Update app.py

app.py

@@ -1,4 +1,4 @@
-# HUGING_FACE_V2.1.3.py (整合 Bert_predict 版本)
+# HUGING_FACE_V3.1.2.py (整合 Bert_predict 版本)
 
 # 系統套件
 import os
@@ -477,61 +477,53 @@ def update_volume_chart(selected_stock, period):
     fig.update_layout(title=f'{stock_name} 成交量', xaxis_title='日期', yaxis_title='成交量', height=300)
     return fig
 
-# 更新產業分析圖表 (修改版本 - 自動抓取波動標的)
+# ========================= MODIFIED SECTION START =========================
 @app.callback(
     dash.dependencies.Output('industry-analysis', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')] # 這個輸入參數在您原程式碼中，但新邏輯中用不到
+    [dash.dependencies.Input('stock-dropdown', 'value')] # Callback can be triggered by any change, e.g., the main stock selection
 )
 def update_industry_analysis(selected_stock):
-    # 1. 獲取所有股票的近3個月歷史數據，並計算波動性
-    all_stocks_volatility = []
-    # 這裡的 TAIWAN_STOCKS.values() 包含了您所有的股票代碼
-    for symbol in TAIWAN_STOCKS.values():
-        # 這裡獲取3個月的數據來計算波動性，確保數據量足夠
-        data = get_stock_data(symbol, '3mo')
-        if not data.empty and len(data) >= 60: # 確保至少有3個月左右的交易日數據 (約60天)
-            # 計算收盤價的標準差，標準差越大，代表波動性越高
-            volatility = data['Close'].std()
-            stock_name = [name for name, symbol_code in TAIWAN_STOCKS.items() if symbol_code == symbol][0]
-            all_stocks_volatility.append({'股票': stock_name, '代碼': symbol, '波動性': volatility})
-    
-    # 如果沒有任何股票數據，則直接返回空圖表
-    if not all_stocks_volatility:
-        fig = go.Figure().add_annotation(text="無法獲取任何股票數據", showarrow=False)
-        fig.update_layout(title="產業表現分析")
-        return fig
-    
-    # 2. 根據波動性從高到低排序，並選出前10名
-    df_volatility = pd.DataFrame(all_stocks_volatility).sort_values(by='波動性', ascending=False)
-    top_10_volatile_stocks = df_volatility.head(10)['代碼'].tolist()
-    
-    # 3. 針對這前10名股票，重新獲取1個月的數據來計算月報酬率
-    industry_data = []
-    for symbol in top_10_volatile_stocks:
+    performance_data = []
+    # 1. Iterate through ALL stocks to calculate their performance
+    for name, symbol in TAIWAN_STOCKS.items():
         data = get_stock_data(symbol, '1mo')
-        if not data.empty and len(data) > 1: # 確保有足夠的數據來計算報酬率
-            stock_name = [name for name, symbol_code in TAIWAN_STOCKS.items() if symbol_code == symbol][0]
-            # 計算月報酬率
+        if not data.empty and len(data) > 1:
+            # Calculate 1-month return percentage
             return_pct = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
-            industry_data.append({'股票': stock_name, '代碼': symbol, '月報酬率(%)': return_pct, '產業': INDUSTRY_MAPPING.get(symbol, '其他')})
-    
-    # 如果篩選後沒有任何數據，也返回空圖表
-    if not industry_data:
-        fig = go.Figure().add_annotation(text="無法計算波動較大股票的月報酬率", showarrow=False)
-        fig.update_layout(title="產業表現分析")
+            performance_data.append({
+                '股票': name,
+                '代碼': symbol,
+                '月報酬率(%)': return_pct,
+                '絕對波動': abs(return_pct) # Use absolute value for sorting
+            })
+
+    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
-    
-    # 4. 繪製圓餅圖
-    df_industry = pd.DataFrame(industry_data)
-    fig = px.pie(df_industry,
-                 values='月報酬率(%)',
-                 names='股票',
-                 title='月波動較大股票之報酬率比較',
-                 color_discrete_sequence=px.colors.qualitative.Set3)
-    
-    fig.update_layout(height=400)
-    
+
+    # 2. Sort by the absolute fluctuation and take the top 10
+    df_performance = pd.DataFrame(performance_data)
+    df_top_movers = df_performance.sort_values(by='絕對波動', ascending=False).head(10)
+
+    # 3. Create the pie chart with the top 10 movers
+    # We use the absolute value for the pie chart size to represent volatility,
+    # but the hover data will show the actual (positive/negative) return.
+    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'} # Show the actual return on hover
+    )
+    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
+# ========================== MODIFIED SECTION END ==========================
 
 # 更新景氣燈號圖表
 @app.callback(