app.py

# 系統套件
import os
from datetime import datetime, timedelta

# 數據處理
import pandas as pd
import numpy as np
import yfinance as yf

# Dash & Plotly
from dash import Dash, dcc, html, callback
import dash
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# 台股代號對應表 (移除台指期，因為它現在是獨立區塊)
TAIWAN_STOCKS = {
    '台積電': '2330.TW',
    '聯發科': '2454.TW',
    '鴻海': '2317.TW',
    '台塑': '1301.TW',
    '中華電': '2412.TW',
    '富邦金': '2881.TW',
    '國泰金': '2882.TW',
    '台達電': '2308.TW',
    '統一': '1216.TW',
    '日月光': '2311.TW',
    '長榮': '2306.TW',
    '慧洋-KY': '2637.TW',
    '上銀': '2049.TW',
    '台泥': '1101.TW',
    '譜瑞-KY': '4966.TW',
    '貿聯-KY': '3665.TW'
}

# 產業分類
INDUSTRY_MAPPING = {
    '2330.TW': '半導體',
    '2454.TW': '半導體',
    '2317.TW': '電子組件',
    '1301.TW': '塑膠',
    '2412.TW': '電信',
    '2881.TW': '金融',
    '2882.TW': '金融',
    '2308.TW': '電子',
    '1216.TW': '食品',
    '2311.TW': '半導體',
    '2306.TW': '航運',
    '2637.TW': '散裝航運',
    '2049.TW': '工具機',
    '1101.TW': '營建',
    '4966.TW': '高速傳輸',
    '3665.TW': '連接器'
}

def get_stock_data(symbol, period='1y'):
    """獲取股票資料"""
    try:
        stock = yf.Ticker(symbol)
        data = stock.history(period=period)

        # 如果台指期資料為空，嘗試替代方案
        if data.empty and symbol == 'TXF=F':
            # 嘗試使用台灣50ETF作為替代
            stock = yf.Ticker('0050.TW')
            data = stock.history(period=period)
            if data.empty:
                # 最後嘗試使用加權指數
                stock = yf.Ticker('^TWII')
                data = stock.history(period=period)

        return data
    except:
        return pd.DataFrame()

def create_lstm_dataset(data, time_step=60):
    """建立LSTM訓練資料集"""
    X, y = [], []
    for i in range(time_step, len(data)):
        X.append(data[i-time_step:i, 0])
        y.append(data[i, 0])
    return np.array(X), np.array(y)

def simple_lstm_predict(data, predict_days=5):
    """簡化的LSTM預測模型 (使用統計方法模擬)"""
    if len(data) < 60:
        return None

    # 使用移動平均和趨勢分析來模擬深度學習預測
    prices = data['Close'].values

    # 計算短期和長期移動平均
    ma_short = np.mean(prices[-5:])
    ma_medium = np.mean(prices[-20:])
    ma_long = np.mean(prices[-60:])

    # 計算價格變化趨勢
    recent_trend = np.polyfit(range(20), prices[-20:], 1)[0]
    volatility = np.std(prices[-20:]) / np.mean(prices[-20:])

    # 模擬預測邏輯
    base_change = recent_trend * predict_days
    trend_factor = 1.0

    if ma_short > ma_medium > ma_long:
        trend_factor = 1.02  # 上升趨勢
    elif ma_short < ma_medium < ma_long:
        trend_factor = 0.98  # 下降趨勢
    else:
        trend_factor = 1.0   # 盤整

    # 加入隨機性模擬市場不確定性
    noise_factor = np.random.normal(1, volatility * 0.1)

    predicted_price = prices[-1] * trend_factor + base_change + (prices[-1] * noise_factor * 0.01)
    change_pct = ((predicted_price - prices[-1]) / prices[-1]) * 100

    return {
        'predicted_price': predicted_price,
        'change_pct': change_pct,
        'confidence': max(0.6, 1 - volatility * 2)  # 基於波動率的信心度
    }

def calculate_technical_indicators(df):
    """計算技術指標"""
    if df.empty:
        return df

    # 移動平均線
    df['MA5'] = df['Close'].rolling(window=5).mean()
    df['MA20'] = df['Close'].rolling(window=20).mean()

    # RSI
    delta = df['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
    rs = gain / loss
    df['RSI'] = 100 - (100 / (1 + rs))

    # MACD (12, 26, 9)
    exp1 = df['Close'].ewm(span=12).mean()
    exp2 = df['Close'].ewm(span=26).mean()
    df['MACD'] = exp1 - exp2
    df['MACD_Signal'] = df['MACD'].ewm(span=9).mean()
    df['MACD_Histogram'] = df['MACD'] - df['MACD_Signal']

    # 布林通道 (20日, 2倍標準差)
    df['BB_Middle'] = df['Close'].rolling(window=20).mean()
    bb_std = df['Close'].rolling(window=20).std()
    df['BB_Upper'] = df['BB_Middle'] + (bb_std * 2)
    df['BB_Lower'] = df['BB_Middle'] - (bb_std * 2)
    df['BB_Width'] = df['BB_Upper'] - df['BB_Lower']
    df['BB_Position'] = (df['Close'] - df['BB_Lower']) / (df['BB_Upper'] - df['BB_Lower'])

    # KD指標 (9, 3, 3)
    low_min = df['Low'].rolling(window=9).min()
    high_max = df['High'].rolling(window=9).max()
    rsv = (df['Close'] - low_min) / (high_max - low_min) * 100
    df['K'] = rsv.ewm(com=2).mean()  # com=2 相當於 span=3
    df['D'] = df['K'].ewm(com=2).mean()

    # 威廉指標 %R (14日)
    low_min_14 = df['Low'].rolling(window=14).min()
    high_max_14 = df['High'].rolling(window=14).max()
    df['Williams_R'] = -100 * (high_max_14 - df['Close']) / (high_max_14 - low_min_14)

    return df

def get_business_climate_data():
    """獲取台灣景氣燈號資料"""
    try:
        # 檢查檔案是否存在
        if not os.path.exists('business_climate.csv'):
            print("business_climate.csv 檔案不存在")
            return pd.DataFrame()

        # 讀取CSV檔案，假設列名為 Date 和 Index
        df = pd.read_csv('business_climate.csv')

        # 檢查列名並調整
        if 'Date' not in df.columns:
            # 如果第一列是日期，重新命名
            df.columns = ['Date', 'Index'] if len(df.columns) == 2 else df.columns

        # 轉換日期格式 (處理 YYYY-MM 格式)
        if 'Date' in df.columns:
            try:
                # 如果是 YYYY-MM 格式，轉換為日期
                df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
            except:
                df['Date'] = pd.to_datetime(df['Date'], errors='coerce')

