880,6 +904,8 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
             name='MACD (快線)',
             line=dict(color='blue', width=2)
         ), row=2, col=1)
+
+        # 2. Signal 慢線 (MACD)
         fig.add_trace(go.Scatter(
             x=data.index,
             y=data['MACD_Signal'],
@@ -887,6 +913,9 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
             name='Signal (慢線)',
             line=dict(color='red', width=2)
         ), row=2, col=1)
+
+        # 3. Histogram 柱狀圖
+        # 根據台股慣例修改顏色
         colors = ['red' if x >= 0 else 'green' for x in data['MACD_Histogram']]
         fig.add_trace(go.Bar(
             x=data.index,
@@ -894,30 +923,46 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
             name='MACD柱狀圖',
             marker_color=colors
         ), row=2, col=1)
+
+        # 在MACD子圖中添加一條零軸水平線，方便觀察
         fig.add_hline(y=0, line_dash="dash", line_color="gray", row=2, col=1)
+
+        # 更新整個圖表的佈局
         fig.update_layout(
             title_text=f'{stock_name} - MACD 指數平滑異同移動平均線',
-            height=550,
+            height=550, # 可以適當增加圖表高度以容納兩個子圖
             legend_title_text='圖例',
-            showlegend=True
+            showlegend=True # 確保圖例顯示
         )
+        # 隱藏柱狀圖的圖例，因為顏色已經表達了正負值
         fig.update_traces(showlegend=False, selector=dict(type='bar'))
 
     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)))
+
+        # 布林通道上軌
         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',
@@ -930,17 +975,28 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
                            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
+        # 根據台股慣例修改顏色
         fig.add_hline(y=80, line_dash="dash", line_color="green", annotation_text="超買線(80)", row=2, col=1)
         fig.add_hline(y=20, line_dash="dash", line_color="red", annotation_text="超賣線(20)", row=2, col=1)
         fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)", row=2, col=1)
+
+        # 超買超賣區域
+        # 根據台股慣例修改顏色
         fig.add_hrect(y0=80, y1=100, fillcolor="green", opacity=0.1, row=2, col=1)
         fig.add_hrect(y0=0, y1=20, fillcolor="red", opacity=0.1, row=2, col=1)
+
         fig.update_layout(
             title=f'{stock_name} - KD 隨機指標 (9,3,3)',
             height=500
@@ -952,56 +1008,32 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
                            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
+        # 根據台股慣例修改顏色
         fig.add_hline(y=-20, line_dash="dash", line_color="green", annotation_text="超買線(-20)", row=2, col=1)
         fig.add_hline(y=-80, line_dash="dash", line_color="red", annotation_text="超賣線(-80)", row=2, col=1)
         fig.add_hline(y=-50, line_dash="dot", line_color="gray", annotation_text="中線(-50)", row=2, col=1)
+
+        # 超買超賣區域
+        # 根據台股慣例修改顏色
         fig.add_hrect(y0=-20, y1=0, fillcolor="green", opacity=0.1, row=2, col=1)
         fig.add_hrect(y0=-100, y1=-80, fillcolor="red", 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)
 
-    # ==============================================================================
-    # ===== 新增 DMI 圖表繪製邏輯 =====
-    # ==============================================================================
-    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 動向指標 (14日)'))
-
-        # 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：DMI 指標線
-        # +DI (pandas-ta 欄位為 DMP_14)
-        fig.add_trace(go.Scatter(x=data.index, y=data['DMP_14'], mode='lines', name='+DI (上升趨向)',
-                                line=dict(color='green', width=2)), row=2, col=1)
-        # -DI (pandas-ta 欄位為 DMN_14)
-        fig.add_trace(go.Scatter(x=data.index, y=data['DMN_14'], mode='lines', name='-DI (下降趨向)',
-                                line=dict(color='red', width=2)), row=2, col=1)
-        # ADX (pandas-ta 欄位為 ADX_14)
-        fig.add_trace(go.Scatter(x=data.index, y=data['ADX_14'], mode='lines', name='ADX (趨勢強度)',
-                                line=dict(color='blue', width=1.5, dash='dash')), row=2, col=1)
-
-        # ADX 趨勢強度參考線
-        fig.add_hline(y=25, line_dash="dot", line_color="gray",
-                      annotation_text="趨勢增強線(25)", row=2, col=1)
-        
-        fig.update_layout(
-            title=f'{stock_name} - DMI 動向指標',
-            height=500
-        )
-        fig.update_yaxes(title_text="指標值", row=2, col=1)
-
-
     return fig
 
