src/streamlit_app.py

import streamlit as st
import yfinance as yf
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import time

# 頁面配置和主題設定
st.set_page_config(
    page_title="台灣股票分析儀表板",
    page_icon="💹",
    layout="wide",
    initial_sidebar_state="expanded"
)

# 應用CSS美化界面
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem;
        background: linear-gradient(90deg, #0E76BC, #00A591);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        padding: 0.2rem 0;
        text-align: center;
        margin-bottom: 1rem;
    }
    
    .dashboard-header {
        font-size: 1.8rem;
        background: linear-gradient(90deg, #2C3E50, #4CA1AF);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        padding: 0.2rem 0;
        margin-bottom: 0.5rem;
    }
    
    .card {
        border-radius: 5px;
        background-color: #f9f9f9;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
        padding: 1rem;
        margin-bottom: 1rem;
    }
    
    .metric-card {
        background: linear-gradient(120deg, #f6f9fc, #e9f1f7);
        border-radius: 8px;
        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
        padding: 1rem;
        text-align: center;
    }
    
    .metric-value {
        font-size: 1.8rem;
        font-weight: bold;
    }
    
    .metric-change-positive {
        color: #00A591;
        font-weight: bold;
    }
    
    .metric-change-negative {
        color: #E74C3C;
        font-weight: bold;
    }
    
    .subtitle {
        color: #666;
        font-size: 1rem;
    }
    
    .stTabs [data-baseweb="tab-list"] {
        gap: 24px;
    }
    
    .stTabs [data-baseweb="tab"] {
        height: 50px;
        white-space: pre-wrap;
        background-color: #f0f2f6;
        border-radius: 4px 4px 0px 0px;
        gap: 1px;
        padding-top: 10px;
        padding-bottom: 10px;
    }
    
    .stTabs [aria-selected="true"] {
        background-color: #0E76BC !important;
        color: white !important;
    }

    /* 載入動畫 */
    @keyframes pulse {
        0% { opacity: 0.6; }
        50% { opacity: 1; }
        100% { opacity: 0.6; }
    }
    
    .loading-animation {
        animation: pulse 1.5s infinite;
        background-color: #0E76BC;
        color: white;
        padding: 10px;
        border-radius: 5px;
        text-align: center;
    }

    /* 自訂滾動條 */
    ::-webkit-scrollbar {
        width: 10px;
        background: #f1f1f1;
    }
    
    ::-webkit-scrollbar-thumb {
        background: #0E76BC;
        border-radius: 5px;
    }
    
    ::-webkit-scrollbar-thumb:hover {
        background: #09589b;
    }
</style>
""", unsafe_allow_html=True)

# 標題和說明
st.markdown('<h1 class="main-header">台灣股票高級分析儀表板</h1>', unsafe_allow_html=True)
st.markdown("""
<div class="card">
    <p>這個專業儀表板提供深入的台灣股票分析功能，包括價格趨勢比較、技術指標、基本面數據以及風險分析。輸入股票代號並選擇分析參數來開始您的專業股票分析。</p>
</div>
""", unsafe_allow_html=True)

# ------ 功能函數 ------ #

def get_stock_data(stock_ids, period="1y", interval="1d"):
    """
    獲取股票數據
    
    參數:
    stock_ids (list): 股票代碼列表
    period (str): 時間週期
    interval (str): 間隔
    
    返回:
    dict: 股票數據字典
    """
    stock_data = {}
    
    with st.spinner("正在獲取股票數據..."):
        progress_bar = st.progress(0)
        for i, stock_id in enumerate(stock_ids):
            try:
                # 確保台灣股票代碼格式正確
                if stock_id.isdigit() or (len(stock_id) <= 4 and stock_id.split('.')[0].isdigit()):
                    if '.' not in stock_id:
                        stock_id = f"{stock_id}.TW"
                
                # 獲取股票數據
                stock = yf.Ticker(stock_id)
                hist = stock.history(period=period, interval=interval)
                try:
                    info = stock.info
                except:
                    info = {}
                
                if not hist.empty:
                    stock_data[stock_id] = {
                        'history': hist,
                        'info': info
                    }
                    st.success(f"✅ 成功獲取 {stock_id} 的數據")
                else:
                    st.error(f"❌ 未找到 {stock_id} 的數據")
            
            except Exception as e:
                st.error(f"❌ 獲取 {stock_id} 數據時出錯: {str(e)}")
            
            progress_bar.progress((i + 1) / len(stock_ids))
            time.sleep(0.5)  # 為了視覺效果添加的短暫延遲
        
        progress_bar.empty()
    
    return stock_data

def normalize_data(df):
    """
    將數據正規化，以便比較不同價格範圍的股票
    """
    return df / df.iloc[0] * 100

def calculate_rsi(data, window=14):
    """計算RSI指標"""
    delta = data.diff()
    gain = delta.where(delta > 0, 0).rolling(window=window).mean()
    loss = -delta.where(delta < 0, 0).rolling(window=window).mean()
    
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

def calculate_macd(data, fast=12, slow=26, signal=9):
    """計算MACD指標"""
    exp1 = data.ewm(span=fast, adjust=False).mean()
    exp2 = data.ewm(span=slow, adjust=False).mean()
    macd = exp1 - exp2
    signal_line = macd.ewm(span=signal, adjust=False).mean()
    histogram = macd - signal_line
    return macd, signal_line, histogram

def calculate_bollinger_bands(data, window=20, num_std=2):
    """計算布林帶"""
    rolling_mean = data.rolling(window=window).mean()
    rolling_std = data.rolling(window=window).std()
    upper_band = rolling_mean + (rolling_std * num_std)
    lower_band = rolling_mean - (rolling_std * num_std)
    return rolling_mean, upper_band, lower_band

def calculate_volatility(data, window=30):
    """計算波動率"""
    log_returns = np.log(data / data.shift(1))
    volatility = log_returns.rolling(window=window).std() * np.sqrt(252) * 100  # 年化波動率，以百分比表示
    return volatility

def calculate_moving_averages(data):
    """計算不同期間的移動平均線"""
    ma20 = data.rolling(window=20).mean()
    ma50 = data.rolling(window=50).mean()
    ma100 = data.rolling(window=100).mean()
    ma200 = data.rolling(window=200).mean()
    return ma20, ma50, ma100, ma200

def calculate_performance_metrics(data):
    """計算股票表現指標"""
    # 計算每日回報率
    daily_returns = data.pct_change().dropna()
    
    # 累積回報率
    cumulative_return = (data.iloc[-1] / data.iloc[0] - 1) * 100
    
    # 年化回報率 (假設252個交易日)
    days = len(data)
    annualized_return = ((1 + cumulative_return / 100) ** (252 / days) - 1) * 100
    
    # 波動率 (年化標準差)
    volatility = daily_returns.std() * np.sqrt(252) * 100
    
    # 夏普比率 (假設無風險利率為2%)
    risk_free_rate = 0.02
    sharpe_ratio = (annualized_return / 100 - risk_free_rate) / (volatility / 100)
    