        # 移除日期轉換失敗的行
        df = df.dropna(subset=['Date'])

        print(f"成功讀取景氣燈號資料：{len(df)} 筆記錄")
        return df

    except Exception as e:
        print(f"無法獲取景氣燈號資料: {str(e)}")
        return pd.DataFrame()

def get_pmi_data():
    """獲取台灣 PMI 資料"""
    try:
        # 檢查檔案是否存在
        if not os.path.exists('taiwan_pmi.csv'):
            print("taiwan_pmi.csv 檔案不存在")
            return pd.DataFrame()

        # 讀取CSV檔案
        df = pd.read_csv('taiwan_pmi.csv')

        # 檢查列名並調整 (處理 DATE/INDEX 或其他可能的列名)
        if 'DATE' in df.columns:
            df = df.rename(columns={'DATE': 'Date', 'INDEX': 'Index'})
        elif len(df.columns) == 2:
            df.columns = ['Date', 'Index']

        # 轉換日期格式
        if 'Date' in df.columns:
            try:
                # 如果是 YYYY-MM 格式，轉換為日期
                df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
            except:
                df['Date'] = pd.to_datetime(df['Date'], errors='coerce')

        # 移除日期轉換失敗的行
        df = df.dropna(subset=['Date'])

        print(f"成功讀取 PMI 資料：{len(df)} 筆記錄")
        return df

    except Exception as e:
        print(f"無法獲取 PMI 資料: {str(e)}")
        return pd.DataFrame()

def calculate_volume_profile(df, num_bins=50):
    """
    計算成交量分佈圖 (Volume Profile) 的數據。

    Args:
        df (pd.DataFrame): 包含 'High', 'Low', 'Volume' 欄位的 DataFrame。
        num_bins (int): 分割價格區間的數量。

    Returns:
        tuple: 包含 (bin_edges, volume_per_bin, price_centers) 的 tuple。
               bin_edges: 每個區間的邊界。
               volume_per_bin: 每個區間對應的成交量。
               price_centers: 每個區間的中心價格。
    """
    if df.empty or 'High' not in df.columns or 'Low' not in df.columns or 'Volume' not in df.columns:
        return None, None, None

    # 建立一個包含所有高低點的陣列，用於確定價格範圍
    all_prices = np.concatenate([df['High'].values, df['Low'].values])
    min_price = all_prices.min()
    max_price = all_prices.max()

    price_for_volume = (df['High'] + df['Low'] + df['Close']) / 3
    df_vol_profile = df.copy()
    df_vol_profile['Price_Indicator'] = price_for_volume
    df_vol_profile['Volume'] = df_vol_profile['Volume'] # 確保 Volume 欄位存在

    # 創建直方圖來計算成交量分佈
    # `density=False` 確保我們得到的是實際的成交量總和，而不是密度
    # `bins=num_bins` 設定價格區間的數量
    # `range` 設定價格的最小值和最大值
    hist, bin_edges = np.histogram(df_vol_profile['Price_Indicator'], bins=num_bins, range=(min_price, max_price), weights=df_vol_profile['Volume'])

    # 計算每個區間的中心價格
    price_centers = (bin_edges[:-1] + bin_edges[1:]) / 2

    return bin_edges, hist, price_centers




# 建立 Dash 應用程式
app = dash.Dash(__name__, suppress_callback_exceptions=True)

# 應用程式佈局
app.layout = html.Div([
    html.H1("台股分析儀表板", style={'text-align': 'center', 'margin-bottom': '30px'}),

    # 台指期獨立預測區塊 - 置於頂部
    html.Div([
        html.H2("🤖 AI深度學習預測 - 台指期指數", style={
            'text-align': 'center', 
            'color': '#FFCC22',
            'margin-bottom': '25px'
        }),
        html.Div([
            html.Div([
                html.Label("預測期間:", style={'font-weight': 'bold', 'color': '#FFCC22'}),
                dcc.Dropdown(
                    id='taiex-prediction-period',
                    options=[
                        {'label': '1日後預測', 'value': 1},
                        {'label': '5日後預測', 'value': 5},
                        {'label': '10日後預測', 'value': 10},
                        {'label': '20日後預測', 'value': 20},
                        {'label': '60日後預測', 'value': 60}
                    ],
                    value=5,
                    style={'margin-bottom': '10px', 'color': '#272727'}
                )
            ], style={'width': '30%', 'display': 'inline-block'}),

            html.Div(id='taiex-prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
        ]),

        html.Div([
            dcc.Graph(id='taiex-prediction-chart')
        ], style={'margin-top': '20px'})
    ], style={
        'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
        'padding': '25px',
        'border-radius': '15px',
        'box-shadow': '0 8px 25px rgba(0,0,0,0.15)',
        'color': 'white',
        'margin-bottom': '40px'
    }),

    # 控制面板 (移除台指期選項)
    html.Div([
        html.Div([
            html.Label("選擇股票:"),
            dcc.Dropdown(
                id='stock-dropdown',
                options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()],
                value='2330.TW',  # 預設改為台積電
                style={'margin-bottom': '10px'}
            )
        ], style={'width': '30%', 'display': 'inline-block', 'vertical-align': 'top'}),

        html.Div([
            html.Label("時間範圍:"),
            dcc.Dropdown(
                id='period-dropdown',
                options=[
                    {'label': '1個月', 'value': '1mo'},
                    {'label': '3個月', 'value': '3mo'},
                    {'label': '6個月', 'value': '6mo'},
                    {'label': '1年', 'value': '1y'},
                    {'label': '2年', 'value': '2y'}
                ],
                value='6mo',
                style={'margin-bottom': '10px'}
            )
        ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'}),

        html.Div([
            html.Label("圖表類型:"),
            dcc.Dropdown(
                id='chart-type',
                options=[
                    {'label': '線圖', 'value': 'line'},
                    {'label': '蠟燭圖', 'value': 'candlestick'}
                ],
                value='candlestick',
                style={'margin-bottom': '10px'}
            )
        ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'})
    ], style={'margin-bottom': '30px'}),

    # 股價資訊卡片
    html.Div(id='stock-info-cards', style={'margin-bottom': '30px'}),

    # 主要圖表區域 - 移除RSI圖表
    html.Div([
        # 左側：股價走勢圖
        html.Div([
            html.Div([
                dcc.Graph(id='price-chart')
            ])
        ], style={'width': '65%', 'display': 'inline-block', 'vertical-align': 'top'}),

        # 右側：分析資訊面板
        html.Div([
            html.Div(id='analysis-panel')
        ], style={'width': '33%', 'display': 'inline-block', 'margin-left': '2%', 'vertical-align': 'top'})
        ]),
         # 新增：成交量分佈圖 (Volume Profile)
        html.Div([
            html.H3("📊 成交量分佈圖 (Volume Profile)"),
            dcc.Graph(id='volume-profile-chart')
        ], style={
            'margin-top': '30px',
            'padding': '20px',
            'background': 'white',
            'border-radius': '10px',
            'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'
        }),

