Update app.py

app.py

@@ -1,108 +1,7 @@
 import streamlit as st
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import pandas as pd
 import twstock
-from datetime import datetime, timedelta
-
-def plot_stock_data(stock_symbols):
-    """
-    繪製股票價格圖表
-    :param stock_symbols: 股票代號列表
-    :return: Plotly figure
-    """
-    # 創建子圖
-    fig = make_subplots(
-        rows=len(stock_symbols),
-        cols=1,
-        subplot_titles=[f"股價走勢: {symbol}" for symbol in stock_symbols],
-        vertical_spacing=0.05,
-        specs=[[{"secondary_y": True}] for _ in stock_symbols]
-    )
-
-    # 為每個股票繪製圖形
-    for idx, symbol in enumerate(stock_symbols, 1):
-        try:
-            # 獲取股票數據
-            stock = twstock.Stock(symbol)
-            recent_data = stock.fetch_31()  # 抓取最近 31 天的交易數據
-
-            if not recent_data:
-                st.warning(f"無法找到 {symbol} 的股票數據")
-                continue
-
-            # 準備繪圖數據
-            df = pd.DataFrame([
-                {
-                    "Date": data.date,
-                    "Open": data.open,
-                    "High": data.high,
-                    "Low": data.low,
-                    "Close": data.close,
-                    "Volume": data.transaction
-                }
-                for data in recent_data
-            ])
-            df['Date'] = pd.to_datetime(df['Date'])
-            df.set_index('Date', inplace=True)
-
-            # 添加蠟燭圖
-            fig.add_trace(
-                go.Candlestick(
-                    x=df.index,
-                    open=df['Open'],
-                    high=df['High'],
-                    low=df['Low'],
-                    close=df['Close'],
-                    name=f'{symbol} 價格'
-                ),
-                row=idx, col=1
-            )
-
-            # 添加成交量柱狀圖
-            fig.add_trace(
-                go.Bar(
-                    x=df.index,
-                    y=df['Volume'],
-                    name=f'{symbol} 成交量',
-                    opacity=0.3
-                ),
-                row=idx, col=1,
-                secondary_y=True
-            )
-
-            # 添加移動平均線
-            for ma_days in [5, 20]:
-                ma = df['Close'].rolling(window=ma_days).mean()
-                fig.add_trace(
-                    go.Scatter(
-                        x=df.index,
-                        y=ma,
-                        name=f'{symbol} MA{ma_days}',
-                        line=dict(width=1)
-                    ),
-                    row=idx, col=1
-                )
-
-        except Exception as e:
-            st.error(f"處理 {symbol} 時發生錯誤: {str(e)}")
-
-    # 更新布局
-    fig.update_layout(
-        height=400 * len(stock_symbols),
-        title_text="台股分析圖",
-        showlegend=True,
-        xaxis_rangeslider_visible=False,
-        template="plotly_white"
-    )
-
-    # 更新軸標籤
-    for i in range(1, len(stock_symbols) + 1):
-        fig.update_xaxes(title_text="日期", row=i, col=1)
-        fig.update_yaxes(title_text="價格 (TWD)", row=i, col=1)
-        fig.update_yaxes(title_text="成交量", row=i, col=1, secondary_y=True)
-
-    return fig
+import pandas as pd
+import matplotlib.pyplot as plt
 
 def fetch_recent_stock_data(stock_code):
     """
@@ -131,12 +30,28 @@ def fetch_recent_stock_data(stock_code):
             for data in recent_data
         ]
         df = pd.DataFrame(data_list)
+        df['Date'] = pd.to_datetime(df['Date'])
         return df
 
     except Exception as e:
         st.error(f"發生錯誤: {e}")
         return None
 
+def plot_stock_price(df):
+    """
+    使用 matplotlib 繪製股價走勢
+    """
+    plt.figure(figsize=(12, 6))
+    plt.plot(df['Date'], df['Close'], label='收盤價')
+    plt.plot(df['Date'], df['Close'].rolling(window=5).mean(), label='5日移動平均', linestyle='--')
+    plt.title('股價走勢')
+    plt.xlabel('日期')
+    plt.ylabel('股價')
+    plt.legend()
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    return plt
+
 def main():
     st.set_page_config(page_title="台股分析工具", page_icon=":chart_with_upwards_trend:", layout="wide")
     
@@ -144,13 +59,13 @@ def main():
 
     # 側邊欄設置
     with st.sidebar:
-        st.header("股票分析設定")
+        st.header("股票分析")
         
         # 股票代碼輸入
-        stock_input = st.text_input(
-            "股票代號 (用逗號分隔)",
-            value="2330,2454",
-            placeholder="例如: 2330,2454"
+        stock_code = st.text_input(
+            "股票代號",
+            value="2330",
+            placeholder="例如: 2330"
         )
 
     # 股票分析頁籤
@@ -159,52 +74,47 @@ def main():
     with tab1:
         # 股價走勢圖
         if st.button("繪製股價走勢圖"):
-            # 處理股票代號
-            stocks = [s.strip() for s in stock_input.split(',')]
+            # 獲取股票數據
+            df = fetch_recent_stock_data(stock_code)
             
-            # 創建圖表
-            fig = plot_stock_data(stocks)
-            st.plotly_chart(fig, use_container_width=True)
+            if df is not None:
+                # 繪製股價圖
+                fig = plot_stock_price(df)
+                st.pyplot(fig)
 
     with tab2:
         # 近期交易數據
         st.subheader("個股近期交易數據")
         
-        single_stock_code = st.text_input(
-            "請輸入股票代碼",
-            placeholder="例如: 2330"
-        )
-        
         if st.button("查詢交易數據"):
-            if single_stock_code:
-                # 獲取近期股票數據
-                df = fetch_recent_stock_data(single_stock_code)
+            # 獲取近期股票數據
+            df = fetch_recent_stock_data(stock_code)
+            
+            if df is not None:
+                # 顯示數據
+                st.dataframe(df)
+                
+                # 統計資訊
+                st.subheader("基本統計")
+                col1, col2, col3 = st.columns(3)
+                
+                with col1:
+                    st.metric("平均收盤價", f"{df['Close'].mean():.2f}")
                 
-                if df is not None:
-                    # 顯示數據
-                    st.dataframe(df)
-                    
-                    # 統計資訊
-                    st.subheader("基本統計")
-                    col1, col2, col3 = st.columns(3)
-                    
-                    with col1:
-                        st.metric("平均收盤價", f"{df['Close'].mean():.2f}")
-                    
-                    with col2:
-                        st.metric("最高價", f"{df['High'].max():.2f}")
-                    
-                    with col3:
-                        st.metric("最低價", f"{df['Low'].min():.2f}")
-                    
-                    # 匯出 CSV
-                    csv_data = df.to_csv(index=False).encode('utf-8-sig')
-                    st.download_button(
-                        label="下載CSV",
-                        data=csv_data,
-                        file_name=f"{single_stock_code}_recent_30days.csv",
-                        mime="text/csv"
-                    )
+                with col2:
+                    st.metric("最高價", f"{df['High'].max():.2f}")
+                
+                with col3:
+                    st.metric("最低價", f"{df['Low'].min():.2f}")
+                
+                # 匯出 CSV
+                csv_data = df.to_csv(index=False).encode('utf-8-sig')
+                st.download_button(
+                    label="下載CSV",
+                    data=csv_data,
+                    file_name=f"{stock_code}_recent_30days.csv",
+                    mime="text/csv"
+                )
 
 if __name__ == "__main__":
     main()