    # 最大回撤
    cum_returns = (1 + daily_returns).cumprod()
    running_max = cum_returns.cummax()
    drawdown = (cum_returns / running_max - 1) * 100
    max_drawdown = drawdown.min()
    
    return {
        'cumulative_return': cumulative_return,
        'annualized_return': annualized_return,
        'volatility': volatility,
        'sharpe_ratio': sharpe_ratio,
        'max_drawdown': max_drawdown
    }

def display_stock_metrics(stock_data):
    """顯示多個股票的關鍵指標"""
    if not stock_data:
        return
    
    # 創建列來顯示指標
    cols = st.columns(len(stock_data))
    
    for i, (stock_id, data) in enumerate(stock_data.items()):
        hist = data['history']
        if hist.empty:
            continue
        
        # 獲取最新和前一天的價格
        latest_price = hist['Close'].iloc[-1]
        prev_price = hist['Close'].iloc[-2] if len(hist) > 1 else latest_price
        price_change = latest_price - prev_price
        price_change_pct = (price_change / prev_price) * 100 if prev_price != 0 else 0
        
        # 計算成交量變化
        latest_volume = hist['Volume'].iloc[-1] if 'Volume' in hist else 0
        avg_volume = hist['Volume'].mean() if 'Volume' in hist else 0
        volume_change_pct = ((latest_volume / avg_volume) - 1) * 100 if avg_volume != 0 else 0
        
        with cols[i]:
            st.markdown(f"<h3 style='text-align: center;'>{stock_id}</h3>", unsafe_allow_html=True)
            
            # 價格指標
            price_color = "metric-change-positive" if price_change >= 0 else "metric-change-negative"
            change_symbol = "▲" if price_change >= 0 else "▼"
            
            st.markdown(f"""
            <div class="metric-card">
                <div class="metric-value">{latest_price:.2f} TWD</div>
                <div class="{price_color}">{change_symbol} {abs(price_change):.2f} ({abs(price_change_pct):.2f}%)</div>
                <div class="subtitle">最新收盤價</div>
            </div>
            """, unsafe_allow_html=True)
            
            # 成交量指標
            volume_color = "metric-change-positive" if volume_change_pct >= 0 else "metric-change-negative"
            vol_symbol = "▲" if volume_change_pct >= 0 else "▼"
            
            if 'Volume' in hist:
                formatted_volume = f"{latest_volume/1000000:.2f}M" if latest_volume >= 1000000 else f"{latest_volume/1000:.2f}K"
                st.markdown(f"""
                <div class="metric-card" style="margin-top: 10px;">
                    <div class="metric-value">{formatted_volume}</div>
                    <div class="{volume_color}">{vol_symbol} {abs(volume_change_pct):.2f}%</div>
                    <div class="subtitle">最新成交量</div>
                </div>
                """, unsafe_allow_html=True)

def plot_stock_comparison(stock_data):
    """使用 Plotly 繪製股票比較圖表"""
    if not stock_data:
        st.warning("沒有可用的股票數據來繪製圖表")
        return
    
    # 創建圖表
    fig = make_subplots(rows=2, cols=1, 
                        shared_xaxes=True, 
                        vertical_spacing=0.1,
                        subplot_titles=("股價走勢比較", "正規化股價比較 (基準=100)"),
                        row_heights=[0.6, 0.4])
    
    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
    color_idx = 0
    
    # 用於正規化的數據
    normalized_data = {}
    
    # 添加每個股票的數據到圖表
    for stock_id, data in stock_data.items():
        hist = data['history']
        # 確保數據不為空
        if hist.empty:
            continue
        
        # 股票名稱顯示
        display_name = stock_id
        
        # 獲取顏色
        color = colors[color_idx % len(colors)]
        color_idx += 1
        
        # 添加原始價格折線圖
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=hist['Close'],
                mode='lines',
                name=f"{display_name}",
                line=dict(color=color, width=2)
            ),
            row=1, col=1
        )
        
        # 正規化數據
        normalized = normalize_data(hist['Close'])
        normalized_data[stock_id] = normalized
        
        # 添加正規化折線圖
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=normalized,
                mode='lines',
                name=f"{display_name} (正規化)",
                line=dict(color=color, width=2, dash='dot')
            ),
            row=2, col=1
        )
    
    # 更新布局
    fig.update_layout(
        height=700,
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        template="plotly_white",
        margin=dict(l=40, r=40, t=60, b=40),
        hovermode="x unified",
        plot_bgcolor='rgba(240,242,246,0.8)',
    )
    
    # 更新Y軸標題
    fig.update_yaxes(title_text="價格 (TWD)", row=1, col=1, gridcolor='rgba(220,220,220,0.5)')
    fig.update_yaxes(title_text="正規化價格 (基準=100)", row=2, col=1, gridcolor='rgba(220,220,220,0.5)')
    fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
    
    # 顯示圖表
    st.plotly_chart(fig, use_container_width=True)

def plot_volume_comparison(stock_data):
    """繪製股票成交量比較圖表"""
    if not stock_data:
        return
    
    # 創建圖表
    fig = go.Figure()
    
    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
    color_idx = 0
    
    # 添加每個股票的成交量到圖表
    has_volume_data = False
    
    for stock_id, data in stock_data.items():
        hist = data['history']
        # 確保數據不為空且有成交量數據
        if hist.empty or 'Volume' not in hist.columns:
            continue
        
        has_volume_data = True
        
        # 股票名稱顯示
        display_name = stock_id
        
        # 獲取顏色
        color = colors[color_idx % len(colors)]
        color_idx += 1
        
        # 添加成交量柱狀圖
        fig.add_trace(
            go.Bar(
                x=hist.index,
                y=hist['Volume'],
                name=f"{display_name}",
                marker_color=color,
                opacity=0.7
            )
        )
    
    if not has_volume_data:
        st.warning("沒有可用的成交量數據")
        return
    
    # 更新布局
    fig.update_layout(
        title="股票成交量比較",
        height=400,
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        template="plotly_white",
        barmode='group',
        bargap=0.15,
        bargroupgap=0.1,
        margin=dict(l=40, r=40, t=60, b=40),
        plot_bgcolor='rgba(240,242,246,0.8)',
    )
    
    fig.update_yaxes(title_text="成交量", gridcolor='rgba(220,220,220,0.5)')
    fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
    
    # 顯示圖表
    st.plotly_chart(fig, use_container_width=True)

def plot_technical_indicators(stock_data, selected_stock, indicator_type):
    """繪製技術指標圖表"""
    if not stock_data or selected_stock not in stock_data:
        st.warning("沒有所選股票的數據")
        return
    
    hist = stock_data[selected_stock]['history']
    if hist.empty:
        st.warning("所選股票沒有足夠的歷史數據")
        return
    
    # RSI 指標
    if indicator_type == "RSI":
        rsi = calculate_rsi(hist['Close'])
        
        fig = go.Figure()
        
        # 添加RSI線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=rsi,
                mode='lines',
                name='RSI',
                line=dict(color='#1f77b4', width=2)
            )
        )
        
        # 添加超買超賣水平線
        fig.add_trace(
            go.Scatter(
                x=[hist.index[0], hist.index[-1]],
                y=[70, 70],
                mode='lines',
                line=dict(color='red', width=1, dash='dash'),
                name='超買線 (70)'
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=[hist.index[0], hist.index[-1]],
                y=[30, 30],
                mode='lines',
                line=dict(color='green', width=1, dash='dash'),
                name='超賣線 (30)'
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=[hist.index[0], hist.index[-1]],
                y=[50, 50],
                mode='lines',
                line=dict(color='gray', width=1, dash='dot'),
                name='中位線 (50)'
            )
        )
        
        fig.update_layout(
            title=f"{selected_stock} RSI 指標",
            height=400,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            hovermode="x unified",
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig.update_yaxes(title_text="RSI 值", gridcolor='rgba(220,220,220,0.5)')
        fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
        
        st.plotly_chart(fig, use_container_width=True)
    