    # 技術指標選擇區域
    html.Div([
        html.H3("📊 進階技術指標分析", style={'margin-bottom': '20px'}),
        html.Div([
            html.Label("選擇技術指標:", style={'font-weight': 'bold', 'margin-right': '10px'}),
            dcc.Dropdown(
                id='technical-indicator-selector',
                options=[
                    {'label': 'RSI 相對強弱指標', 'value': 'RSI'},
                    {'label': 'MACD 指數平滑異同移動平均線', 'value': 'MACD'},
                    {'label': '布林通道 Bollinger Bands', 'value': 'BB'},
                    {'label': 'KD 隨機指標', 'value': 'KD'},
                    {'label': '威廉指標 %R', 'value': 'WR'}
                ],
                value='RSI',
                style={'width': '100%'}
            )
        ], style={'margin-bottom': '20px'}),

        html.Div([
            dcc.Graph(id='advanced-technical-chart')
        ])
    ], style={
        'margin-top': '20px',
        'padding': '20px',
        'background': 'white',
        'border-radius': '10px',
        'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'
    }),

    # 成交量圖
    html.Div([
        dcc.Graph(id='volume-chart')
    ], style={'margin-top': '20px'}),

    # 產業分析
    html.Div([
        html.H3("產業表現分析"),
        dcc.Graph(id='industry-analysis')
    ], style={'margin-top': '30px'}),

    # 分析師觀點區域
    html.Div([
        html.H3("📊 分析師觀點與市場解讀", style={'color': '#2E86AB', 'margin-bottom': '20px'}),
        html.Div([
            # 左側：技術分析觀點
            html.Div([
                html.H4("🔍 技術面分析", style={'color': '#A23B72', 'margin-bottom': '15px'}),
                html.Div(id='technical-analysis-text', style={
                    'background': '#f8f9fa',
                    'padding': '15px',
                    'border-radius': '8px',
                    'border-left': '4px solid #A23B72',
                    'min-height': '150px',
                    'font-size': '14px',
                    'line-height': '1.6'
                })
            ], style={'width': '48%', 'display': 'inline-block', 'vertical-align': 'top'}),

            # 右側：基本面分析觀點
            html.Div([
                html.H4("📈 基本面分析", style={'color': '#F18F01', 'margin-bottom': '15px'}),
                html.Div(id='fundamental-analysis-text', style={
                    'background': '#f8f9fa',
                    'padding': '15px',
                    'border-radius': '8px',
                    'border-left': '4px solid #F18F01',
                    'min-height': '150px',
                    'font-size': '14px',
                    'line-height': '1.6'
                })
            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%', 'vertical-align': 'top'})
        ]),

        # 底部：市場展望
        html.Div([
            html.H4("🎯 市場展望與投資建議", style={'color': '#C73E1D', 'margin-bottom': '15px', 'margin-top': '25px'}),
            html.Div(id='market-outlook-text', style={
                'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
                'color': 'white',
                'padding': '20px',
                'border-radius': '10px',
                'min-height': '100px',
                'font-size': '15px',
                'line-height': '1.7',
                'box-shadow': '0 4px 15px rgba(0,0,0,0.1)'
            })
        ])
    ], style={
        'margin-top': '30px',
        'padding': '25px',
        'background': 'white',
        'border-radius': '12px',
        'box-shadow': '0 4px 20px rgba(0,0,0,0.08)',
        'border': '1px solid #e9ecef'
    }),

    # 景氣燈號與 PMI 分析
    html.Div([
        html.H3("景氣燈號與 PMI 分析"),
        html.Div([
            html.Div([
                dcc.Graph(id='business-climate-chart')
            ], style={'width': '48%', 'display': 'inline-block'}),
            html.Div([
                dcc.Graph(id='pmi-chart')
            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '2%'})
        ])
    ], style={'margin-top': '30px'}),

    # 多檔股票比較區域
    html.Div([
        html.H3("📊 多檔股票比較分析", style={'margin-bottom': '20px'}),
        html.Div([
            html.Div([
                html.Label("選擇比較股票（最多5檔）:", style={'font-weight': 'bold'}),
                dcc.Dropdown(
                    id='comparison-stocks',
                    options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()],
                    value=['2330.TW', '2454.TW', '2317.TW'],  # 預設選擇
                    multi=True,
                    style={'margin-bottom': '15px'}
                )
            ], style={'width': '60%', 'display': 'inline-block'}),

            html.Div([
                html.Label("比較期間:", style={'font-weight': 'bold'}),
                dcc.Dropdown(
                    id='comparison-period',
                    options=[
                        {'label': '1個月', 'value': '1mo'},
                        {'label': '3個月', 'value': '3mo'},
                        {'label': '6個月', 'value': '6mo'},
                        {'label': '1年', 'value': '1y'}
                    ],
                    value='3mo'
                )
            ], style={'width': '35%', 'display': 'inline-block', 'margin-left': '5%'})
        ]),

        html.Div([
            html.Div([
                dcc.Graph(id='comparison-chart')
            ], style={'width': '65%', 'display': 'inline-block'}),

            html.Div([
                html.H4("比較結果", style={'color': '#2E86AB'}),
                html.Div(id='comparison-table')
            ], style={'width': '33%', 'display': 'inline-block', 'margin-left': '2%', 'vertical-align': 'top'})
        ])
    ], style={
        'margin-top': '30px',
        'padding': '20px',
        'background': 'white',
        'border-radius': '10px',
        'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'
    }),

    # 新聞情感分析區域（模擬）
    html.Div([
        html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
        html.Div([
            html.Div([
                html.H4("市場情緒指標", style={'color': '#8E44AD'}),
                html.Div(id='sentiment-gauge')
            ], style={'width': '48%', 'display': 'inline-block'}),

            html.Div([
                html.H4("關鍵新聞摘要", style={'color': '#27AE60'}),
                html.Div(id='news-summary', style={
                    'background': '#f8f9fa',
                    'padding': '15px',
                    'border-radius': '8px',
                    'max-height': '200px',
                    'overflow-y': 'auto'
                })
            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%'})
        ])
    ], style={
        'margin-top': '30px',
        'padding': '20px',
        'background': 'white',
        'border-radius': '10px',
        'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'
    })
])

# 台指期獨立預測回調函數 (新版本)
@app.callback(
    [dash.dependencies.Output('taiex-prediction-results', 'children'),
     dash.dependencies.Output('taiex-prediction-chart', 'figure')],
    [dash.dependencies.Input('taiex-prediction-period', 'value')]
)
def update_taiex_prediction(predict_days):
    # 獲取台指期歷史資料
    data = get_stock_data('^TWII', '2y')
    if data.empty:
        return html.Div("無法獲取台指期資料"), {}

    # 執行最終日的預測，用於顯示在結果卡片上
    final_prediction = simple_lstm_predict(data, predict_days)
    if final_prediction is None:
        return html.Div("資料不足，無法進行預測"), {}

    current_price = data['Close'].iloc[-1]
    last_date = data.index[-1]
    predicted_price = final_prediction['predicted_price']
    change_pct = final_prediction['change_pct']
    confidence = final_prediction['confidence']