 # 更新成交量圖表
@@ -1077,12 +1109,13 @@ def update_industry_analysis(selected_stock):
 # 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')]
+    [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,
@@ -1098,16 +1131,24 @@ def update_business_climate_chart(selected_stock):
         )
         return fig
 
+    # 定義燈號顏色
     def get_light_color(score):
-        if score >= 32: return 'red'
-        elif score >= 24: return 'orange'
-        elif score >= 17: return 'yellow'
-        elif score >= 10: return 'lightgreen'
-        else: return 'blue'
-
+        if score >= 32:
+            return 'red'      # 紅燈
+        elif score >= 24:
+            return 'orange'   # 黃紅燈
+        elif score >= 17:
+            return 'yellow'   # 黃燈
+        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'],
@@ -1120,10 +1161,13 @@ def update_business_climate_chart(selected_stock):
             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',
@@ -1131,6 +1175,7 @@ def update_business_climate_chart(selected_stock):
         height=300,
         yaxis=dict(range=[0, 40])
     )
+
     return fig
 
 # 新增：更新分析師觀點
@@ -1142,23 +1187,32 @@ def update_business_climate_chart(selected_stock):
      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
+    # 根據台股慣例修改顏色
     technical_text = html.Div([
         html.P([
             html.Strong("價格趨勢："),
@@ -1173,7 +1227,8 @@ def update_analysis_text(selected_stock, period):
             html.Span(
                 "處於超買區間" if rsi_current > 70 else "處於超賣區間" if rsi_current < 30 else "在正常範圍內",
                 style={'color': 'green' if rsi_current > 70 else 'red' if rsi_current < 30 else 'blue', 'font-weight': 'bold'}
-            ), "。"
+            ),
+            "。"
         ]),
         html.P([
             html.Strong("MACD指標："),
@@ -1181,7 +1236,9 @@ def update_analysis_text(selected_stock, period):
             html.Span(
                 "高於" if macd_current > macd_signal_current else "低於",
                 style={'color': 'red' if macd_current > macd_signal_current else 'green', 'font-weight': 'bold'}
-            ), f"信號線({macd_signal_current:.3f})，", f"顯示{'多頭' if macd_current > macd_signal_current else '空頭'}格局。"
+            ),
+            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："),
@@ -1189,7 +1246,9 @@ def update_analysis_text(selected_stock, period):
             html.Span(
                 "上半部" if bb_position > 0.8 else "下半部" if bb_position < 0.2 else "中段",
                 style={'color': 'green' if bb_position > 0.8 else 'red' 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'}。"
+            ),
+            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："),
@@ -1197,11 +1256,13 @@ def update_analysis_text(selected_stock, period):
             html.Span(
                 "高於" if k_current > d_current else "低於",
                 style={'color': 'red' if k_current > d_current else 'green', 'font-weight': 'bold'}
-            ), f"D值({d_current:.1f})，",
+            ),
+            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': 'green' if k_current > 80 else 'red' if k_current < 20 else 'blue', 'font-weight': 'bold'}
-            ), "。"
+            ),
+            "。"
         ]),
         html.P([
             html.Strong("成交量分析："),
@@ -1210,13 +1271,15 @@ def update_analysis_text(selected_stock, period):
         ])
     ])
 
+    # 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'}), "。"
+                     style={'font-weight': 'bold'}),
+            "。"
         ]),
         html.P([
             html.Strong("營運展望："),
@@ -1228,18 +1291,24 @@ def update_analysis_text(selected_stock, period):
         ])
     ])
 
+    # 市場展望
+    # 根據台股慣例修改顏色
     if price_change > 10:
-        outlook_tone = "謹慎樂觀"; outlook_color = "#dc3545"
+        outlook_tone = "謹慎樂觀"
+        outlook_color = "#dc3545"
     elif price_change < -10:
-        outlook_tone = "保守觀望"; outlook_color = "#28a745"
+        outlook_tone = "保守觀望"
+        outlook_color = "#28a745"
     else:
-        outlook_tone = "中性持平"; outlook_color = "#ffc107"
+        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.Span(f"{outlook_tone}", style={'color': outlook_color, 'font-weight': 'bold', 'font-size': '16px'}),
+            "態度。"
         ]),
         html.P([
             html.Strong("投資建議："),
@@ -1256,11 +1325,13 @@ def update_analysis_text(selected_stock, period):
 # 新增：更新PMI圖表
 @app.callback(
     dash.dependencies.Output('pmi-chart', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]
+    [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,
@@ -1276,26 +1347,44 @@ def update_pmi_chart(selected_stock):
         )
         return fig
 