    # MACD 指標
    elif indicator_type == "MACD":
        macd, signal, histogram = calculate_macd(hist['Close'])
        
        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
                            vertical_spacing=0.1, 
                            row_heights=[0.7, 0.3])
        
        # 添加價格線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=hist['Close'],
                mode='lines',
                name='收盤價',
                line=dict(color='#1f77b4', width=2)
            ),
            row=1, col=1
        )
        
        # 添加MACD線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=macd,
                mode='lines',
                name='MACD',
                line=dict(color='#ff7f0e', width=2)
            ),
            row=2, col=1
        )
        
        # 添加訊號線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=signal,
                mode='lines',
                name='Signal',
                line=dict(color='#2ca02c', width=2)
            ),
            row=2, col=1
        )
        
        # 添加柱狀圖
        colors = ['red' if val < 0 else 'green' for val in histogram]
        
        fig.add_trace(
            go.Bar(
                x=hist.index,
                y=histogram,
                name='Histogram',
                marker_color=colors
            ),
            row=2, col=1
        )
        
        fig.update_layout(
            title=f"{selected_stock} MACD 指標",
            height=600,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            hovermode="x unified",
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            ),
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig.update_yaxes(title_text="價格", row=1, col=1, gridcolor='rgba(220,220,220,0.5)')
        fig.update_yaxes(title_text="MACD", row=2, col=1, gridcolor='rgba(220,220,220,0.5)')
        fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
        
        st.plotly_chart(fig, use_container_width=True)
    
    # 布林帶
    elif indicator_type == "布林帶":
        ma, upper, lower = calculate_bollinger_bands(hist['Close'])
        
        fig = go.Figure()
        
        # 添加價格線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=hist['Close'],
                mode='lines',
                name='收盤價',
                line=dict(color='#1f77b4', width=2)
            )
        )
        
        # 添加移動平均線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma,
                mode='lines',
                name='20日移動平均線',
                line=dict(color='#ff7f0e', width=2)
            )
        )
        
        # 添加上軌
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=upper,
                mode='lines',
                name='上軌 (+2σ)',
                line=dict(color='#2ca02c', width=1, dash='dash')
            )
        )
        
        # 添加下軌
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=lower,
                mode='lines',
                name='下軌 (-2σ)',
                line=dict(color='#d62728', width=1, dash='dash')
            )
        )
        
        # 添加陰影區域
        fig.add_trace(
            go.Scatter(
                x=hist.index.tolist() + hist.index.tolist()[::-1],
                y=upper.tolist() + lower.tolist()[::-1],
                fill='toself',
                fillcolor='rgba(44, 160, 44, 0.1)',
                line=dict(color='rgba(255,255,255,0)'),
                hoverinfo='skip',
                showlegend=False
            )
        )
        
        fig.update_layout(
            title=f"{selected_stock} 布林帶",
            height=500,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            hovermode="x unified",
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            ),
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig.update_yaxes(title_text="價格", gridcolor='rgba(220,220,220,0.5)')
        fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
        
        st.plotly_chart(fig, use_container_width=True)
    
    # 移動平均線
    elif indicator_type == "移動平均線":
        ma20, ma50, ma100, ma200 = calculate_moving_averages(hist['Close'])
        
        fig = go.Figure()
        
        # 添加價格線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=hist['Close'],
                mode='lines',
                name='收盤價',
                line=dict(color='#1f77b4', width=2)
            )
        )
        
        # 添加移動平均線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma20,
                mode='lines',
                name='20日均線',
                line=dict(color='#ff7f0e', width=1.5)
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma50,
                mode='lines',
                name='50日均線',
                line=dict(color='#2ca02c', width=1.5)
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma100,
                mode='lines',
                name='100日均線',
                line=dict(color='#d62728', width=1.5)
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma200,
                mode='lines',
                name='200日均線',
                line=dict(color='#9467bd', width=1.5)
            )
        )
        
        fig.update_layout(
            title=f"{selected_stock} 移動平均線",
            height=500,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            hovermode="x unified",
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            ),
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig.update_yaxes(title_text="價格", gridcolor='rgba(220,220,220,0.5)')
        fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
        
        st.plotly_chart(fig, use_container_width=True)

def plot_performance_comparison(stock_data):
    """繪製績效比較圖表"""
    if not stock_data:
        return
    
    # 收集績效指標
    performance_metrics = {}
    for stock_id, data in stock_data.items():
        hist = data['history']
        if not hist.empty:
            metrics = calculate_performance_metrics(hist['Close'])
            performance_metrics[stock_id] = metrics

def create_candlestick_chart(stock_data, selected_stock):
    """創建蠟燭圖"""
    if not stock_data or selected_stock not in stock_data:
        st.warning("沒有所選股票的數據")
        return
    
    hist = stock_data[selected_stock]['history']
    if hist.empty:
        st.warning("所選股票沒有足夠的歷史數據")
        return
    
    # 創建蠟燭圖
    fig = go.Figure(data=[go.Candlestick(
        x=hist.index,
        open=hist['Open'],
        high=hist['High'],
        low=hist['Low'],
        close=hist['Close'],
        increasing_line_color='#26a69a', 
        decreasing_line_color='#ef5350'
    )])
    
    # 添加移動平均線
    ma20 = hist['Close'].rolling(window=20).mean()
    ma50 = hist['Close'].rolling(window=50).mean()
    
    fig.add_trace(
        go.Scatter(
            x=hist.index,
            y=ma20,
            mode='lines',
            name='MA 20',
            line=dict(color='#2962ff', width=1.5)
        )
    )
    
    fig.add_trace(
        go.Scatter(
            x=hist.index,
            y=ma50,
            mode='lines',
            name='MA 50',
            line=dict(color='#ff6d00', width=1.5)
        )
    )
    
    # 更新布局
    fig.update_layout(
        title=f"{selected_stock} 蠟燭圖與移動平均線",
        height=600,
        template="plotly_white",
        margin=dict(l=40, r=40, t=60, b=40),
        xaxis_rangeslider_visible=False,  # 隱藏底部滾動條
        hovermode="x unified",
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        plot_bgcolor='rgba(240,242,246,0.8)',
    )
    
    fig.update_yaxes(title_text="價格 (TWD)", gridcolor='rgba(220,220,220,0.5)')
    fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
    
    # 下方加上成交量圖表
    fig2 = make_subplots(rows=2, cols=1, shared_xaxes=True, 
                        vertical_spacing=0.1, 
                        row_heights=[0.7, 0.3])
    
    # 添加蠟燭圖
    fig2.add_trace(
        go.Candlestick(
            x=hist.index,
            open=hist['Open'],
            high=hist['High'],
            low=hist['Low'],
            close=hist['Close'],
            increasing_line_color='#26a69a', 
            decreasing_line_color='#ef5350',
            name="股價"
        ),
        row=1, col=1
    )
    
