Update src/streamlit_app.py

src/streamlit_app.py

@@ -1473,7 +1473,8 @@ st.markdown("""
 """, unsafe_allow_html=True)
 
 # ------ 功能函數 ------ #
-
+def get_stock_data(stock_ids, period="1y", interval="1d"):
+    '''
     參數:
     stock_ids (list): 股票代碼列表
     period (str): 時間週期
@@ -1481,9 +1482,9 @@ st.markdown("""
     
     返回:
     dict: 股票數據字典
-    """
+    '''
     stock_data = {}
-    
+
     with st.spinner("正在獲取股票數據..."):
         progress_bar = st.progress(0)
         for i, stock_id in enumerate(stock_ids):
@@ -1517,7 +1518,7 @@ st.markdown("""
             time.sleep(0.5)  # 為了視覺效果添加的短暫延遲
         
         progress_bar.empty()
-    
+
     return stock_data
 
     rs = gain / loss
@@ -1526,24 +1527,24 @@ st.markdown("""
 
     # 累積回報率
     cumulative_return = (data.iloc[-1] / data.iloc[0] - 1) * 100
-    
+
     # 年化回報率 (假設252個交易日)
     days = len(data)
     annualized_return = ((1 + cumulative_return / 100) ** (252 / days) - 1) * 100
-    
+
     # 波動率 (年化標準差)
     volatility = daily_returns.std() * np.sqrt(252) * 100
-    
+
     # 夏普比率 (假設無風險利率為2%)
     risk_free_rate = 0.02
     sharpe_ratio = (annualized_return / 100 - risk_free_rate) / (volatility / 100)
-    
+
     # 最大回撤
     cum_returns = (1 + daily_returns).cumprod()
     running_max = cum_returns.cummax()
     drawdown = (cum_returns / running_max - 1) * 100
     max_drawdown = drawdown.min()
-    
+
     return {
         'cumulative_return': cumulative_return,
         'annualized_return': annualized_return,
@@ -1554,7 +1555,7 @@ st.markdown("""
 
     # 創建列來顯示指標
     cols = st.columns(len(stock_data))
-    
+
     for i, (stock_id, data) in enumerate(stock_data.items()):
         hist = data['history']
         if hist.empty:
@@ -1606,13 +1607,13 @@ st.markdown("""
                         vertical_spacing=0.1,
                         subplot_titles=("股價走勢比較", "正規化股價比較 (基準=100)"),
                         row_heights=[0.6, 0.4])
-    
+
     colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
     color_idx = 0
-    
+
     # 用於正規化的數據
     normalized_data = {}
-    
+
     # 添加每個股票的數據到圖表
     for stock_id, data in stock_data.items():
         hist = data['history']
@@ -1654,7 +1655,7 @@ st.markdown("""
             ),
             row=2, col=1
         )
-    
+
     # 更新布局
     fig.update_layout(
         height=700,
@@ -1670,24 +1671,24 @@ st.markdown("""
         hovermode="x unified",
         plot_bgcolor='rgba(240,242,246,0.8)',
     )
-    
+
     # 更新Y軸標題
     fig.update_yaxes(title_text="價格 (TWD)", row=1, col=1, gridcolor='rgba(220,220,220,0.5)')
     fig.update_yaxes(title_text="正規化價格 (基準=100)", row=2, col=1, gridcolor='rgba(220,220,220,0.5)')
     fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
-    
+
     # 顯示圖表
     st.plotly_chart(fig, use_container_width=True)
 
