src/streamlit_app.py

import streamlit as st
import yfinance as yf
import pandas as pd
import ta
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import datetime
import io

# 設定頁面配置
st.set_page_config(
    page_title="股票技術分析系統",
    page_icon="📈",
    layout="wide"
)

def analyze_stock(ticker_input):
    """分析單一股票的技術指標"""
    try:
        ticker = yf.Ticker(ticker_input)
        df = ticker.history(period="200d")
        if df.empty:
            return None, f"無法取得 {ticker_input} 的資料，請確認股票代碼是否正確。"
    except Exception as e:
        return None, f"取得 {ticker_input} 資料時發生錯誤: {e}"

    # 處理時區和數據
    df.index = df.index.tz_localize(None)
    df = df.tail(200)

    # 計算技術指標
    df['MA5'] = df['Close'].rolling(window=5).mean()
    df['MA10'] = df['Close'].rolling(window=10).mean()
    df['MA20'] = df['Close'].rolling(window=20).mean()
    df['RSI'] = ta.momentum.RSIIndicator(df['Close']).rsi()
    df['OBV'] = ta.volume.OnBalanceVolumeIndicator(df['Close'], df['Volume']).on_balance_volume()

    # 布林帶
    bb = ta.volatility.BollingerBands(df['Close'])
    df['BB_High'] = bb.bollinger_hband()
    df['BB_Low'] = bb.bollinger_lband()
    df['%B'] = (df['Close'] - df['BB_Low']) / (df['BB_High'] - df['BB_Low'])
    df['BBW'] = (df['BB_High'] - df['BB_Low']) / df['MA20']

    # KD指標
    kd = ta.momentum.StochasticOscillator(df['High'], df['Low'], df['Close'])
    df['K'] = kd.stoch()
    df['D'] = kd.stoch_signal()

    # MACD
    macd = ta.trend.MACD(df['Close'])
    df['MACD'] = macd.macd()
    df['MACD_Signal'] = macd.macd_signal()
    df['MACD_Diff'] = macd.macd_diff()

    # CCI和黃金交叉
    df['CCI'] = ta.trend.CCIIndicator(df['High'], df['Low'], df['Close']).cci()
    df['Golden_Cross'] = df['MA5'] > df['MA20']

    return df, None

def create_stock_chart(df, ticker):
    """創建股票圖表"""
    fig = make_subplots(
        rows=4, cols=1,
        subplot_titles=('價格與移動平均線', 'RSI', 'MACD', 'KD指標'),
        vertical_spacing=0.08,
        row_heights=[0.5, 0.2, 0.2, 0.2]
    )

    # 價格與移動平均線
    fig.add_trace(go.Candlestick(
        x=df.index,
        open=df['Open'],
        high=df['High'],
        low=df['Low'],
        close=df['Close'],
        name='價格'
    ), row=1, col=1)

    fig.add_trace(go.Scatter(x=df.index, y=df['MA5'], name='MA5', line=dict(color='orange')), row=1, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['MA10'], name='MA10', line=dict(color='blue')), row=1, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['MA20'], name='MA20', line=dict(color='red')), row=1, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['BB_High'], name='布林帶上軌', line=dict(color='gray', dash='dash')), row=1, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['BB_Low'], name='布林帶下軌', line=dict(color='gray', dash='dash')), row=1, col=1)

    # RSI
    fig.add_trace(go.Scatter(x=df.index, y=df['RSI'], name='RSI', line=dict(color='purple')), row=2, col=1)
    fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
    fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)

    # MACD
    fig.add_trace(go.Scatter(x=df.index, y=df['MACD'], name='MACD', line=dict(color='blue')), row=3, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['MACD_Signal'], name='MACD Signal', line=dict(color='red')), row=3, col=1)
    fig.add_trace(go.Bar(x=df.index, y=df['MACD_Diff'], name='MACD Histogram', marker_color='gray'), row=3, col=1)

    # KD指標
    fig.add_trace(go.Scatter(x=df.index, y=df['K'], name='K', line=dict(color='blue')), row=4, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['D'], name='D', line=dict(color='red')), row=4, col=1)
    fig.add_hline(y=80, line_dash="dash", line_color="red", row=4, col=1)
    fig.add_hline(y=20, line_dash="dash", line_color="green", row=4, col=1)

    fig.update_layout(
        title=f'{ticker} 技術分析圖表',
        height=800,
        showlegend=True,
        xaxis_rangeslider_visible=False
    )

    return fig

def get_investment_advice(df, ticker):
    """獲取投資建議"""
    advice_list = []
    latest_data = df.iloc[-1]
    