    # --- 主要修改處：計算預測路徑 ---
    # 1. 定義不同預測天期所包含的中間節點
    prediction_paths = {
        1: [1],
        5: [1, 5],
        10: [1, 5, 10],
        20: [1, 10, 20],
        60: [1, 10, 20, 60]
    }
    intervals_to_predict = prediction_paths.get(predict_days, [predict_days])

    # 2. 準備儲存預測路徑的座標點 (起始點為目前價格)
    prediction_dates = [last_date]
    prediction_prices = [current_price]

    # 3. 循環計算路徑上每個點的預測值
    for days in intervals_to_predict:
        interim_prediction = simple_lstm_predict(data, days)
        if interim_prediction:
            prediction_dates.append(last_date + timedelta(days=days))
            prediction_prices.append(interim_prediction['predicted_price'])
    # --- 修改結束 ---

    # 預測結果卡片 (維持不變)
    color = '#00C851' if change_pct >= 0 else '#FF4444'
    arrow = '📈' if change_pct >= 0 else '📉'

    result_card = html.Div([
        html.H4(f"{predict_days}日後預測結果", style={'margin': '0 0 15px 0', 'color': 'white'}),
        html.Div([
            html.Span(f"{arrow} ", style={'font-size': '24px'}),
            html.Span(f"{change_pct:+.2f}%", style={
                'font-size': '28px',
                'font-weight': 'bold',
                'color': color
            })
        ], style={'margin': '10px 0'}),
        html.P(f"目前價格: {current_price:.2f}", style={'margin': '5px 0'}),
        html.P(f"預測價格: {predicted_price:.2f}", style={'margin': '5px 0'}),
        html.P(f"信心度: {confidence:.1%}", style={'margin': '5px 0', 'font-size': '14px'})
    ], style={
        'background': 'rgba(255,255,255,0.1)',
        'padding': '20px',
        'border-radius': '10px',
        'border': '1px solid rgba(255,255,255,0.2)'
    })

    # 建立預測趨勢圖
    fig = go.Figure()

    # 歷史價格 (最近30天)
    recent_data = data.tail(30)
    fig.add_trace(go.Scatter(
        x=recent_data.index,
        y=recent_data['Close'],
        mode='lines',
        name='歷史價格',
        line=dict(color='#FFA726', width=2)
    ))

    # --- 修改處：使用新的座標點繪製預測線 ---
    # 4. 繪製由多個預測點連接而成的路徑
    fig.add_trace(go.Scatter(
        x=prediction_dates,  # 使用包含多個日期的列表
        y=prediction_prices, # 使用包含多個預測價格的列表
        mode='lines+markers',
        name=f'{predict_days}日預測路徑',
        line=dict(color=color, width=3, dash='dash'),
        marker=dict(size=8)
    ))
    # --- 修改結束 ---

    fig.update_layout(
        title=f'台指期 {predict_days}日預測走勢',
        xaxis_title='日期',
        yaxis_title='指數點位',
        height=350,
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        font=dict(color='white')
    )

    return result_card, fig

# 更新股價資訊卡片
@app.callback(
    dash.dependencies.Output('stock-info-cards', 'children'),
    [dash.dependencies.Input('stock-dropdown', 'value')]
)
def update_stock_info(selected_stock):
    data = get_stock_data(selected_stock, '5d')
    if data.empty:
        return html.Div("無法獲取股票資料")

    current_price = data['Close'].iloc[-1]
    prev_price = data['Close'].iloc[-2] if len(data) > 1 else current_price
    change = current_price - prev_price
    change_pct = (change / prev_price) * 100

    # 找出股票中文名稱
    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]

    color = 'green' if change >= 0 else 'red'

    return html.Div([
        html.Div([
            html.H3(f"{stock_name} ({selected_stock})", style={'margin': '0'}),
            html.H2(f"${current_price:.2f}", style={'margin': '5px 0', 'color': color}),
            html.P(f"{'▲' if change >= 0 else '▼'} {change:+.2f} ({change_pct:+.2f}%)",
                   style={'margin': '0', 'color': color, 'font-weight': 'bold'})
        ], style={
            'background': 'white',
            'padding': '20px',
            'border-radius': '10px',
            'box-shadow': '0 2px 10px rgba(0,0,0,0.1)',
            'display': 'inline-block',
            'margin-right': '20px'
        }),

        html.Div([
            html.H4("今日統計", style={'margin': '0 0 10px 0'}),
            html.P(f"最高: ${data['High'].iloc[-1]:.2f}", style={'margin': '5px 0'}),
            html.P(f"最低: ${data['Low'].iloc[-1]:.2f}", style={'margin': '5px 0'}),
            html.P(f"成交量: {data['Volume'].iloc[-1]:,.0f}", style={'margin': '5px 0'})
        ], style={
            'background': 'white',
            'padding': '20px',
            'border-radius': '10px',
            'box-shadow': '0 2px 10px rgba(0,0,0,0.1)',
            'display': 'inline-block'
        })
    ])

# 更新股價圖表
@app.callback(
    dash.dependencies.Output('price-chart', 'figure'),
    [dash.dependencies.Input('stock-dropdown', 'value'),
     dash.dependencies.Input('period-dropdown', 'value'),
     dash.dependencies.Input('chart-type', 'value')]
)
def update_price_chart(selected_stock, period, chart_type):
    data = get_stock_data(selected_stock, period)
    if data.empty:
        return {}

    data = calculate_technical_indicators(data)
    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]

    if chart_type == 'candlestick':
        fig = go.Figure(data=go.Candlestick(
            x=data.index,
            open=data['Open'],
            high=data['High'],
            low=data['Low'],
            close=data['Close'],
            name=stock_name
        ))
    else:
        fig = px.line(data, y='Close', title=f'{stock_name} 股價走勢')

    # 添加移動平均線
    fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], mode='lines', name='MA5', line=dict(color='orange')))
    fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], mode='lines', name='MA20', line=dict(color='blue')))

    fig.update_layout(
        title=f'{stock_name} 股價走勢',
        xaxis_title='日期',
        yaxis_title='價格 (TWD)',
        height=400
    )

    return fig

# 更新RSI圖表（保持兼容性）
@app.callback(
    dash.dependencies.Output('rsi-chart', 'figure'),
    [dash.dependencies.Input('stock-dropdown', 'value'),
     dash.dependencies.Input('period-dropdown', 'value')]
)
def update_rsi_chart(selected_stock, period):
    data = get_stock_data(selected_stock, period)
    if data.empty:
        return {}

    data = calculate_technical_indicators(data)

    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], mode='lines', name='RSI', line=dict(color='purple', width=2)))
    fig.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="超買線(70)")
    fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="超賣線(30)")
    fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)")