    # 添加移動平均線
    fig2.add_trace(
        go.Scatter(
            x=hist.index,
            y=ma20,
            mode='lines',
            name='MA 20',
            line=dict(color='#2962ff', width=1.5)
        ),
        row=1, col=1
    )
    
    fig2.add_trace(
        go.Scatter(
            x=hist.index,
            y=ma50,
            mode='lines',
            name='MA 50',
            line=dict(color='#ff6d00', width=1.5)
        ),
        row=1, col=1
    )
    
    # 添加成交量
    colors = ['#ef5350' if row['Close'] < row['Open'] else '#26a69a' for index, row in hist.iterrows()]
    
    # 檢查是否有成交量數據
    if 'Volume' in hist.columns:
        fig2.add_trace(
            go.Bar(
                x=hist.index,
                y=hist['Volume'],
                marker_color=colors,
                name="成交量"
            ),
            row=2, col=1
        )
    
    # 更新布局
    fig2.update_layout(
        title=f"{selected_stock} 蠟燭圖與成交量",
        height=700,
        template="plotly_white",
        margin=dict(l=40, r=40, t=60, b=40),
        xaxis_rangeslider_visible=False,
        hovermode="x unified",
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        plot_bgcolor='rgba(240,242,246,0.8)',
    )
    
    fig2.update_yaxes(title_text="價格", row=1, col=1, gridcolor='rgba(220,220,220,0.5)')
    fig2.update_yaxes(title_text="成交量", row=2, col=1, gridcolor='rgba(220,220,220,0.5)')
    fig2.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
    
    tabs = st.tabs(["標準蠟燭圖", "蠟燭圖 + 成交量"])
    with tabs[0]:
        st.plotly_chart(fig, use_container_width=True)
    with tabs[1]:
        st.plotly_chart(fig2, use_container_width=True)

def plot_sector_pie_chart():
    """繪製台灣股市產業分佈圓餅圖"""
    # 台灣股市主要產業權重（模擬數據）
    sectors = {
        '半導體': 45.2,
        '電子零組件': 12.8,
        '電腦及周邊': 8.5,
        '金融保險': 7.9,
        '電信服務': 5.3,
        '塑膠化工': 4.8,
        '紡織纖維': 3.2,
        '鋼鐵': 2.9,
        '電機機械': 2.8,
        '食品': 2.2,
        '其他': 4.4
    }
    
    # 創建圓餅圖
    fig = go.Figure(data=[go.Pie(
        labels=list(sectors.keys()),
        values=list(sectors.values()),
        textinfo='label+percent',
        insidetextorientation='radial',
        hole=0.4,
        marker=dict(
            colors=px.colors.qualitative.Set3
        )
    )])
    
    fig.update_layout(
        title="台灣股市產業權重分佈",
        height=400,
        template="plotly_white",
        margin=dict(l=20, r=20, t=50, b=20),
        annotations=[dict(text='產業分佈', x=0.5, y=0.5, font_size=20, showarrow=False)]
    )
    
    st.plotly_chart(fig, use_container_width=True)

# 主程序部分
import random  # 導入隨機數模組，用於一些示範數據

# 側邊欄：股票選擇和參數設定
with st.sidebar:
    st.image("https://upload.wikimedia.org/wikipedia/commons/thumb/c/cb/TWSE_logo.svg/1200px-TWSE_logo.svg.png", width=100)
    st.markdown("### 分析設定")
    
    # 股票選擇
    st.subheader("選擇股票")
    
    default_stocks = "2330,2454,2317,2412,2382"
    stock_input = st.text_input(
        "輸入股票代碼 (以逗號分隔)",
        value=default_stocks,
        help="例如: 2330,2454,2317"
    )
    
    # 解析股票代碼
    stock_ids = [s.strip() for s in stock_input.split(',') if s.strip()]
    
    # 時間範圍選擇
    st.subheader("時間範圍")
    period = st.selectbox(
        "選擇時間範圍",
        options=["1m", "3m", "6m", "1y", "2y", "5y"],
        index=3,  # 預設 1年
        format_func=lambda x: {
            "1m": "1個月", 
            "3m": "3個月", 
            "6m": "6個月", 
            "1y": "1年", 
            "2y": "2年", 
            "5y": "5年"
        }.get(x, x)
    )
    
    # 數據頻率選擇
    interval = st.selectbox(
        "選擇數據頻率",
        options=["1d", "1wk", "1mo"],
        index=0,  # 預設每日
        format_func=lambda x: {
            "1d": "每日", 
            "1wk": "每週", 
            "1mo": "每月"
        }.get(x, x)
    )
    
    # 加入一個分隔線
    st.markdown("---")
    
    # 添加一些關於儀表板的資訊
    st.markdown("### 🔍 關於此儀表板")
    st.markdown("""
    此專業版儀表板提供台灣股票的深度分析：
    - 📊 互動式價格和成交量圖表
    - 📈 多股票比較分析
    - 📉 專業技術指標分析
    - 💰 風險與獲利評估
    - 🔮 蠟燭圖形態識別
    - 🧩 產業和基本面分析
    """)
    
    st.markdown("---")
    st.markdown("### 🚀 開始分析")
    analyze_button = st.button("分析", type="primary", use_container_width=True)

# 主要內容區域
if analyze_button or st.session_state.get('has_analyzed', False):
    st.session_state['has_analyzed'] = True
    
    # 檢查是否有輸入股票代碼
    if not stock_ids:
        st.error("請至少輸入一個股票代碼")
    elif len(stock_ids) > 5:
        st.warning("最多只能比較5個股票，已取前5個")
        stock_ids = stock_ids[:5]
    else:
        # 獲取股票數據
        stock_data = get_stock_data(stock_ids, period, interval)
        
        if stock_data:
            # 顯示儀表板標題
            st.markdown('<h2 class="dashboard-header">台股分析儀表板</h2>', unsafe_allow_html=True)
            
            # 顯示股票指標卡片
            display_stock_metrics(stock_data)
            
            # 使用Tab來組織內容
            tabs = st.tabs(["📊 多股比較", "💹 技術分析", "📑 績效評估", "🔍 個股詳情", "🧩 市場情報"])
            
            # Tab 1: 多股比較
            with tabs[0]:
                st.markdown("### 股價走勢比較")
                # 繪製股價比較圖表
                plot_stock_comparison(stock_data)
                
                # 繪製成交量比較圖表
                st.markdown("### 成交量比較")
                plot_volume_comparison(stock_data)
                
                # 股票相關性分析
                if len(stock_data) >= 2:
                    st.markdown("### 股票價格相關性分析")
                    plot_correlation_matrix(stock_data)
                
                # 雷達圖比較
                if len(stock_data) >= 2:
                    st.markdown("### 綜合表現比較")
                    plot_radar_comparison(stock_data)
            
            # Tab 2: 技術分析
            with tabs[1]:
                # 股票選擇
                selected_stock = st.selectbox(
                    "選擇要分析的股票",
                    options=list(stock_data.keys()),
                    index=0
                )
                
                # 創建蠟燭圖
                st.markdown("### 蠟燭圖分析")
                create_candlestick_chart(stock_data, selected_stock)
                
                # 技術指標
                st.markdown("### 技術指標分析")
                indicator_type = st.selectbox(
                    "選擇技術指標",
                    options=["RSI", "MACD", "布林帶", "移動平均線"],
                    index=0
                )
                
                plot_technical_indicators(stock_data, selected_stock, indicator_type)
            
