diff --git "a/app.py" "b/app.py"
--- "a/app.py"
+++ "b/app.py"
@@ -1,1459 +1,1448 @@
-import pandas as pd
-import dash
-from dash import dcc, html, callback
-import plotly.express as px
-import plotly.graph_objects as go
-from datetime import datetime, timedelta
-import yfinance as yf
-import numpy as np
-from plotly.subplots import make_subplots
-import os
-
-# 安裝必要套件 (在 Colab 中執行)
-# !pip install dash plotly yfinance pandas numpy
-
-# 台股代號對應表
-TAIWAN_STOCKS = {
- '台指期': 'TXF=F', # 台指期貨
- '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()
-
-# 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'}),
-
- # 控制面板
- 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='TXF=F',
- 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'}),
-
- # AI預測區塊 (只在選擇台指期時顯示)
- html.Div(id='prediction-section', style={'margin-bottom': '30px'}),
-
- # 主要圖表區域
- html.Div([
- # 左側：股價走勢圖和技術指標
- html.Div([
- html.Div([
- dcc.Graph(id='price-chart')
- ], style={'margin-bottom': '20px'}),
-
- html.Div([
- dcc.Graph(id='rsi-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'})
- ]),
-
- # 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)'
- })
-])
-
-# 更新AI預測區塊
-@app.callback(
- dash.dependencies.Output('prediction-section', 'children'),
- [dash.dependencies.Input('stock-dropdown', 'value')],
- prevent_initial_call=False
-)
-def update_prediction_section(selected_stock):
- if selected_stock != 'TXF=F':
- return html.Div() # 只在選擇台指期時顯示預測功能
-
- return html.Div([
- html.H3("🤖 AI深度學習預測 - 台指期指數", style={'text-align': 'center', 'color': '#FFCC22'}),
- html.Div([
- html.Div([
- html.Label("預測期間:", style={'font-weight': 'bold', 'color': '#FFCC22'}),
- dcc.Dropdown(
- id='prediction-period',
- options=[
- {'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='prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
- ]),
-
- html.Div([
- dcc.Graph(id='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'
- })
-
-# 更新預測結果
-@app.callback(
- [dash.dependencies.Output('prediction-results', 'children'),
- dash.dependencies.Output('prediction-chart', 'figure')],
- [dash.dependencies.Input('prediction-period', 'value'),
- dash.dependencies.Input('stock-dropdown', 'value')]
-)
-def update_prediction(predict_days, selected_stock):
- if selected_stock != 'TXF=F':
- return html.Div(), {}
-
- # 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"), {}
-
- # 執行預測
- prediction = simple_lstm_predict(data, predict_days)
- if prediction is None:
- return html.Div("資料不足，無法進行預測"), {}
-
- current_price = data['Close'].iloc[-1]
- predicted_price = prediction['predicted_price']
- change_pct = prediction['change_pct']
- confidence = prediction['confidence']
-
- # 預測結果卡片
- 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)
- ))
-
- # 預測點
- future_date = recent_data.index[-1] + timedelta(days=predict_days)
- fig.add_trace(go.Scatter(
- x=[recent_data.index[-1], future_date],
- y=[current_price, predicted_price],
- mode='lines+markers',
- name=f'{predict_days}日預測',
- line=dict(color=color, width=3, dash='dash'),
- marker=dict(size=8)
- ))
-
- 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':
- fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
- vertical_spacing=0.1,
- row_heights=[0.7, 0.3],
- subplot_titles=('價格與MACD線', 'MACD柱狀圖'))
-
- # 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)
-
- # MACD線和信號線
- fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], mode='lines', name='MACD',
- line=dict(color='blue', width=2)), row=1, col=1)
- fig.add_trace(go.Scatter(x=data.index, y=data['MACD_Signal'], mode='lines', name='信號線',
- line=dict(color='red', width=2)), row=1, col=1)
-
- # 下方：MACD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)
-
- fig.add_hline(y=0, line_dash="dash", line_color="gray", row=1, col=1)
- fig.add_hline(y=0, line_dash="dash", line_color="gray", row=2, col=1)
-
- fig.update_layout(
- title=f'{stock_name} - MACD 指數平滑異同移動平均線',
- height=500
- )
-
- 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
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')
- if selected_stock == 'TXF=F':
- fundamental_text = html.Div([
- html.P([
- html.Strong("總體經濟："),
- "台指期反映台股整體表現，當前需關注聯準會政策、國際貿易情勢及台灣出口動能。"
- ]),
- html.P([
- html.Strong("產業輪動："),
- "觀察半導體、電子等權重產業表現，以及傳統產業復甦力道。"
- ]),
- html.P([
- html.Strong("資金面："),
- "外資動向、匯率變化及市場流動性為主要觀察重點。"
- ])
- ])
- else:
- 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
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
-
-# 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 Colab 中需要使用以下方式啟動
- app.run(mode='inline', port=8050, debug=True)
-
- # 如果在本地環境執行，使用以下方式
- # app.run_server(debug=True)
-
-# 使用說明：
-"""
-在 Google Colab 中執行此程式的步驟：
-
-1. 首先安裝必要套件：
- !pip install dash plotly yfinance pandas numpy
-
-2. 確保你的 CSV 檔案格式正確：
- - business_climate.csv: 包含 Date 和 Index 兩欄
- - taiwan_pmi.csv: 包含 DATE 和 INDEX 兩欄 (或 Date 和 Index)
- - 日期格式建議為 YYYY-MM (例如: 2023-07)
-
-3. 將 CSV 檔案上傳到 Colab 的檔案區域，確保檔名正確
-
-4. 將上述程式碼貼到 Colab cell 中執行
-
-5. 程式會先測試檔案讀取，然後啟動儀表板
-
-修正內容：
-- 新增了 os 模組來檢查檔案是否存在
-- 改善了 CSV 檔案讀取函數，加入錯誤處理和格式檢查
-- 新增了景氣燈號和 PMI 圖表的回調函數
-- 修正了 dash.Output 的語法（使用 dash.dependencies.Output）
-- 加入了日期格式處理，支援 YYYY-MM 格式
-- 新增了檔案讀取測試功能
-
-如果仍有問題，請檢查：
-1. CSV 檔案是否在正確位置
-2. 檔案格式是否正確
-3. 日期欄位是否包含有效日期
+# 安裝必要套件 (在 Colab 中執行)
+# !pip install dash plotly yfinance pandas numpy
+
+# 台股代號對應表
+TAIWAN_STOCKS = {
+ '台指期': 'TXF=F', # 台指期貨
+ '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()
+
+# 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'}),
+
+ # 控制面板
+ 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='TXF=F',
+ 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'}),
+
+ # AI預測區塊 (只在選擇台指期時顯示)
+ html.Div(id='prediction-section', style={'margin-bottom': '30px'}),
+
+ # 主要圖表區域
+ html.Div([
+ # 左側：股價走勢圖和技術指標
+ html.Div([
+ html.Div([
+ dcc.Graph(id='price-chart')
+ ], style={'margin-bottom': '20px'}),
+
+ html.Div([
+ dcc.Graph(id='rsi-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'})
+ ]),
+
+ # 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)'
+ })
+])
+
+# 更新AI預測區塊
+@app.callback(
+ dash.dependencies.Output('prediction-section', 'children'),
+ [dash.dependencies.Input('stock-dropdown', 'value')],
+ prevent_initial_call=False
+)
+def update_prediction_section(selected_stock):
+ if selected_stock != 'TXF=F':
+ return html.Div() # 只在選擇台指期時顯示預測功能
+
+ return html.Div([
+ html.H3("🤖 AI深度學習預測 - 台指期指數", style={'text-align': 'center', 'color': '#FFCC22'}),
+ html.Div([
+ html.Div([
+ html.Label("預測期間:", style={'font-weight': 'bold', 'color': '#FFCC22'}),
+ dcc.Dropdown(
+ id='prediction-period',
+ options=[
+ {'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='prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
+ ]),
+
+ html.Div([
+ dcc.Graph(id='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'