    # 添加超買超賣區域背景
    fig.add_hrect(y0=70, y1=100, fillcolor="red", opacity=0.1, annotation_text="超買區")
    fig.add_hrect(y0=0, y1=30, fillcolor="green", opacity=0.1, annotation_text="超賣區")

    fig.update_layout(
        title='RSI 相對強弱指標',
        xaxis_title='日期',
        yaxis_title='RSI',
        height=400,
        yaxis=dict(range=[0, 100])
    )

    return fig

# 新增：進階技術指標圖表
@app.callback(
    dash.dependencies.Output('advanced-technical-chart', 'figure'),
    [dash.dependencies.Input('technical-indicator-selector', 'value'),
     dash.dependencies.Input('stock-dropdown', 'value'),
     dash.dependencies.Input('period-dropdown', 'value')]
)
def update_advanced_technical_chart(indicator, selected_stock, period):
    data = get_stock_data(selected_stock, period)
    if data.empty:
        return {}

    data = calculate_technical_indicators(data)
    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]

    if indicator == 'RSI':
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], mode='lines', name='RSI', line=dict(color='purple', width=2)))
        fig.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="超買線(70)")
        fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="超賣線(30)")
        fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)")

        fig.add_hrect(y0=70, y1=100, fillcolor="red", opacity=0.1)
        fig.add_hrect(y0=0, y1=30, fillcolor="green", opacity=0.1)

        fig.update_layout(
            title=f'{stock_name} - RSI 相對強弱指標',
            xaxis_title='日期',
            yaxis_title='RSI',
            height=450,
            yaxis=dict(range=[0, 100])
        )

    elif indicator == 'MACD':
        # 建立兩個垂直排列的子圖，並共享X軸
        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
                           vertical_spacing=0.1,  # 子圖間的垂直間距
                           row_heights=[0.7, 0.3], # 上方圖佔70%，下方圖佔30%
                           subplot_titles=('價格走勢', 'MACD 指標')) # 設定子圖標題

        # --- 上方子圖 (row=1)：只繪製價格走勢 ---
        fig.add_trace(go.Scatter(
            x=data.index, 
            y=data['Close'], 
            mode='lines', 
            name='收盤價',
            line=dict(color='black', width=1.5)
        ), row=1, col=1)

        # --- 下方子圖 (row=2)：繪製所有MACD相關指標 ---
        # 1. MACD 快線 (DIF)
        fig.add_trace(go.Scatter(
            x=data.index, 
            y=data['MACD'], 
            mode='lines', 
            name='MACD (快線)',
            line=dict(color='blue', width=2)
        ), row=2, col=1)
        
        # 2. Signal 慢線 (MACD)
        fig.add_trace(go.Scatter(
            x=data.index, 
            y=data['MACD_Signal'], 
            mode='lines', 
            name='Signal (慢線)',
            line=dict(color='red', width=2)
        ), row=2, col=1)
        
        # 3. Histogram 柱狀圖
        colors = ['green' if x >= 0 else 'red' for x in data['MACD_Histogram']]
        fig.add_trace(go.Bar(
            x=data.index, 
            y=data['MACD_Histogram'], 
            name='MACD柱狀圖',
            marker_color=colors
        ), row=2, col=1)

        # 在MACD子圖中添加一條零軸水平線，方便觀察
        fig.add_hline(y=0, line_dash="dash", line_color="gray", row=2, col=1)

        # 更新整個圖表的佈局
        fig.update_layout(
            title_text=f'{stock_name} - MACD 指數平滑異同移動平均線',
            height=550, # 可以適當增加圖表高度以容納兩個子圖
            legend_title_text='圖例',
            showlegend=True # 確保圖例顯示
        )
        # 隱藏柱狀圖的圖例，因為顏色已經表達了正負值
        fig.update_traces(showlegend=False, selector=dict(type='bar'))

    elif indicator == 'BB':
        fig = go.Figure()

        # 價格線
        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價',
                                line=dict(color='black', width=2)))

        # 布林通道上軌
        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], mode='lines', name='上軌',
                                line=dict(color='red', width=1, dash='dash')))

        # 布林通道中軌
        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Middle'], mode='lines', name='中軌(MA20)',
                                line=dict(color='blue', width=1)))

        # 布林通道下軌
        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], mode='lines', name='下軌',
                                line=dict(color='green', width=1, dash='dash')))

        # 填充通道區域
        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], mode='lines',
                                line=dict(color='rgba(0,0,0,0)'), showlegend=False))
        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], mode='lines',
                                fill='tonexty', fillcolor='rgba(173,216,230,0.2)',
                                line=dict(color='rgba(0,0,0,0)'), name='布林通道', showlegend=False))

        fig.update_layout(
            title=f'{stock_name} - 布林通道 (20日, 2σ)',
            xaxis_title='日期',
            yaxis_title='價格 (TWD)',
            height=450
        )

    elif indicator == 'KD':
        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
                           vertical_spacing=0.1,
                           row_heights=[0.6, 0.4],
                           subplot_titles=('價格走勢', 'KD指標'))

        # 上方：價格線
        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價',
                                line=dict(color='black', width=1)), row=1, col=1)

        # 下方：KD線
        fig.add_trace(go.Scatter(x=data.index, y=data['K'], mode='lines', name='K線',
                                line=dict(color='blue', width=2)), row=2, col=1)
        fig.add_trace(go.Scatter(x=data.index, y=data['D'], mode='lines', name='D線',
                                line=dict(color='red', width=2)), row=2, col=1)

        # KD指標參考線
        fig.add_hline(y=80, line_dash="dash", line_color="red", annotation_text="超買線(80)", row=2, col=1)
        fig.add_hline(y=20, line_dash="dash", line_color="green", annotation_text="超賣線(20)", row=2, col=1)
        fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)", row=2, col=1)

        # 超買超賣區域
        fig.add_hrect(y0=80, y1=100, fillcolor="red", opacity=0.1, row=2, col=1)
        fig.add_hrect(y0=0, y1=20, fillcolor="green", opacity=0.1, row=2, col=1)

        fig.update_layout(
            title=f'{stock_name} - KD 隨機指標 (9,3,3)',
            height=500
        )
        fig.update_yaxes(range=[0, 100], row=2, col=1)

    elif indicator == 'WR':
        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
                           vertical_spacing=0.1,
                           row_heights=[0.6, 0.4],
                           subplot_titles=('價格走勢', '威廉指標 %R'))

        # 上方：價格線
        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價',
                                line=dict(color='black', width=1)), row=1, col=1)

        # 下方：威廉指標
        fig.add_trace(go.Scatter(x=data.index, y=data['Williams_R'], mode='lines', name='威廉%R',
                                line=dict(color='purple', width=2)), row=2, col=1)

        # 威廉指標參考線
        fig.add_hline(y=-20, line_dash="dash", line_color="red", annotation_text="超買線(-20)", row=2, col=1)
        fig.add_hline(y=-80, line_dash="dash", line_color="green", annotation_text="超賣線(-80)", row=2, col=1)
        fig.add_hline(y=-50, line_dash="dot", line_color="gray", annotation_text="中線(-50)", row=2, col=1)