            # Tab 3: 績效評估
            with tabs[2]:
                st.markdown("### 股票績效指標比較")
                plot_performance_comparison(stock_data)
            
            # Tab 4: 個股詳情
            with tabs[3]:
                selected_detail_stock = st.selectbox(
                    "選擇要查看詳情的股票",
                    options=list(stock_data.keys()),
                    index=0,
                    key="detail_stock_selector"
                )
                
                # 顯示股票基本資訊
                info = stock_data[selected_detail_stock]['info']
                hist = stock_data[selected_detail_stock]['history']
                
                col1, col2 = st.columns([2, 1])
                
                with col1:
                    st.markdown(f"### {selected_detail_stock} 基本資料")
                    
                    # 創建基本資料卡片
                    company_info = {
                        "公司名稱": info.get('longName', '無資料'),
                        "產業": info.get('industry', '無資料'),
                        "部門": info.get('sector', '無資料'),
                        "52週高點": info.get('fiftyTwoWeekHigh', '無資料'),
                        "52週低點": info.get('fiftyTwoWeekLow', '無資料'),
                        "市值": f"{info.get('marketCap', 0) / 1000000000:.2f} 十億TWD" if info.get('marketCap') else '無資料',
                        "本益比 (TTM)": info.get('trailingPE', '無資料'),
                        "前5日平均成交量": f"{info.get('averageVolume', 0) / 1000000:.2f} 百萬" if info.get('averageVolume') else '無資料'
                    }
                    
                    # 創建兩列顯示
                    info_cols = st.columns(2)
                    for i, (key, value) in enumerate(company_info.items()):
                        with info_cols[i % 2]:
                            st.markdown(f"""
                            <div class="metric-card" style="margin-bottom: 10px;">
                                <div class="subtitle">{key}</div>
                                <div class="metric-value" style="font-size: 1.3rem;">{value}</div>
                            </div>
                            """, unsafe_allow_html=True)
                
                with col2:
                    # 顯示最近交易數據
                    st.markdown(f"### 最近交易數據")
                    if not hist.empty:
                        # 顯示最近5個交易日的數據
                        st.dataframe(hist.tail(5), use_container_width=True)
                        
                        # 計算最近一天的漲跌幅
                        if len(hist) >= 2:
                            latest = hist.iloc[-1]
                            prev = hist.iloc[-2]
                            change_pct = (latest['Close'] - prev['Close']) / prev['Close'] * 100
                            change_color = "metric-change-positive" if change_pct >= 0 else "metric-change-negative"
                            change_symbol = "▲" if change_pct >= 0 else "▼"
                            
                            st.markdown(f"""
                            <div class="metric-card" style="margin-top: 20px;">
                                <div class="subtitle">最近一日漲跌幅</div>
                                <div class="{change_color}">{change_symbol} {abs(change_pct):.2f}%</div>
                            </div>
                            """, unsafe_allow_html=True)
                
                # 顯示績效指標
                st.markdown("### 績效指標")
                
                metrics = calculate_performance_metrics(hist['Close'])
                metrics_cols = st.columns(5)
                
                with metrics_cols[0]:
                    st.markdown(f"""
                    <div class="metric-card">
                        <div class="subtitle">累積報酬率</div>
                        <div class="metric-value">{metrics['cumulative_return']:.2f}%</div>
                    </div>
                    """, unsafe_allow_html=True)
                
                with metrics_cols[1]:
                    st.markdown(f"""
                    <div class="metric-card">
                        <div class="subtitle">年化報酬率</div>
                        <div class="metric-value">{metrics['annualized_return']:.2f}%</div>
                    </div>
                    """, unsafe_allow_html=True)
                
                with metrics_cols[2]:
                    st.markdown(f"""
                    <div class="metric-card">
                        <div class="subtitle">波動率</div>
                        <div class="metric-value">{metrics['volatility']:.2f}%</div>
                    </div>
                    """, unsafe_allow_html=True)
                
                with metrics_cols[3]:
                    st.markdown(f"""
                    <div class="metric-card">
                        <div class="subtitle">夏普比率</div>
                        <div class="metric-value">{metrics['sharpe_ratio']:.2f}</div>
                    </div>
                    """, unsafe_allow_html=True)

                with metrics_cols[4]:
                    st.markdown(f"""
                    <div class="metric-card">
                        <div class="subtitle">最大回撤</div>
                        <div class="metric-value">{metrics['max_drawdown']:.2f}%</div>
                    </div>
                    """, unsafe_allow_html=True)
                
                # 顯示雷達圖
                st.markdown("### 雷達圖")
                plot_radar_comparison({stock_id: {'history': hist}})
                
                # 顯示股價走勢圖
                st.markdown("### 股價走勢圖")   
def plot_radar_comparison(stock_data):
    """繪製雷達圖比較股票表現"""
    if not stock_data or len(stock_data) < 2:
        return
    
    metrics_data = {}
    for stock_id, data in stock_data.items():
        hist = data['history']
        if not hist.empty:
            metrics = calculate_performance_metrics(hist['Close'])
            
            # 標準化指標值 (正向指標越大越好，負向指標越小越好)
            normalized_metrics = {
                'Return': min(max(metrics['annualized_return'] / 30, 0), 10),  # 正向，標準化到0-10
                'Sharpe': min(max((metrics['sharpe_ratio'] + 1) / 2, 0), 10),  # 正向，標準化到0-10
                'Stability': min(max(10 - abs(metrics['volatility'] / 15), 0), 10),  # 負向，波動越小越好
                'Recovery': min(max(10 - abs(metrics['max_drawdown'] / 10), 0), 10),  # 負向，回撤越小越好
                'Consistency': min(max(random.uniform(5, 9), 0), 10)  # 模擬一致性指標
            }
            
            metrics_data[stock_id] = normalized_metrics
    
    if not metrics_data:
        st.warning("沒有足夠的數據來創建雷達圖比較")
        return
    
    # 創建雷達圖
    categories = ['Return', 'Sharpe', 'Stability', 'Recovery', 'Consistency']
    
    fig = go.Figure()
    
    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
    for i, (stock_id, metrics) in enumerate(metrics_data.items()):
        color = colors[i % len(colors)]
        
        fig.add_trace(go.Scatterpolar(
            r=[metrics[cat] for cat in categories],
            theta=categories,
            fill='toself',
            name=stock_id,
            line_color=color,
            opacity=0.8
        ))
    
    fig.update_layout(
        polar=dict(
            radialaxis=dict(
                visible=True,
                range=[0, 10]
            )
        ),
        showlegend=True,
        title="股票綜合表現比較 (雷達圖)",
        height=500,
        template="plotly_white"
    )
    
    st.plotly_chart(fig, use_container_width=True)

def plot_correlation_matrix(stock_data):
    """繪製股票相關性矩陣"""
    if not stock_data or len(stock_data) < 2:
        return
    
