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| # HUGING_FACE_V4.2(輕量AI版).py - 已整合 XGBoost 模型 | |
| # 系統套件 | |
| import os | |
| from datetime import datetime, timedelta | |
| import google.generativeai as genai | |
| import pandas as pd | |
| import numpy as np | |
| import yfinance as yf | |
| 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 | |
| import re | |
| from bs4 import BeautifulSoup | |
| import requests | |
| import time # 引用 time 模組以處理時間戳 | |
| # ========================= 引用外部模組 START ========================= | |
| # 引用您組員的預測器程式 | |
| from Bert_predict import BertPredictor | |
| # 【修改 1】: 匯入 XGBoostModel 類別 | |
| from model_predictor import XGBoostModel | |
| # ========================== 引用外部模組 END ========================== | |
| # ========================= 全域設定 START ========================= | |
| # 【修改 2】: 將開關設為 True 來啟用您的 XGBoost 模型 | |
| USE_ADVANCED_MODEL = True | |
| # ========================= CACHE 設定 START ========================= | |
| # 分析結果的快取字典 | |
| ANALYSIS_CACHE = {} | |
| # 快取有效時間（秒），例如：8 小時 = 8 * 60 * 60 = 28800 秒 | |
| CACHE_DURATION_SECONDS = 8 * 60 * 60 | |
| # ========================== CACHE 設定 END ========================== | |
| # 【修改 3】: 在應用程式啟動時，預先載入 XGBoost 模型 | |
| try: | |
| print("正在初始化 XGBoost 預測模型...") | |
| xgb_model = XGBoostModel(default_model='xgboost_model') | |
| print("XGBoost 預測模型初始化成功。") | |
| except Exception as e: | |
| print(f"錯誤：XGBoost 預測模型初始化失敗 - {e}") | |
| # 如果模型載入失敗，則強制關閉進階模型開關，退回簡易模式 | |
| USE_ADVANCED_MODEL = False | |
| xgb_model = None | |
| print("警告：已自動切換回簡易統計模型模式。") | |
| # ========================== 全域設定 END ========================== | |
| # 台股代號對應表 | |
| TAIWAN_STOCKS = { | |
| '元大台灣50': '0050.TW', '台積電': '2330.TW', '聯發科': '2454.TW', | |
| '鴻海': '2317.TW', '台達電': '2308.TW', '廣達': '2382.TW', '富邦金': '2881.TW', | |
| '中信金': '2891.TW', '國泰金': '2882.TW', '聯電': '2303.TW', '中華電': '2412.TW', | |
| '玉山金': '2884.TW', '兆豐金': '2886.TW', '日月光投控': '3711.TW', '華碩': '2357.TW', | |
| '統一': '1216.TW', '元大金': '2885.TW', '智邦': '2345.TW', '緯創': '3231.TW', | |
| '聯詠': '3034.TW', '第一金': '2892.TW', '瑞昱': '2379.TW', '緯穎': '6669.TWO', | |
| '永豐金': '2890.TW', '合庫金': '5880.TW', '華南金': '2880.TW', '台光電': '2383.TW', | |
| '世芯-KY': '3661.TWO', '奇鋐': '3017.TW', '凱基金': '2883.TW', '大立光': '3008.TW', | |
| '長榮': '2603.TW', '光寶科': '2301.TW', '中鋼': '2002.TW', '中租-KY': '5871.TW', | |
| '國巨': '2327.TW', '台新金': '2887.TW', '上海商銀': '5876.TW', '台泥': '1101.TW', | |
| '台灣大': '3045.TW', '和碩': '4938.TW', '遠傳': '4904.TW', '和泰車': '2207.TW', | |
| '研華': '2395.TW', '台塑': '1301.TW', '統一超': '2912.TW', '藥華藥': '6446.TWO', | |
| '南亞': '1303.TW', '陽明': '2609.TW', '萬海': '2615.TW', '台塑化': '6505.TW', | |
| '慧洋-KY': '2637.TW', '上銀': '2049.TW', '南亞科': '2408.TW', '旺宏': '2337.TW', | |
| '譜瑞-KY': '4966.TWO', '貿聯-KY': '3665.TW', '騰雲': '6870.TWO', '穩懋': '3105.TWO' | |
| } | |
| # 產業分類 | |
| INDUSTRY_MAPPING = { | |
| '0050.TW': 'ETF', '2330.TW': '半導體', '2454.TW': '半導體', '2317.TW': '電子組件', | |
| '2308.TW': '電子', '2382.TW': '電子', '2881.TW': '金融', '2891.TW': '金融', | |