        # 超買超賣區域
        fig.add_hrect(y0=-20, y1=0, fillcolor="red", opacity=0.1, row=2, col=1)
        fig.add_hrect(y0=-100, y1=-80, fillcolor="green", opacity=0.1, row=2, col=1)

        fig.update_layout(
            title=f'{stock_name} - 威廉指標 %R (14日)',
            height=500
        )
        fig.update_yaxes(range=[-100, 0], row=2, col=1)

    return fig

# 更新成交量圖表
@app.callback(
    dash.dependencies.Output('volume-chart', 'figure'),
    [dash.dependencies.Input('stock-dropdown', 'value'),
     dash.dependencies.Input('period-dropdown', 'value')]
)
def update_volume_chart(selected_stock, period):
    data = get_stock_data(selected_stock, period)
    if data.empty:
        return {}

    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]

    fig = px.bar(data, y='Volume', title=f'{stock_name} 成交量')
    fig.update_layout(
        xaxis_title='日期',
        yaxis_title='成交量',
        height=300
    )

    return fig

# 更新產業分析圖表
@app.callback(
    dash.dependencies.Output('industry-analysis', 'figure'),
    [dash.dependencies.Input('stock-dropdown', 'value')]
)
def update_industry_analysis(selected_stock):
    # 獲取多檔股票資料進行產業比較
    industry_data = []

    for symbol in list(TAIWAN_STOCKS.values())[:10]:  # 取前10檔做示範
        data = get_stock_data(symbol, '1mo')
        if not data.empty:
            stock_name = [name for name, symbol_code in TAIWAN_STOCKS.items() if symbol_code == symbol][0]
            latest_price = data['Close'].iloc[-1]
            first_price = data['Close'].iloc[0]
            return_pct = ((latest_price - first_price) / first_price) * 100

            industry_data.append({
                '股票': stock_name,
                '代碼': symbol,
                '月報酬率(%)': return_pct,
                '產業': INDUSTRY_MAPPING.get(symbol, '其他')
            })

    if not industry_data:
        return {}

    df_industry = pd.DataFrame(industry_data)

    # 建立產業表現圓餅圖
    fig = px.pie(df_industry, values='月報酬率(%)', names='股票',
                title='各股票月報酬率比較',
                color_discrete_sequence=px.colors.qualitative.Set3)

    fig.update_layout(height=400)
    return fig

# 新增：更新景氣燈號圖表
@app.callback(
    dash.dependencies.Output('business-climate-chart', 'figure'),
    [dash.dependencies.Input('stock-dropdown', 'value')]  # 雖然不會影響圖表，但需要觸發
)
def update_business_climate_chart(selected_stock):
    df = get_business_climate_data()

    if df.empty:
        # 如果沒有資料，顯示提示圖表
        fig = go.Figure()
        fig.add_annotation(
            x=0.5, y=0.5,
            text="無法載入景氣燈號資料<br>請確認 business_climate.csv 檔案是否存在",
            xref="paper", yref="paper",
            showarrow=False,
            font=dict(size=14)
        )
        fig.update_layout(
            title="台灣景氣燈號",
            height=300,
            showlegend=False
        )
        return fig

    # 定義燈號顏色
    def get_light_color(score):
        if score >= 32:
            return 'red'      # 紅燈
        elif score >= 24:
            return 'orange'   # 黃紅燈
        elif score >= 17:
            return 'yellow'   # 黃燈
        elif score >= 10:
            return 'lightgreen'  # 黃藍燈
        else:
            return 'blue'     # 藍燈

    # 為每個點設定顏色
    colors = [get_light_color(score) for score in df['Index']]

    fig = go.Figure()

    fig.add_trace(go.Scatter(
        x=df['Date'],
        y=df['Index'],
        mode='lines+markers',
        name='景氣燈號',
        line=dict(color='darkblue', width=2),
        marker=dict(
            size=8,
            color=colors,
            line=dict(width=2, color='darkblue')
        )
    ))

    # 添加燈號區間線
    fig.add_hline(y=32, line_dash="dash", line_color="red", annotation_text="紅燈(32)")
    fig.add_hline(y=24, line_dash="dash", line_color="orange", annotation_text="黃紅燈(24)")
    fig.add_hline(y=17, line_dash="dash", line_color="yellow", annotation_text="黃燈(17)")
    fig.add_hline(y=10, line_dash="dash", line_color="lightgreen", annotation_text="黃藍燈(10)")

    fig.update_layout(
        title="台灣景氣燈號走勢",
        xaxis_title='日期',
        yaxis_title='燈號分數',
        height=300,
        yaxis=dict(range=[0, 40])
    )

    return fig

# 新增：更新分析師觀點
@app.callback(
    [dash.dependencies.Output('technical-analysis-text', 'children'),
     dash.dependencies.Output('fundamental-analysis-text', 'children'),
     dash.dependencies.Output('market-outlook-text', 'children')],
    [dash.dependencies.Input('stock-dropdown', 'value'),
     dash.dependencies.Input('period-dropdown', 'value')]
)
def update_analysis_text(selected_stock, period):
    # 獲取股票資料進行分析
    data = get_stock_data(selected_stock, period)
    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]

    if data.empty:
        return "無法獲取資料進行分析", "無法獲取資料進行分析", "無法獲取資料進行分析"

    # 計算技術指標
    data = calculate_technical_indicators(data)

    # 基本數據
    current_price = data['Close'].iloc[-1]
    price_change = ((current_price - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
    volume_avg = data['Volume'].mean()
    recent_volume = data['Volume'].iloc[-5:].mean()
    rsi_current = data['RSI'].iloc[-1] if not pd.isna(data['RSI'].iloc[-1]) else 50

    # 新增技術指標數據
    macd_current = data['MACD'].iloc[-1] if not pd.isna(data['MACD'].iloc[-1]) else 0
    macd_signal_current = data['MACD_Signal'].iloc[-1] if not pd.isna(data['MACD_Signal'].iloc[-1]) else 0
    bb_position = data['BB_Position'].iloc[-1] if not pd.isna(data['BB_Position'].iloc[-1]) else 0.5
    k_current = data['K'].iloc[-1] if not pd.isna(data['K'].iloc[-1]) else 50
    d_current = data['D'].iloc[-1] if not pd.isna(data['D'].iloc[-1]) else 50