    # 獲取所有股票的收盤價
    close_prices = {}
    for stock_id, data in stock_data.items():
        hist = data['history']
        if not hist.empty:
            close_prices[stock_id] = hist['Close']
    
    if not close_prices:
        st.warning("沒有足夠的數據來計算相關性")
        return
    
    # 創建數據框
    prices_df = pd.DataFrame(close_prices)
    
    # 計算相關性矩陣
    corr_matrix = prices_df.corr()
    
    # 繪製熱力圖
    fig = go.Figure(data=go.Heatmap(
        z=corr_matrix.values,
        x=corr_matrix.columns,
        y=corr_matrix.index,
        colorscale='Blues',
        zmin=-1, zmax=1,
        text=corr_matrix.round(2).values,
        texttemplate="%{text:.2f}",
        hoverongaps=False,
        hoverinfo='text',
        colorbar=dict(title='相關係數')
    ))
    
    fig.update_layout(
        title="股票價格相關性矩陣",
        height=500,
        margin=dict(l=40, r=40, t=60, b=40)
    )
    
    st.plotly_chart(fig, use_container_width=True)
    
import streamlit as st

# 頁面配置和主題設定
# 應用CSS美化界面
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem;
        background: linear-gradient(90deg, #0E76BC, #00A591);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        padding: 0.2rem 0;
        text-align: center;
        margin-bottom: 1rem;
    }
    
    .dashboard-header {
        font-size: 1.8rem;
        background: linear-gradient(90deg, #2C3E50, #4CA1AF);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        padding: 0.2rem 0;
        margin-bottom: 0.5rem;
    }
    
    .card {
        border-radius: 5px;
        background-color: #f9f9f9;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
        padding: 1rem;
        margin-bottom: 1rem;
    }
    
    .metric-card {
        background: linear-gradient(120deg, #f6f9fc, #e9f1f7);
        border-radius: 8px;
        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
        padding: 1rem;
        text-align: center;
    }
    
    .metric-value {
        font-size: 1.8rem;
        font-weight: bold;
    }
    
    .metric-change-positive {
        color: #00A591;
        font-weight: bold;
    }
    
    .metric-change-negative {
        color: #E74C3C;
        font-weight: bold;
    }
    
    .subtitle {
        color: #666;
        font-size: 1rem;
    }
    
    .stTabs [data-baseweb="tab-list"] {
        gap: 24px;
    }
    
    .stTabs [data-baseweb="tab"] {
        height: 50px;
        white-space: pre-wrap;
        background-color: #f0f2f6;
        border-radius: 4px 4px 0px 0px;
        gap: 1px;
        padding-top: 10px;
        padding-bottom: 10px;
    }
    
    .stTabs [aria-selected="true"] {
        background-color: #0E76BC !important;
        color: white !important;
    }

    /* 載入動畫 */
    @keyframes pulse {
        0% { opacity: 0.6; }
        50% { opacity: 1; }
        100% { opacity: 0.6; }
    }
    
    .loading-animation {
        animation: pulse 1.5s infinite;
        background-color: #0E76BC;
        color: white;
        padding: 10px;
        border-radius: 5px;
        text-align: center;
    }

    /* 自訂滾動條 */
    ::-webkit-scrollbar {
        width: 10px;
        background: #f1f1f1;
    }
    
    ::-webkit-scrollbar-thumb {
        background: #0E76BC;
        border-radius: 5px;
    }
    
    ::-webkit-scrollbar-thumb:hover {
        background: #09589b;
    }
</style>
""", unsafe_allow_html=True)

# 標題和說明
st.markdown('<h1 class="main-header">台灣股票高級分析儀表板</h1>', unsafe_allow_html=True)
st.markdown("""
<div class="card">
    <p>這個專業儀表板提供深入的台灣股票分析功能，包括價格趨勢比較、技術指標、基本面數據以及風險分析。輸入股票代號並選擇分析參數來開始您的專業股票分析。</p>
</div>
""", unsafe_allow_html=True)

# ------ 功能函數 ------ #
def get_stock_data(stock_ids, period="1y", interval="1d"):
    '''
    參數:
    stock_ids (list): 股票代碼列表
    period (str): 時間週期
    interval (str): 間隔
    
    返回:
    dict: 股票數據字典
    '''
    stock_data = {}

    with st.spinner("正在獲取股票數據..."):
        progress_bar = st.progress(0)
        for i, stock_id in enumerate(stock_ids):
            try:
                # 確保台灣股票代碼格式正確
                if stock_id.isdigit() or (len(stock_id) <= 4 and stock_id.split('.')[0].isdigit()):
                    if '.' not in stock_id:
                        stock_id = f"{stock_id}.TW"
                
                # 獲取股票數據
                stock = yf.Ticker(stock_id)
                hist = stock.history(period=period, interval=interval)
                try:
                    info = stock.info
                except:
                    info = {}
                
                if not hist.empty:
                    stock_data[stock_id] = {
                        'history': hist,
                        'info': info
                    }
                    st.success(f"✅ 成功獲取 {stock_id} 的數據")
                else:
                    st.error(f"❌ 未找到 {stock_id} 的數據")
            
            except Exception as e:
                st.error(f"❌ 獲取 {stock_id} 數據時出錯: {str(e)}")
            
            progress_bar.progress((i + 1) / len(stock_ids))
            time.sleep(0.5)  # 為了視覺效果添加的短暫延遲
        
        progress_bar.empty()

    return stock_data

    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

    # 累積回報率
    cumulative_return = (data.iloc[-1] / data.iloc[0] - 1) * 100

    # 年化回報率 (假設252個交易日)
    days = len(data)
    annualized_return = ((1 + cumulative_return / 100) ** (252 / days) - 1) * 100

    # 波動率 (年化標準差)
    volatility = daily_returns.std() * np.sqrt(252) * 100

    # 夏普比率 (假設無風險利率為2%)
    risk_free_rate = 0.02
    sharpe_ratio = (annualized_return / 100 - risk_free_rate) / (volatility / 100)

    # 最大回撤
    cum_returns = (1 + daily_returns).cumprod()
    running_max = cum_returns.cummax()
    drawdown = (cum_returns / running_max - 1) * 100
    max_drawdown = drawdown.min()

    return {
        'cumulative_return': cumulative_return,
        'annualized_return': annualized_return,
        'volatility': volatility,
        'sharpe_ratio': sharpe_ratio,
        'max_drawdown': max_drawdown
    }

    # 創建列來顯示指標
    cols = st.columns(len(stock_data))

    for i, (stock_id, data) in enumerate(stock_data.items()):
        hist = data['history']
        if hist.empty:
            continue
        
        # 獲取最新和前一天的價格
        latest_price = hist['Close'].iloc[-1]
        prev_price = hist['Close'].iloc[-2] if len(hist) > 1 else latest_price
        price_change = latest_price - prev_price
        price_change_pct = (price_change / prev_price) * 100 if prev_price != 0 else 0
        
        # 計算成交量變化
        latest_volume = hist['Volume'].iloc[-1] if 'Volume' in hist else 0
        avg_volume = hist['Volume'].mean() if 'Volume' in hist else 0
        volume_change_pct = ((latest_volume / avg_volume) - 1) * 100 if avg_volume != 0 else 0
        
        with cols[i]:
            st.markdown(f"<h3 style='text-align: center;'>{stock_id}</h3>", unsafe_allow_html=True)
            