| '2882.TW': '金融', '2303.TW': '半導體', '2412.TW': '電信', '2884.TW': '金融', | |
| '2886.TW': '金融', '3711.TW': '半導體', '2357.TW': '電子', '1216.TW': '食品', | |
| '2885.TW': '金融', '2345.TW': '網通設備', '3231.TW': '電子', '3034.TW': '半導體', | |
| '2892.TW': '金融', '2379.TW': '半導體', '6669.TWO': '電子', '2890.TW': '金融', | |
| '5880.TW': '金融', '2880.TW': '金融', '2383.TW': '電子', '3661.TWO': '半導體', | |
| '3017.TW': '電子', '2883.TW': '金融', '3008.TW': '光學', '2603.TW': '航運', | |
| '2301.TW': '電子', '2002.TW': '鋼鐵', '5871.TW': '金融', '2327.TW': '電子被動元件', | |
| '2887.TW': '金融', '5876.TW': '金融', '1101.TW': '營建', '3045.TW': '電信', | |
| '4938.TW': '電子', '4904.TW': '電信', '2207.TW': '汽車', '2395.TW': '電腦周邊', | |
| '1301.TW': '塑膠', '2912.TW': '百貨', '6446.TWO': '生技', '1303.TW': '塑膠', | |
| '2609.TW': '航運', '2615.TW': '航運', '6505.TW': '塑膠', '2637.TW': '散裝航運', | |
| '2049.TW': '工具機', '2408.TW': 'DRAM', '2337.TW': 'NFLSH', '4966.TWO': '高速傳輸', | |
| '3665.TW': '連接器', '6870.TWO': '軟體整合', '3105.TWO': 'PA功率' | |
| } | |
| def get_stock_data(symbol, period='1y'): | |
| """獲取股票資料""" | |
| try: | |
| stock = yf.Ticker(symbol) | |
| data = stock.history(period=period) | |
| if data.empty and symbol == 'TXF=F': | |
| stock = yf.Ticker('0050.TW') | |
| data = stock.history(period=period) | |
| if data.empty: | |
| stock = yf.Ticker('^TWII') | |
| data = stock.history(period=period) | |
| return data | |
| except: | |
| return pd.DataFrame() | |
| def simple_statistical_predict(data, predict_days=5): | |
| """【備用模型】簡化的統計預測模型。""" | |
| if len(data) < 60: return None | |
| prices = data['Close'].values | |
| ma_short = np.mean(prices[-5:]) | |
| ma_medium = np.mean(prices[-20:]) | |
| ma_long = np.mean(prices[-60:]) | |
| recent_trend = np.polyfit(range(20), prices[-20:], 1)[0] | |
| volatility = np.std(prices[-20:]) / np.mean(prices[-20:]) | |
| base_change = recent_trend * predict_days | |
| trend_factor = 1.0 + (0.02 if ma_short > ma_medium > ma_long else -0.02 if ma_short < ma_medium < ma_long else 0) | |
| 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)} | |
| # 【修改 4】: 建立一個新的函式來處理 XGBoost 模型的輸入和輸出 | |
| # 修正後的 advanced_xgboost_predict 函數 | |
| def advanced_xgboost_predict(data, predict_days): | |
| """ | |
| 【進階模型橋接函式】 | |
| - 準備 XGBoost 模型所需的輸入 DataFrame。 | |
| - 呼叫模型進行預測。 | |
| - 將模型的輸出格式轉換為主程式所需的格式。 | |
| """ | |
| if xgb_model is None or data.empty: | |
| print("XGBoost 模型未載入或數據為空") | |
| return None | |
| # 1. 準備輸入資料 | |
| # 確保數據有足夠的歷史記錄 | |
| if len(data) < 20: | |
| print("歷史數據不足，無法使用 XGBoost 模型") | |
| return None | |
| # 使用最新的資料點來進行未來預測 | |
| input_df = data.tail(1).copy() | |
| # 檢查必要欄位是否存在 | |
| required_columns = ['Open', 'High', 'Low', 'Close', 'Volume'] | |
| missing_columns = [col for col in required_columns if col not in input_df.columns] | |
| if missing_columns: | |
| print(f"缺少必要欄位: {missing_columns}") | |
| return None | |
| try: | |
| # 2. 呼叫模型預測 | |
| print(f"呼叫 XGBoost 模型進行 {predict_days} 天預測...") | |
| predictions = xgb_model.predict('xgboost_model', input_df) | |
| # 3. 根據 predict_days 解析輸出 | |
| # 建立預測天數到模型輸出鍵的映射 | |