    # 技術面分析
    technical_text = html.Div([
        html.P([
            html.Strong("價格趨勢："),
            f"近期{period}期間內，{stock_name}呈現",
            html.Span(f"{'上漲' if price_change > 5 else '下跌' if price_change < -5 else '盤整'}",
                     style={'color': 'green' if price_change > 5 else 'red' if price_change < -5 else 'orange', 'font-weight': 'bold'}),
            f"走勢，累計變動{price_change:+.1f}%。"
        ]),
        html.P([
            html.Strong("RSI指標："),
            f"目前為{rsi_current:.1f}，",
            html.Span(
                "處於超買區間" if rsi_current > 70 else "處於超賣區間" if rsi_current < 30 else "在正常範圍內",
                style={'color': 'red' if rsi_current > 70 else 'green' if rsi_current < 30 else 'blue', 'font-weight': 'bold'}
            ),
            "。"
        ]),
        html.P([
            html.Strong("MACD指標："),
            f"MACD線({macd_current:.3f})",
            html.Span(
                "高於" if macd_current > macd_signal_current else "低於",
                style={'color': 'green' if macd_current > macd_signal_current else 'red', 'font-weight': 'bold'}
            ),
            f"信號線({macd_signal_current:.3f})，",
            f"顯示{'多頭' if macd_current > macd_signal_current else '空頭'}格局。"
        ]),
        html.P([
            html.Strong("布林通道："),
            f"股價位於通道",
            html.Span(
                "上半部" if bb_position > 0.8 else "下半部" if bb_position < 0.2 else "中段",
                style={'color': 'red' if bb_position > 0.8 else 'green' if bb_position < 0.2 else 'blue', 'font-weight': 'bold'}
            ),
            f"({bb_position*100:.0f}%)，",
            f"{'壓力較大' if bb_position > 0.8 else '支撐較強' if bb_position < 0.2 else '整理格局'}。"
        ]),
        html.P([
            html.Strong("KD指標："),
            f"K值({k_current:.1f})",
            html.Span(
                "高於" if k_current > d_current else "低於",
                style={'color': 'green' if k_current > d_current else 'red', 'font-weight': 'bold'}
            ),
            f"D值({d_current:.1f})，",
            html.Span(
                "超買警戒" if k_current > 80 else "超賣關注" if k_current < 20 else "正常區間",
                style={'color': 'red' if k_current > 80 else 'green' if k_current < 20 else 'blue', 'font-weight': 'bold'}
            ),
            "。"
        ]),
        html.P([
            html.Strong("成交量分析："),
            f"近期成交量{'放大' if recent_volume > volume_avg * 1.2 else '萎縮' if recent_volume < volume_avg * 0.8 else '平穩'}，",
            f"顯示市場{'關注度提升' if recent_volume > volume_avg * 1.2 else '觀望氣氛濃厚' if recent_volume < volume_avg * 0.8 else '交投正常'}。"
        ])
    ])

    # 基本面分析
    industry = INDUSTRY_MAPPING.get(selected_stock, '綜合')
    fundamental_text = html.Div([
        html.P([
            html.Strong("產業地位："),
            f"{stock_name}屬於{industry}產業，在產業鏈中具有",
            html.Span("重要地位" if selected_stock in ['2330.TW', '2454.TW', '2317.TW'] else "一定影響力",
                     style={'font-weight': 'bold'}),
            "。"
        ]),
        html.P([
            html.Strong("營運展望："),
            f"考量{industry}產業前景及公司基本面，建議持續關注季報表現及未來指引。"
        ]),
        html.P([
            html.Strong("風險評估："),
            "注意產業週期性變化、國際競爭及法規環境變化等風險因子。"
        ])
    ])

    # 市場展望
    if price_change > 10:
        outlook_tone = "謹慎樂觀"
        outlook_color = "#28a745"
    elif price_change < -10:
        outlook_tone = "保守觀望"
        outlook_color = "#dc3545"
    else:
        outlook_tone = "中性持平"
        outlook_color = "#ffc107"

    market_outlook = html.Div([
        html.P([
            html.Strong("整體評估：", style={'font-size': '16px'}),
            f"基於技術面及基本面分析，對{stock_name}採取",
            html.Span(f"{outlook_tone}", style={'color': outlook_color, 'font-weight': 'bold', 'font-size': '16px'}),
            "態度。"
        ]),
        html.P([
            html.Strong("投資建議："),
            "建議投資人根據自身風險承受能力，採取適當的資產配置策略。短線操作注意技術指標，長線投資關注基本面變化。"
        ]),
        html.P([
            html.Strong("風險提醒："),
            "股票投資具有風險，過去績效不代表未來表現，投資前請詳閱公開說明書並審慎評估。"
        ], style={'font-style': 'italic', 'font-size': '13px'})
    ])

    return technical_text, fundamental_text, market_outlook

# 新增：更新PMI圖表
@app.callback(
    dash.dependencies.Output('pmi-chart', 'figure'),
    [dash.dependencies.Input('stock-dropdown', 'value')]  # 雖然不會影響圖表，但需要觸發
)
def update_pmi_chart(selected_stock):
    df = get_pmi_data()

    if df.empty:
        # 如果沒有資料，顯示提示圖表
        fig = go.Figure()
        fig.add_annotation(
            x=0.5, y=0.5,
            text="無法載入PMI資料<br>請確認 taiwan_pmi.csv 檔案是否存在",
            xref="paper", yref="paper",
            showarrow=False,
            font=dict(size=14)
        )
        fig.update_layout(
            title="台灣PMI指數",
            height=300,
            showlegend=False
        )
        return fig

    # 定義PMI顏色 (50以上擴張，以下緊縮)
    def get_pmi_color(value):
        return 'green' if value >= 50 else 'red'

    colors = [get_pmi_color(value) for value in df['Index']]

    fig = go.Figure()

    fig.add_trace(go.Scatter(
        x=df['Date'],
        y=df['Index'],
        mode='lines+markers',
        name='PMI指數',
        line=dict(color='darkblue', width=2),
        marker=dict(
            size=8,
            color=colors,
            line=dict(width=2, color='darkblue')
        )
    ))

    # 添加榮枯線
    fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線(50)")

    # 添加背景色區域
    fig.add_hrect(
        y0=50, y1=60,
        fillcolor="lightgreen", opacity=0.2,
        annotation_text="擴張區間", annotation_position="top left"
    )
    fig.add_hrect(
        y0=40, y1=50,
        fillcolor="lightcoral", opacity=0.2,
        annotation_text="緊縮區間", annotation_position="bottom left"
    )

    fig.update_layout(
        title="台灣PMI指數走勢",
        xaxis_title='日期',
        yaxis_title='PMI指數',
        height=300,
        yaxis=dict(range=[35, 60])
    )

    return fig


@app.callback(
    dash.dependencies.Output('volume-profile-chart', 'figure'),
    [dash.dependencies.Input('stock-dropdown', 'value'),
     dash.dependencies.Input('period-dropdown', 'value')]
)
def update_volume_profile_chart(selected_stock, period):
    data = get_stock_data(selected_stock, period)
    if data.empty:
        return {}

    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]

    # 計算 Volume Profile
    bin_edges, volume_per_bin, price_centers = calculate_volume_profile(data, num_bins=50) # 您可以調整 num_bins

    if bin_edges is None or volume_per_bin is None:
        return {}

    # 創建 Volume Profile 圖 (通常是水平長條圖)
    # 我們將其繪製為一個水平的長條圖，成交量在 X 軸，價格在 Y 軸
    fig = go.Figure(go.Bar(
        orientation='h', # 設定為水平長條圖
        y=price_centers,
        x=volume_per_bin,
        name='Volume Profile',
        marker=dict(
            color='rgba(173, 216, 230, 0.6)', # 淡藍色
            line=dict(color='rgba(30, 144, 255, 0.8)', width=1) # 邊框線
        ),
        # 顯示具體的成交量數字
        text=[f'{vol:.0f}' for vol in volume_per_bin],
        textposition='outside', # 將文字顯示在長條圖外面
        hoverinfo='y+text' # hover 時顯示 Y 軸 (價格) 和 text (成交量)
    ))