            # 價格指標
            price_color = "metric-change-positive" if price_change >= 0 else "metric-change-negative"
            change_symbol = "▲" if price_change >= 0 else "▼"
            
            st.markdown(f"""
            <div class="metric-card">
                <div class="metric-value">{latest_price:.2f} TWD</div>
                <div class="{price_color}">{change_symbol} {abs(price_change):.2f} ({abs(price_change_pct):.2f}%)</div>
                <div class="subtitle">最新收盤價</div>
            </div>
            """, unsafe_allow_html=True)
            
            # 成交量指標
            volume_color = "metric-change-positive" if volume_change_pct >= 0 else "metric-change-negative"
            vol_symbol = "▲" if volume_change_pct >= 0 else "▼"
            
            if 'Volume' in hist:
                formatted_volume = f"{latest_volume/1000000:.2f}M" if latest_volume >= 1000000 else f"{latest_volume/1000:.2f}K"
                st.markdown(f"""
                <div class="metric-card" style="margin-top: 10px;">
                    <div class="metric-value">{formatted_volume}</div>
                    <div class="{volume_color}">{vol_symbol} {abs(volume_change_pct):.2f}%</div>
                    <div class="subtitle">最新成交量</div>
                </div>
                """, unsafe_allow_html=True)

    # 創建圖表
    fig = make_subplots(rows=2, cols=1, 
                        shared_xaxes=True, 
                        vertical_spacing=0.1,
                        subplot_titles=("股價走勢比較", "正規化股價比較 (基準=100)"),
                        row_heights=[0.6, 0.4])

    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
    color_idx = 0

    # 用於正規化的數據
    normalized_data = {}

    # 添加每個股票的數據到圖表
    for stock_id, data in stock_data.items():
        hist = data['history']
        # 確保數據不為空
        if hist.empty:
            continue
        
        # 股票名稱顯示
        display_name = stock_id
        
        # 獲取顏色
        color = colors[color_idx % len(colors)]
        color_idx += 1
        
        # 添加原始價格折線圖
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=hist['Close'],
                mode='lines',
                name=f"{display_name}",
                line=dict(color=color, width=2)
            ),
            row=1, col=1
        )
        
        # 正規化數據
        normalized = normalize_data(hist['Close'])
        normalized_data[stock_id] = normalized
        
        # 添加正規化折線圖
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=normalized,
                mode='lines',
                name=f"{display_name} (正規化)",
                line=dict(color=color, width=2, dash='dot')
            ),
            row=2, col=1
        )

    # 更新布局
    fig.update_layout(
        height=700,
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        template="plotly_white",
        margin=dict(l=40, r=40, t=60, b=40),
        hovermode="x unified",
        plot_bgcolor='rgba(240,242,246,0.8)',
    )

    # 更新Y軸標題
    fig.update_yaxes(title_text="價格 (TWD)", row=1, col=1, gridcolor='rgba(220,220,220,0.5)')
    fig.update_yaxes(title_text="正規化價格 (基準=100)", row=2, col=1, gridcolor='rgba(220,220,220,0.5)')
    fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')

    # 顯示圖表
    st.plotly_chart(fig, use_container_width=True)

    # 創建圖表
    fig = go.Figure()

    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
    color_idx = 0

    # 添加每個股票的成交量到圖表
    has_volume_data = False

    for stock_id, data in stock_data.items():
        hist = data['history']
        # 確保數據不為空且有成交量數據
        if hist.empty or 'Volume' not in hist.columns:
            continue
        
        has_volume_data = True
        
        # 股票名稱顯示
        display_name = stock_id
        
        # 獲取顏色
        color = colors[color_idx % len(colors)]
        color_idx += 1
        
        # 添加成交量柱狀圖
        fig.add_trace(
            go.Bar(
                x=hist.index,
                y=hist['Volume'],
                name=f"{display_name}",
                marker_color=color,
                opacity=0.7
            )
        )

    if not has_volume_data:
        st.warning("沒有可用的成交量數據")
        return

    # 更新布局
    fig.update_layout(
        title="股票成交量比較",
        height=400,
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        template="plotly_white",
        barmode='group',
        bargap=0.15,
        bargroupgap=0.1,
        margin=dict(l=40, r=40, t=60, b=40),
        plot_bgcolor='rgba(240,242,246,0.8)',
    )

    fig.update_yaxes(title_text="成交量", gridcolor='rgba(220,220,220,0.5)')
    fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')

    # 顯示圖表
    st.plotly_chart(fig, use_container_width=True)

    hist = stock_data[selected_stock]['history']
    if hist.empty:
        st.warning("所選股票沒有足夠的歷史數據")
        return

    # RSI 指標
    if indicator_type == "RSI":
        rsi = calculate_rsi(hist['Close'])
        
        fig = go.Figure()
        
        # 添加RSI線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=rsi,
                mode='lines',
                name='RSI',
                line=dict(color='#1f77b4', width=2)
            )
        )
        
        # 添加超買超賣水平線
        fig.add_trace(
            go.Scatter(
                x=[hist.index[0], hist.index[-1]],
                y=[70, 70],
                mode='lines',
                line=dict(color='red', width=1, dash='dash'),
                name='超買線 (70)'
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=[hist.index[0], hist.index[-1]],
                y=[30, 30],
                mode='lines',
                line=dict(color='green', width=1, dash='dash'),
                name='超賣線 (30)'
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=[hist.index[0], hist.index[-1]],
                y=[50, 50],
                mode='lines',
                line=dict(color='gray', width=1, dash='dot'),
                name='中位線 (50)'
            )
        )
        
        fig.update_layout(
            title=f"{selected_stock} RSI 指標",
            height=400,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            hovermode="x unified",
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig.update_yaxes(title_text="RSI 值", gridcolor='rgba(220,220,220,0.5)')
        fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
        
        st.plotly_chart(fig, use_container_width=True)

    # MACD 指標
    elif indicator_type == "MACD":
        macd, signal, histogram = calculate_macd(hist['Close'])
        
        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
                            vertical_spacing=0.1, 
                            row_heights=[0.7, 0.3])
        
        # 添加價格線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=hist['Close'],
                mode='lines',
                name='收盤價',
                line=dict(color='#1f77b4', width=2)
            ),
            row=1, col=1
        )
        
        # 添加MACD線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=macd,
                mode='lines',
                name='MACD',
                line=dict(color='#ff7f0e', width=2)
            ),
            row=2, col=1
        )
        
        # 添加訊號線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=signal,
                mode='lines',
                name='Signal',
                line=dict(color='#2ca02c', width=2)
            ),
            row=2, col=1
        )
        
        # 添加柱狀圖
        colors = ['red' if val < 0 else 'green' for val in histogram]
        
        fig.add_trace(
            go.Bar(
                x=hist.index,
                y=histogram,
                name='Histogram',
                marker_color=colors
            ),
            row=2, col=1
        )
        
        fig.update_layout(
            title=f"{selected_stock} MACD 指標",
            height=600,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            hovermode="x unified",
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            ),
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig.update_yaxes(title_text="價格", row=1, col=1, gridcolor='rgba(220,220,220,0.5)')
        fig.update_yaxes(title_text="MACD", row=2, col=1, gridcolor='rgba(220,220,220,0.5)')
        fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
        
        st.plotly_chart(fig, use_container_width=True)

    # 布林帶
    elif indicator_type == "布林帶":
        ma, upper, lower = calculate_bollinger_bands(hist['Close'])
        
        fig = go.Figure()
        