    # RSI建議
    if latest_data['RSI'] < 30:
        advice_list.append({
            'type': '買進建議',
            'reason': f'RSI = {latest_data["RSI"]:.2f} < 30 (超賣)',
            'color': 'success'
        })
    elif latest_data['RSI'] > 70:
        advice_list.append({
            'type': '賣出建議',
            'reason': f'RSI = {latest_data["RSI"]:.2f} > 70 (超買)',
            'color': 'error'
        })
    
    # %B建議
    if latest_data['%B'] < 0:
        advice_list.append({
            'type': '買進建議',
            'reason': f'%B = {latest_data["%B"]:.2f} < 0 (價格低於布林帶下軌)',
            'color': 'success'
        })
    elif latest_data['%B'] > 1:
        advice_list.append({
            'type': '賣出建議',
            'reason': f'%B = {latest_data["%B"]:.2f} > 1 (價格高於布林帶上軌)',
            'color': 'error'
        })
    
    # 黃金交叉
    if latest_data['Golden_Cross']:
        advice_list.append({
            'type': '看多信號',
            'reason': '短期均線突破長期均線 (黃金交叉)',
            'color': 'info'
        })
    
    return advice_list

def create_excel_download(df_dict):
    """創建Excel下載檔案"""
    from openpyxl import Workbook
    from openpyxl.styles import PatternFill
    from openpyxl.utils.dataframe import dataframe_to_rows
    
    wb = Workbook()
    wb.remove(wb.active)

    yellow_fill = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")
    red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
    green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")
    purple_fill = PatternFill(start_color="800080", end_color="800080", fill_type="solid")

    advice_rows = []

    for ticker_input, df in df_dict.items():
        ws = wb.create_sheet(title=ticker_input)
        ws.append(['股票代碼:', ticker_input])
        ws.append(['分析日期:', datetime.now().strftime('%Y-%m-%d %H:%M:%S')])
        ws.append([])

        for r in dataframe_to_rows(df, index=True, header=True):
            ws.append(r)

        # 格式化和標色邏輯（保持原有邏輯）
        header_row = 4
        headers = {cell.value: cell.column for cell in ws[header_row]}
        rsi_col = headers.get('RSI')
        percentage_b_col = headers.get('%B')
        
        if rsi_col and percentage_b_col:
            for row in range(header_row + 1, ws.max_row + 1):
                rsi_val = ws.cell(row=row, column=rsi_col).value
                pct_b_val = ws.cell(row=row, column=percentage_b_col).value

                if rsi_val is not None:
                    rsi_cell = ws.cell(row=row, column=rsi_col)
                    if rsi_val < 20:
                        rsi_cell.fill = yellow_fill
                    elif rsi_val > 70:
                        rsi_cell.fill = red_fill

                if pct_b_val is not None:
                    b_cell = ws.cell(row=row, column=percentage_b_col)
                    if pct_b_val < 0:
                        b_cell.fill = green_fill
                    elif pct_b_val > 1:
                        b_cell.fill = purple_fill

    # 儲存到記憶體
    output = io.BytesIO()
    wb.save(output)
    output.seek(0)
    return output.getvalue()

# Streamlit 應用程式主體
def main():
    st.title("📈 股票技術分析系統")
    st.markdown("---")

    # 側邊欄設定
    st.sidebar.header("分析設定")
    
    # 選擇分析模式
    analysis_mode = st.sidebar.radio(
        "選擇分析模式",
        ["單一股票分析", "批量股票分析", "從Google Sheets匯入"]
    )

    if analysis_mode == "單一股票分析":
        st.header("🎯 單一股票分析")
        
        ticker_input = st.text_input("請輸入股票代碼 (例如: AAPL, 2330.TW):", value="AAPL")
        
        if st.button("開始分析", type="primary"):
            if ticker_input:
                with st.spinner(f"正在分析 {ticker_input} ..."):
                    df, error = analyze_stock(ticker_input)
                    
                    if error:
                        st.error(error)
                    else:
                        st.success(f"成功分析 {ticker_input}")
                        
                        # 顯示基本資訊
                        col1, col2, col3, col4 = st.columns(4)
                        latest_data = df.iloc[-1]
                        
                        with col1:
                            st.metric("最新收盤價", f"${latest_data['Close']:.2f}")
                        with col2:
                            st.metric("RSI", f"{latest_data['RSI']:.2f}")
                        with col3:
                            st.metric("%B", f"{latest_data['%B']:.2f}")
                        with col4:
                            st.metric("成交量", f"{latest_data['Volume']:,}")
                        
                        # 顯示圖表
                        fig = create_stock_chart(df, ticker_input)
                        st.plotly_chart(fig, use_container_width=True)
                        
                        # 投資建議
                        st.subheader("💡 投資建議")
                        advice_list = get_investment_advice(df, ticker_input)
                        