     # 創建圖表
     fig = go.Figure()
-    
+
     colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
     color_idx = 0
-    
+
     # 添加每個股票的成交量到圖表
     has_volume_data = False
-    
+
     for stock_id, data in stock_data.items():
         hist = data['history']
         # 確保數據不為空且有成交量數據
@@ -1713,11 +1714,11 @@ st.markdown("""
                 opacity=0.7
             )
         )
-    
+
     if not has_volume_data:
         st.warning("沒有可用的成交量數據")
         return
-    
+
     # 更新布局
     fig.update_layout(
         title="股票成交量比較",
@@ -1736,10 +1737,10 @@ st.markdown("""
         margin=dict(l=40, r=40, t=60, b=40),
         plot_bgcolor='rgba(240,242,246,0.8)',
     )
-    
+
     fig.update_yaxes(title_text="成交量", gridcolor='rgba(220,220,220,0.5)')
     fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
-    
+
     # 顯示圖表
     st.plotly_chart(fig, use_container_width=True)
 
@@ -1747,7 +1748,7 @@ st.markdown("""
     if hist.empty:
         st.warning("所選股票沒有足夠的歷史數據")
         return
-    
+
     # RSI 指標
     if indicator_type == "RSI":
         rsi = calculate_rsi(hist['Close'])
@@ -1809,7 +1810,7 @@ st.markdown("""
         fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
         
         st.plotly_chart(fig, use_container_width=True)
-    
+
     # MACD 指標
     elif indicator_type == "MACD":
         macd, signal, histogram = calculate_macd(hist['Close'])
@@ -1888,7 +1889,7 @@ st.markdown("""
         fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
         
         st.plotly_chart(fig, use_container_width=True)
-    
+
     # 布林帶
     elif indicator_type == "布林帶":
         ma, upper, lower = calculate_bollinger_bands(hist['Close'])
@@ -1972,7 +1973,7 @@ st.markdown("""
         fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
         
         st.plotly_chart(fig, use_container_width=True)
-    
+
     # 移動��均線
     elif indicator_type == "移動平均線":
         ma20, ma50, ma100, ma200 = calculate_moving_averages(hist['Close'])
@@ -2059,17 +2060,17 @@ st.markdown("""
         if not hist.empty:
             metrics = calculate_performance_metrics(hist['Close'])
             performance_metrics[stock_id] = metrics
-    
+
     if not performance_metrics:
         st.warning("沒有足夠的數據來計算績效指標")
         return
-    
+
     # 創建圖表數據
     metrics_df = pd.DataFrame(performance_metrics).T
-    
+
     # 創建多圖表比較
     cols = st.columns(2)
-    
+
     # 1. 累積回報率比較
     with cols[0]:
         fig_return = px.bar(
@@ -2098,7 +2099,7 @@ st.markdown("""
         )
         
         st.plotly_chart(fig_return, use_container_width=True)
-    
+
     # 2. 波動率比較
     with cols[1]:
         fig_vol = px.bar(
@@ -2127,9 +2128,9 @@ st.markdown("""
         )
         
         st.plotly_chart(fig_vol, use_container_width=True)
-    
+
     cols = st.columns(2)
-    
+
     # 3. 夏普比率比較
     with cols[0]:
         sharpe_colors = ['#d62728' if s < 0 else '#2ca02c' for s in metrics_df['sharpe_ratio']]
@@ -2159,7 +2160,7 @@ st.markdown("""
         )
         
         st.plotly_chart(fig_sharpe, use_container_width=True)
-    
+
     # 4. 最大回撤比較
     with cols[1]:
         fig_drawdown = px.bar(
@@ -2188,10 +2189,10 @@ st.markdown("""
         )
         
         st.plotly_chart(fig_drawdown, use_container_width=True)
-    
+
     # 5. 績效指標比較表格
     st.markdown("### 績效指標詳細比較")
-    
+
     # 準備顯示的數據
     display_df = pd.DataFrame({
         '累積報酬率 (%)': metrics_df['cumulative_return'].round(2),
@@ -2200,7 +2201,7 @@ st.markdown("""
         '夏普比率': metrics_df['sharpe_ratio'].round(2),
         '最大回撤 (%)': metrics_df['max_drawdown'].round(2)
     })
-    
+
     # 表格高亮設定
     st.dataframe(
         display_df,