Update app.py

app.py

@@ -1,13 +1,102 @@
 import streamlit as st
+import yfinance as yf
 import twstock
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 import pandas as pd
-import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+
+def plot_stock_data(stock_symbols, period='1y'):
+    """
+    繪製股票價格圖表
+    :param stock_symbols: 股票代號列表
+    :param period: 時間區間
+    :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 = yf.Ticker(symbol)
+            df = stock.history(period=period)
+
+            if df.empty:
+                st.warning(f"無法獲取 {symbol} 的股票數據")
+                continue
+
+            # 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, 60]:
+                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
 
 def fetch_recent_stock_data(stock_code):
     """
-    使用 twstock 獲取近期股票交易數據
+    使用 twstock 獲取近 30 天的交易數據
     """
     try:
+        # 使用 twstock 獲取近 30 天的交易數據
         stock = twstock.Stock(stock_code)
         recent_data = stock.fetch_31()  # 抓取最近 31 天的交易數據
 
@@ -18,100 +107,80 @@ def fetch_recent_stock_data(stock_code):
         # 將數據整理為 DataFrame 格式
         data_list = [
             {
-                "Date": data.date.strftime('%Y-%m-%d'),
-                "Open": data.open,
-                "High": data.high,
-                "Low": data.low,
-                "Close": data.close,
-                "Transaction": data.transaction,
-                "Capacity": data.capacity,
-                "Turnover": data.turnover
+                "Date": data.date.strftime('%Y-%m-%d'),  # 日期
+                "Open": data.open,      # 開盤價
+                "High": data.high,      # 最高價
+                "Low": data.low,        # 最低價
+                "Close": data.close,    # 收盤價
+                "Transaction": data.transaction,  # 成交筆數
+                "Capacity": data.capacity,       # 成交股數
+                "Turnover": data.turnover        # 成交金額
             }
             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}")
+        st.error("請確認股票代碼是否正確，或是否為台股上市/上櫃股票。")
         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="��股分析工具", page_icon=":chart_with_upwards_trend:", layout="wide")
-    
-    st.title("🚀 台股分析工具")
+    st.set_page_config(page_title="台股分析工具", layout="wide")
+    st.title("台股分析工具")
 
-    # 側邊欄設置
-    with st.sidebar:
-        st.header("股票分析")
-        
-        # 股票代碼輸入
-        stock_code = st.text_input(
-            "股票代號",
-            value="2330",
-            placeholder="例如: 2330"
+    # 選擇分析模式
+    mode = st.sidebar.radio("選擇分析模式", ["歷史股價圖", "近期交易資料"])
+
+    if mode == "歷史股價圖":
+        # 股票代號輸入
+        stock_input = st.text_input(
+            "股票代號 (用逗號分隔)",
+            value="2330.TW,2454.TW",
+            placeholder="例如: 2330,2454"
         )
 
-    # 股票分析頁籤
-    tab1, tab2 = st.tabs(["股價走勢圖", "近期交易數據"])
+        # 時間區間選擇
+        period_select = st.selectbox(
+            "時間區間",
+            ["1mo", "3mo", "6mo", "1y", "2y", "5y", "max"],
+            index=3  # 預設為 1y
+        )
 
-    with tab1:
-        # 股價走勢圖
-        if st.button("繪製股價走勢圖"):
-            # 獲取股票數據
-            df = fetch_recent_stock_data(stock_code)
-            
-            if df is not None:
-                # 繪製股價圖
-                fig = plot_stock_price(df)
-                st.pyplot(fig)
+        # 處理股票代號
+        stocks = [s.strip() for s in stock_input.split(',')]
+        stocks = [f"{s}.TW" if not s.endswith('.TW') and s.isdigit() else s for s in stocks]
 
-    with tab2:
-        # 近期交易數據
-        st.subheader("個股近期交易數據")
-        
-        if st.button("查詢交易數據"):
-            # 獲取近期股票數據
+        # 繪製圖表按鈕
+        if st.button("繪製圖表"):
+            fig = plot_stock_data(stocks, period_select)
+            st.plotly_chart(fig, use_container_width=True)
+
+    else:  # 近期交易資料模式
+        stock_code = st.text_input("請輸入股票代碼", value="2330")
+
+        if st.button("查詢資料"):
             df = fetch_recent_stock_data(stock_code)
             
             if df is not None:
-                # 顯示數據
+                # 顯示 DataFrame
+                st.subheader(f"股票代碼: {stock_code} - 最近30天交易數據")
                 st.dataframe(df)
-                
-                # 統計資訊
+
+                # 基本統計
                 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')
+                col1.metric("平均收盤價", f"{df['Close'].mean():.2f}")
+                col2.metric("最高價", f"{df['High'].max():.2f}")
+                col3.metric("最低價", f"{df['Low'].min():.2f}")
+
+                # 下載 CSV
+                csv = df.to_csv(index=False, encoding="utf-8-sig")
                 st.download_button(
-                    label="下載CSV",
-                    data=csv_data,
+                    label="下載 CSV 檔案",
+                    data=csv,
                     file_name=f"{stock_code}_recent_30days.csv",
                     mime="text/csv"
                 )