import streamlit as st
import twstock
import pandas as pd
import matplotlib.pyplot as plt

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)
        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")
    
    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"
        )

    # 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"):
            # 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)

    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
            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}")
                
                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()