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
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 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

        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'),  # 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)
        
        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 main():
    st.set_page_config(page_title="台股分析工具", layout="wide")
    st.title("台股分析工具")

    # 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"
        )

        # 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
        )

        # 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]

        # 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 DataFrame
                st.subheader(f"股票代碼: {stock_code} - 最近30天交易數據")
                st.dataframe(df)

                # 基本統計
                st.subheader("基本統計")
                col1, col2, col3 = st.columns(3)
                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 檔案",
                    data=csv,
                    file_name=f"{stock_code}_recent_30days.csv",
                    mime="text/csv"
                )

if __name__ == "__main__":
    main()