+ })
+
+# 更新預測結果
+@app.callback(
+ [dash.dependencies.Output('prediction-results', 'children'),
+ dash.dependencies.Output('prediction-chart', 'figure')],
+ [dash.dependencies.Input('prediction-period', 'value'),
+ dash.dependencies.Input('stock-dropdown', 'value')]
+)
+def update_prediction(predict_days, selected_stock):
+ if selected_stock != 'TXF=F':
+ return html.Div(), {}
+
+ # 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"), {}
+
+ # 執行預測
+ prediction = simple_lstm_predict(data, predict_days)
+ if prediction is None:
+ return html.Div("資料不足，無法進行預測"), {}
+
+ current_price = data['Close'].iloc[-1]
+ predicted_price = prediction['predicted_price']
+ change_pct = prediction['change_pct']
+ confidence = prediction['confidence']
+
+ # 預測結果卡片
+ 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)
+ ))
+
+ # 預測點
+ future_date = recent_data.index[-1] + timedelta(days=predict_days)
+ fig.add_trace(go.Scatter(
+ x=[recent_data.index[-1], future_date],
+ y=[current_price, predicted_price],
+ mode='lines+markers',
+ name=f'{predict_days}日預測',
+ line=dict(color=color, width=3, dash='dash'),
+ marker=dict(size=8)
+ ))
+
+ 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':
+ fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
+ vertical_spacing=0.1,
+ row_heights=[0.7, 0.3],
+ subplot_titles=('價格與MACD線', 'MACD柱狀圖'))
+
+ # 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)
+
+ # MACD線和信號線
+ fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], mode='lines', name='MACD',
+ line=dict(color='blue', width=2)), row=1, col=1)
+ fig.add_trace(go.Scatter(x=data.index, y=data['MACD_Signal'], mode='lines', name='信號線',
+ line=dict(color='red', width=2)), row=1, col=1)
+
+ # 下方：MACD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)
+
+ fig.add_hline(y=0, line_dash="dash", line_color="gray", row=1, col=1)
+ fig.add_hline(y=0, line_dash="dash", line_color="gray", row=2, col=1)
+
+ fig.update_layout(
+ title=f'{stock_name} - MACD 指數平滑異同移動平均線',
+ height=500
+ )
+
+ 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
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')
+ if selected_stock == 'TXF=F':
+ fundamental_text = html.Div([
+ html.P([
+ html.Strong("總體經濟："),
+ "台指期反映台股整體表現，當前需關注聯準會政策、國際貿易情勢及台灣出口動能。"
+ ]),
+ html.P([
+ html.Strong("產業輪動："),
+ "觀察半導體、電子等權重產業表現，以及傳統產業復甦力道。"
+ ]),
+ html.P([
+ html.Strong("資金面："),
+ "外資動向、匯率變化及市場流動性為主要觀察重點。"
+ ])
+ ])
+ else:
+ 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
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
+
+# 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 Colab 中需要使用以下方式啟動
+ app.run(mode='inline', port=8050, debug=True)
+
+ # 如果在本地環境執行，使用以下方式
+ # app.run_server(debug=True)
+
+# 使用說明：
+"""
+在 Google Colab 中執行此程式的步驟：
+
+1. 首先安裝必要套件：
+ !pip install dash plotly yfinance pandas numpy
+
+2. 確保你的 CSV 檔案格式正確：
+ - business_climate.csv: 包含 Date 和 Index 兩欄
+ - taiwan_pmi.csv: 包含 DATE 和 INDEX 兩欄 (或 Date 和 Index)
+ - 日期格式建議為 YYYY-MM (例如: 2023-07)
+
+3. 將 CSV 檔案上傳到 Colab 的檔案區域，確保檔名正確
+
+4. 將上述程式碼貼到 Colab cell 中執行
+
+5. 程式會先測試檔案讀取，然後啟動儀表板
+
+修正內容：
+- 新增了 os 模組來檢查檔案是否���在
+- 改善了 CSV 檔案讀取函數，加入錯誤處理和格式檢查
+- 新增了景氣燈號和 PMI 圖表的回調函數
+- 修正了 dash.Output 的語法（使用 dash.dependencies.Output）
+- 加入了日期格式處理，支援 YYYY-MM 格式
+- 新增了檔案讀取測試功能
+
+如果仍有問題，請檢查：
+1. CSV 檔案是否在正確位置
+2. 檔案格式是否正確
+3. 日期欄位是否包含有效日期
"""
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