Create app.py

app.py

@@ -0,0 +1,205 @@
+import streamlit as st
+import yfinance as yf
+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, 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"無法找到 {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
+    """
+    try:
+        stock = twstock.Stock(stock_code)
+        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 {stock_code} 的交易數據。")
+            return None
+
+        # 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
+            }
+            for data in recent_data
+        ]
+        df = pd.DataFrame(data_list)
+        return df
+
+    except Exception as e:
+        st.error(f"發生錯誤: {e}")
+        return None
+
+def main():
+    st.set_page_config(page_title="台股分析工具", page_icon=":chart_with_upwards_trend:", layout="wide")
+    
+    st.title("🚀 台股分析工具")
+
+    # 側邊欄設置
+    with st.sidebar:
+        st.header("股票分析設定")
+        
+        # 股票代碼輸入
+        stock_input = st.text_input(
+            "股票代號 (用逗號分隔)",
+            value="2330.TW,2454.TW",
+            placeholder="例如: 2330.TW,2454.TW"
+        )
+        
+        # 時間區間選擇
+        period_select = st.selectbox(
+            "選擇時間區間",
+            ["1mo", "3mo", "6mo", "1y", "2y", "5y", "max"],
+            index=3  # 預設為 1y
+        )
+
+    # 股票分析頁籤
+    tab1, tab2 = st.tabs(["股價走勢圖", "近期交易數據"])
+
+    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(',')]
+            stocks = [f"{s}.TW" if not s.endswith('.TW') and s.isdigit() else s for s in stocks]
+            
+            # 創建圖表
+            fig = plot_stock_data(stocks, period_select)
+            st.plotly_chart(fig, use_container_width=True)
+
+    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)
+                
+                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"
+                    )
+
+if __name__ == "__main__":
+    main()