    # 獲取最高成交量的價格區間 (Point of Control, POC)
    if len(volume_per_bin) > 0:
        poc_volume = np.max(volume_per_bin)
        poc_index = np.argmax(volume_per_bin)
        poc_price = price_centers[poc_index]

        # 在 POC 價格線上添加一條垂直線
        fig.add_vline(x=poc_volume, line_dash="dash", line_color="red",
                      annotation_text=f"POC: ${poc_price:.2f} ({poc_volume:.0f})",
                      annotation_position="top right")

    # 更新圖表佈局
    fig.update_layout(
        title=f'{stock_name} 成交量分佈圖 (Volume Profile)',
        xaxis_title='成交量',
        yaxis_title='價格 (TWD)',
        height=450,
        yaxis=dict(autorange='reversed'), # 讓價格從高到低排列
        bargap=0, # 讓長條圖緊密排列
        plot_bgcolor='rgba(0,0,0,0)', # 透明背景
        hoverlabel=dict(bgcolor="white", font_size=12, font_family="Rockwell")
    )

    return fig    

# 新增：多檔股票比較
@app.callback(
    [dash.dependencies.Output('comparison-chart', 'figure'),
     dash.dependencies.Output('comparison-table', 'children')],
    [dash.dependencies.Input('comparison-stocks', 'value'),
     dash.dependencies.Input('comparison-period', 'value')]
)
def update_comparison_analysis(selected_stocks, period):
    if not selected_stocks:
        return {}, html.Div("請選擇要比較的股票")

    # 限制最多5檔
    selected_stocks = selected_stocks[:5]

    fig = go.Figure()
    comparison_data = []

    for stock in selected_stocks:
        data = get_stock_data(stock, period)
        if not data.empty:
            stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == stock][0]

            # 正規化價格（以期初為基準100）
            normalized_prices = (data['Close'] / data['Close'].iloc[0]) * 100

            fig.add_trace(go.Scatter(
                x=data.index,
                y=normalized_prices,
                mode='lines',
                name=stock_name,
                line=dict(width=2)
            ))

            # 計算績效數據
            total_return = ((data['Close'].iloc[-1] / data['Close'].iloc[0]) - 1) * 100
            volatility = data['Close'].pct_change().std() * np.sqrt(252) * 100  # 年化波動率

            comparison_data.append({
                'name': stock_name,
                'return': total_return,
                'volatility': volatility,
                'current_price': data['Close'].iloc[-1]
            })

    fig.update_layout(
        title=f'股票績效比較 - {period}',
        xaxis_title='日期',
        yaxis_title='相對績效 (基期=100)',
        height=400,
        hovermode='x unified'
    )

    # 建立比較表格
    if comparison_data:
        table_rows = []
        for item in sorted(comparison_data, key=lambda x: x['return'], reverse=True):
            color = 'green' if item['return'] > 0 else 'red'
            table_rows.append(
                html.Tr([
                    html.Td(item['name'], style={'font-weight': 'bold'}),
                    html.Td(f"{item['return']:+.1f}%", style={'color': color, 'font-weight': 'bold'}),
                    html.Td(f"{item['volatility']:.1f}%"),
                    html.Td(f"${item['current_price']:.2f}")
                ])
            )

        table = html.Table([
            html.Thead([
                html.Tr([
                    html.Th("股票", style={'text-align': 'center'}),
                    html.Th("報酬率", style={'text-align': 'center'}),
                    html.Th("波動率", style={'text-align': 'center'}),
                    html.Th("現價", style={'text-align': 'center'})
                ])
            ]),
            html.Tbody(table_rows)
        ], style={
            'width': '100%',
            'border-collapse': 'collapse',
            'font-size': '12px'
        })

        return fig, table

    return fig, html.Div("無可比較資料")

# 新增：市場情緒分析
@app.callback(
    [dash.dependencies.Output('sentiment-gauge', 'children'),
     dash.dependencies.Output('news-summary', 'children')],
    [dash.dependencies.Input('stock-dropdown', 'value')]
)
def update_sentiment_analysis(selected_stock):
    # 模擬情緒指標（實際應用中可接入新聞API或情緒分析服務）
    sentiment_score = np.random.uniform(30, 80)  # 模擬情緒分數 0-100

    # 建立情緒指標圓形圖
    gauge_fig = go.Figure(go.Indicator(
        mode = "gauge+number+delta",
        value = sentiment_score,
        domain = {'x': [0, 1], 'y': [0, 1]},
        title = {'text': "市場情緒指數"},
        delta = {'reference': 50},
        gauge = {
            'axis': {'range': [None, 100]},
            'bar': {'color': "darkblue"},
            'steps': [
                {'range': [0, 30], 'color': "lightcoral"},
                {'range': [30, 70], 'color': "lightgray"},
                {'range': [70, 100], 'color': "lightgreen"}
            ],
            'threshold': {
                'line': {'color': "red", 'width': 4},
                'thickness': 0.75,
                'value': 90
            }
        }
    ))

    gauge_fig.update_layout(height=200, margin=dict(l=20, r=20, t=40, b=20))

    # 模擬新聞摘要
    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]

    news_items = [
        f"📈 {stock_name}獲外資調升目標價，看好後續發展前景",
        f"💼 法人預期{stock_name}下季營收將較上季成長5-10%",
        f"🌐 國際市場波動對{stock_name}影響有限，基本面穩健",
        f"⚡ 產業景氣回溫，{stock_name}受惠程度值得關注",
        f"📊 技術面顯示{stock_name}突破關鍵壓力，短線偏多"
    ]

    news_content = html.Div([
        html.P(news, style={
            'margin': '8px 0',
            'padding': '8px',
            'background': '#e8f4f8',
            'border-radius': '5px',
            'border-left': '3px solid #17a2b8',
            'font-size': '13px'
        }) for news in news_items[:3]  # 顯示前3條
    ])

    return dcc.Graph(figure=gauge_fig), news_content

# 在 Colab 中執行的設定
if __name__ == '__main__':
    # 在執行前先測試檔案讀取
    print("測試檔案讀取...")
    business_data = get_business_climate_data()
    pmi_data = get_pmi_data()

    if not business_data.empty:
        print(f"景氣燈號資料預覽：\n{business_data.head()}")
    if not pmi_data.empty:
        print(f"PMI資料預覽：\n{pmi_data.head()}")

    # 在 Hugging Face Spaces 中執行
    app.run(host="0.0.0.0", port=7860, debug=False)