                        if advice_list:
                            for advice in advice_list:
                                if advice['color'] == 'success':
                                    st.success(f"**{advice['type']}**: {advice['reason']}")
                                elif advice['color'] == 'error':
                                    st.error(f"**{advice['type']}**: {advice['reason']}")
                                else:
                                    st.info(f"**{advice['type']}**: {advice['reason']}")
                        else:
                            st.info("目前沒有明確的買賣建議")
                        
                        # 顯示詳細數據
                        with st.expander("查看詳細數據"):
                            st.dataframe(df.tail(20))

    elif analysis_mode == "批量股票分析":
        st.header("📊 批量股票分析")
        
        ticker_text = st.text_area(
            "請輸入股票代碼 (每行一個):",
            value="AAPL\nMSFT\nGOOGL\n2330.TW\n2317.TW",
            height=150
        )
        
        if st.button("開始批量分析", type="primary"):
            tickers = [ticker.strip() for ticker in ticker_text.split('\n') if ticker.strip()]
            
            if tickers:
                progress_bar = st.progress(0)
                status_text = st.empty()
                all_data = {}
                
                for i, ticker in enumerate(tickers):
                    status_text.text(f"正在分析 {ticker} ({i+1}/{len(tickers)})")
                    progress_bar.progress((i + 1) / len(tickers))
                    
                    df, error = analyze_stock(ticker)
                    if error:
                        st.warning(f"{ticker}: {error}")
                    else:
                        all_data[ticker] = df
                
                status_text.text("分析完成!")
                
                if all_data:
                    st.success(f"成功分析 {len(all_data)} 支股票")
                    
                    # 顯示摘要表格
                    summary_data = []
                    for ticker, df in all_data.items():
                        latest = df.iloc[-1]
                        summary_data.append({
                            '股票代碼': ticker,
                            '最新價格': f"${latest['Close']:.2f}",
                            'RSI': f"{latest['RSI']:.2f}",
                            '%B': f"{latest['%B']:.2f}",
                            '黃金交叉': '是' if latest['Golden_Cross'] else '否'
                        })
                    
                    summary_df = pd.DataFrame(summary_data)
                    st.subheader("📋 分析摘要")
                    st.dataframe(summary_df, use_container_width=True)
                    
                    # 提供Excel下載
                    excel_data = create_excel_download(all_data)
                    st.download_button(
                        label="📥 下載完整分析報告 (Excel)",
                        data=excel_data,
                        file_name=f"技術分析總表_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx",
                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
                    )

    else:  # Google Sheets 匯入
        st.header("📑 從Google Sheets匯入")
        
        sheet_id = st.text_input(
            "請輸入Google Sheets ID:",
            value="1CZlw53z9Fns3XCQmlY_PXF2qOv2Wyqyn7mRrIzDRQMg"
        )
        
        if st.button("從Google Sheets匯入並分析", type="primary"):
            try:
                sheet_url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/export?format=csv"
                df_tickers = pd.read_csv(sheet_url)
                tickers = df_tickers["股號"].dropna().astype(str).tolist()
                
                st.info(f"從Google Sheets讀取到 {len(tickers)} 個股票代碼")
                
                progress_bar = st.progress(0)
                status_text = st.empty()
                all_data = {}
                
                for i, ticker in enumerate(tickers):
                    status_text.text(f"正在分析 {ticker} ({i+1}/{len(tickers)})")
                    progress_bar.progress((i + 1) / len(tickers))
                    
                    df, error = analyze_stock(ticker)
                    if error:
                        st.warning(f"{ticker}: {error}")
                    else:
                        all_data[ticker] = df
                
                status_text.text("分析完成!")
                
                if all_data:
                    st.success(f"成功分析 {len(all_data)} 支股票")
                    
                    # Excel下載
                    excel_data = create_excel_download(all_data)
                    st.download_button(
                        label="📥 下載完整分析報告 (Excel)",
                        data=excel_data,
                        file_name=f"技術分析總表_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx",
                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
                    )
                    
            except Exception as e:
                st.error(f"讀取Google Sheets時發生錯誤: {e}")

    # 說明資訊
    with st.sidebar.expander("📖 使用說明"):
        st.markdown("""
        **技術指標說明:**
        - **RSI < 30**: 超賣，考慮買進
        - **RSI > 70**: 超買，考慮賣出
        - **%B < 0**: 價格低於布林帶下軌
        - **%B > 1**: 價格高於布林帶上軌
        - **黃金交叉**: 短期均線突破長期均線
        
        **股票代碼格式:**
        - 美股: AAPL, MSFT, GOOGL
        - 台股: 2330.TW, 2317.TW
        """)

if __name__ == "__main__":
    main()