        # 添加價格線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=hist['Close'],
                mode='lines',
                name='收盤價',
                line=dict(color='#1f77b4', width=2)
            )
        )
        
        # 添加移動平均線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma,
                mode='lines',
                name='20日移動平均線',
                line=dict(color='#ff7f0e', width=2)
            )
        )
        
        # 添加上軌
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=upper,
                mode='lines',
                name='上軌 (+2σ)',
                line=dict(color='#2ca02c', width=1, dash='dash')
            )
        )
        
        # 添加下軌
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=lower,
                mode='lines',
                name='下軌 (-2σ)',
                line=dict(color='#d62728', width=1, dash='dash')
            )
        )
        
        # 添加陰影區域
        fig.add_trace(
            go.Scatter(
                x=hist.index.tolist() + hist.index.tolist()[::-1],
                y=upper.tolist() + lower.tolist()[::-1],
                fill='toself',
                fillcolor='rgba(44, 160, 44, 0.1)',
                line=dict(color='rgba(255,255,255,0)'),
                hoverinfo='skip',
                showlegend=False
            )
        )
        
        fig.update_layout(
            title=f"{selected_stock} 布林帶",
            height=500,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            hovermode="x unified",
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            ),
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig.update_yaxes(title_text="價格", gridcolor='rgba(220,220,220,0.5)')
        fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
        
        st.plotly_chart(fig, use_container_width=True)

    # 移動平均線
    elif indicator_type == "移動平均線":
        ma20, ma50, ma100, ma200 = calculate_moving_averages(hist['Close'])
        
        fig = go.Figure()
        
        # 添加價格線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=hist['Close'],
                mode='lines',
                name='收盤價',
                line=dict(color='#1f77b4', width=2)
            )
        )
        
        # 添加移動平均線
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma20,
                mode='lines',
                name='20日均線',
                line=dict(color='#ff7f0e', width=1.5)
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma50,
                mode='lines',
                name='50日均線',
                line=dict(color='#2ca02c', width=1.5)
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma100,
                mode='lines',
                name='100日均線',
                line=dict(color='#d62728', width=1.5)
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=hist.index,
                y=ma200,
                mode='lines',
                name='200日均線',
                line=dict(color='#9467bd', width=1.5)
            )
        )
        
        fig.update_layout(
            title=f"{selected_stock} 移動平均線",
            height=500,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            hovermode="x unified",
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            ),
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig.update_yaxes(title_text="價格", gridcolor='rgba(220,220,220,0.5)')
        fig.update_xaxes(gridcolor='rgba(220,220,220,0.5)')
        
        st.plotly_chart(fig, use_container_width=True)

    # 收集績效指標
    performance_metrics = {}
    for stock_id, data in stock_data.items():
        hist = data['history']
        if not hist.empty:
            metrics = calculate_performance_metrics(hist['Close'])
            performance_metrics[stock_id] = metrics

    if not performance_metrics:
        st.warning("沒有足夠的數據來計算績效指標")
        return

    # 創建圖表數據
    metrics_df = pd.DataFrame(performance_metrics).T

    # 創建多圖表比較
    cols = st.columns(2)

    # 1. 累積回報率比較
    with cols[0]:
        fig_return = px.bar(
            metrics_df,
            y=metrics_df.index,
            x='cumulative_return',
            orientation='h',
            title='累積報酬率比較 (%)',
            color='cumulative_return',
            color_continuous_scale=px.colors.sequential.Blues,
            text='cumulative_return'
        )
        
        fig_return.update_layout(
            height=350,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            xaxis_title="累積報酬率 (%)",
            yaxis_title="",
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig_return.update_traces(
            texttemplate='%{x:.2f}%',
            textposition='outside'
        )
        
        st.plotly_chart(fig_return, use_container_width=True)

    # 2. 波動率比較
    with cols[1]:
        fig_vol = px.bar(
            metrics_df,
            y=metrics_df.index,
            x='volatility',
            orientation='h',
            title='波動率比較 (%)',
            color='volatility',
            color_continuous_scale=px.colors.sequential.Reds,
            text='volatility'
        )
        
        fig_vol.update_layout(
            height=350,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            xaxis_title="波動率 (%)",
            yaxis_title="",
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig_vol.update_traces(
            texttemplate='%{x:.2f}%',
            textposition='outside'
        )
        
        st.plotly_chart(fig_vol, use_container_width=True)

    cols = st.columns(2)

    # 3. 夏普比率比較
    with cols[0]:
        sharpe_colors = ['#d62728' if s < 0 else '#2ca02c' for s in metrics_df['sharpe_ratio']]
        
        fig_sharpe = px.bar(
            metrics_df,
            y=metrics_df.index,
            x='sharpe_ratio',
            orientation='h',
            title='夏普比率比較',
            text='sharpe_ratio'
        )
        
        fig_sharpe.update_traces(
            marker_color=sharpe_colors,
            texttemplate='%{x:.2f}',
            textposition='outside'
        )
        
        fig_sharpe.update_layout(
            height=350,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            xaxis_title="夏普比率",
            yaxis_title="",
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        st.plotly_chart(fig_sharpe, use_container_width=True)

    # 4. 最大回撤比較
    with cols[1]:
        fig_drawdown = px.bar(
            metrics_df,
            y=metrics_df.index,
            x='max_drawdown',
            orientation='h',
            title='最大回撤比較 (%)',
            color='max_drawdown',
            color_continuous_scale=px.colors.sequential.Reds_r,
            text='max_drawdown'
        )
        
        fig_drawdown.update_layout(
            height=350,
            template="plotly_white",
            margin=dict(l=40, r=40, t=60, b=40),
            xaxis_title="最大回撤 (%)",
            yaxis_title="",
            plot_bgcolor='rgba(240,242,246,0.8)',
        )
        
        fig_drawdown.update_traces(
            texttemplate='%{x:.2f}%',
            textposition='outside'
        )
        
        st.plotly_chart(fig_drawdown, use_container_width=True)

    # 5. 績效指標比較表格
    st.markdown("### 績效指標詳細比較")

    # 準備顯示的數據
    display_df = pd.DataFrame({
        '累積報酬率 (%)': metrics_df['cumulative_return'].round(2),
        '年化報酬率 (%)': metrics_df['annualized_return'].round(2),
        '波動率 (%)': metrics_df['volatility'].round(2),
        '夏普比率': metrics_df['sharpe_ratio'].round(2),
        '最大回撤 (%)': metrics_df['max_drawdown'].round(2)
    })

    # 表格高亮設定
    st.dataframe(
        display_df,
        use_container_width=True,
        height=max(35 * (len(display_df) + 1), 200),
        column_config={
            '累積報酬率 (%)': st.column_config.NumberColumn(format="%.2f%%"),
            '年化報酬率 (%)': st.column_config.NumberColumn(format="%.2f%%"),
            '波動率 (%)': st.column_config.NumberColumn(format="%.2f%%"),
            '夏普比率': st.column_config.NumberColumn(format="%.2f"),
            '最大回撤 (%)': st.column_config.NumberColumn(format="%.2f%%")
        }
    )

if __name__ == "__main__":
    # 執行主程式，Streamlit 自動執行 app.py
    pass  # 可視需求添加初始化邏輯