+    # 定義PMI顏色 (50以上擴張，以下緊縮)
+    # 根據台股慣例修改顏色
     def get_pmi_color(value):
         return 'red' if value >= 50 else 'green'
 
     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',
+        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'))
+        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
+    # 根據台股慣例修改顏色
     fig.add_hrect(
-        y0=50, y1=60, fillcolor="lightcoral", opacity=0.2,
+        y0=50, y1=60,
+        fillcolor="lightcoral", opacity=0.2,
         annotation_text="擴張區間", annotation_position="top left"
     )
     fig.add_hrect(
-        y0=40, y1=50, fillcolor="lightgreen", opacity=0.2,
+        y0=40, y1=50,
+        fillcolor="lightgreen", 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',
@@ -1303,9 +1392,13 @@ def update_pmi_chart(selected_stock):
         height=300,
         yaxis=dict(range=[35, 60])
     )
+
     return fig
 
-# 多檔股票比較回呼函式
+
+# ==============================================================================
+# ===== 修改後的多檔股票比較回呼函式 =====
+# ==============================================================================
 @app.callback(
     [dash.dependencies.Output('comparison-chart', 'figure'),
      dash.dependencies.Output('comparison-table', 'children')],
@@ -1313,12 +1406,20 @@ def update_pmi_chart(selected_stock):
      dash.dependencies.Input('comparison-period', 'value')]
 )
 def update_comparison_analysis(selected_stocks, period):
+    # --- 新增：確保 0050.TW 始終存在 ---
     fixed_stock = '0050.TW'
+    # 如果列表為空或 None，則只顯示 0050
     if not selected_stocks:
         selected_stocks = [fixed_stock]
+    # 如果 0050 不在列表中，則將其插入到最前面
     elif fixed_stock not in selected_stocks:
         selected_stocks.insert(0, fixed_stock)
+    # --- 修改結束 ---
+
+    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()
@@ -1327,8 +1428,12 @@ def update_comparison_analysis(selected_stocks, period):
     for stock in selected_stocks:
         data = get_stock_data(stock, period)
         if not data.empty:
+            # 安全地獲取股票名稱，如果找不到則使用代碼本身
             stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == stock), stock)
+
+            # 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,
@@ -1336,8 +1441,11 @@ def update_comparison_analysis(selected_stocks, period):
                 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
+            volatility = data['Close'].pct_change().std() * np.sqrt(252) * 100  # 年化波動率
+
             comparison_data.append({
                 'name': stock_name,
                 'return': total_return,
@@ -1353,9 +1461,11 @@ def update_comparison_analysis(selected_stocks, period):
         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):
+            # 根據台股慣例修改顏色
             color = 'red' if item['return'] > 0 else 'green'
             table_rows.append(
                 html.Tr([
@@ -1365,27 +1475,39 @@ def update_comparison_analysis(selected_stocks, period):
                     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.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'})
+        ], style={
+            'width': '100%',
+            'border-collapse': 'collapse',
+            'font-size': '12px'
+        })
+
         return fig, table
 
     return fig, html.Div("無可比較資料")
 
-# 市場情緒分析
+# 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):
-    sentiment_score = np.random.uniform(30, 80)
+    # 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
+    # 根據台股慣例修改顏色
     gauge_fig = go.Figure(go.Indicator(
         mode = "gauge+number+delta",
         value = sentiment_score,
@@ -1407,9 +1529,12 @@ def update_sentiment_analysis(selected_stock):
             }
         }
     ))
+
     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%",
@@ -1417,17 +1542,23 @@ def update_sentiment_analysis(selected_stock):
         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': '#f8f9fa',
-            'border-radius': '5px', 'border-left': '3px solid #17a2b8', 'font-size': '13px'
-        }) for news in news_items[:3]
+            'margin': '8px 0',
+            'padding': '8px',
+            'background': '#f8f9fa',
+            '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
 
-# 主程式執行
+# 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()
@@ -1437,4 +1568,5 @@ if __name__ == '__main__':
     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)