| day_to_key_map = { | |
| 1: 'Close_t0_pred', # 假設 t0 代表 1 天後 | |
| 5: 'Close_t5_pred', | |
| 10: 'Close_t10_pred', | |
| 20: 'Close_t20_pred' | |
| } | |
| # 找到對應的預測鍵 | |
| prediction_key = day_to_key_map.get(predict_days) | |
| if prediction_key is None or prediction_key not in predictions: | |
| print(f"警告: XGBoost 模型沒有提供 {predict_days} 天的預測結果。可用鍵值: {list(predictions.keys())}") | |
| # 如果沒有對應的預測期間，嘗試使用最接近的 | |
| available_days = [1, 5, 10, 20] | |
| closest_day = min(available_days, key=lambda x: abs(x - predict_days)) | |
| prediction_key = day_to_key_map[closest_day] | |
| print(f"使用最接近的預測期間: {closest_day} 天") | |
| predicted_price = predictions[prediction_key] | |
| current_price = data['Close'].iloc[-1] | |
| change_pct = ((predicted_price - current_price) / current_price) * 100 | |
| # 4. 包裝成主程式所需的格式 | |
| result = { | |
| 'predicted_price': float(predicted_price), | |
| 'change_pct': float(change_pct), | |
| 'confidence': 0.85 # XGBoost 模型通常有較高的信心度 | |
| } | |
| print(f"XGBoost 預測成功: 當前價格={current_price:.2f}, 預測價格={predicted_price:.2f}, 變化={change_pct:.2f}%") | |
| return result | |
| except Exception as e: | |
| print(f"執行 XGBoost 預測時發生錯誤: {e}") | |
| import traceback | |
| traceback.print_exc() | |
| return None | |
| def get_prediction(data, predict_days=5): | |
| """ | |
| 【【模型預測控制器】】 | |
| 根據 USE_ADVANCED_MODEL 的設定，呼叫對應的預測模型。 | |
| """ | |
| if USE_ADVANCED_MODEL: | |
| print(f"模式: 進階XGBoost模型 | 預測天期: {predict_days}天") | |
| # 【修改 5】: 呼叫新的 XGBoost 橋接函式 | |
| prediction = advanced_xgboost_predict(data, predict_days) | |
| # 如果進階模型預測失敗，則自動降級使用簡易模型 | |
| if prediction is not None: | |
| return prediction | |
| else: | |
| print("進階模型預測失敗或無對應天期，自動降級為簡易統計模型。") | |
| # 預設或降級時執行簡易模型 | |
| print(f"模式: 簡易統計模型 | 預測天期: {predict_days}天") | |
| return simple_statistical_predict(data, predict_days) | |
| def calculate_technical_indicators(df): | |
| """計算技術指標""" | |
| if df.empty: return df | |
| df['MA5'] = df['Close'].rolling(window=5).mean() | |
| df['MA20'] = df['Close'].rolling(window=20).mean() | |
| 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)) | |
| 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'] | |
| 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) | |
| 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() | |
| df['D'] = df['K'].ewm(com=2).mean() | |
| 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) | |
| df['up_move'] = df['High'] - df['High'].shift(1) | |
| df['down_move'] = df['Low'].shift(1) - df['Low'] | |
| df['+DM'] = np.where((df['up_move'] > df['down_move']) & (df['up_move'] > 0), df['up_move'], 0) | |
| df['-DM'] = np.where((df['down_move'] > df['up_move']) & (df['down_move'] > 0), df['down_move'], 0) | |
| df['TR'] = np.max([df['High'] - df['Low'], abs(df['High'] - df['Close'].shift(1)), abs(df['Low'] - df['Close'].shift(1))], axis=0) | |
| df['+DI'] = (df['+DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean()) * 100 | |
| df['-DI'] = (df['-DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean()) * 100 | |
| df['DX'] = abs(df['+DI'] - df['-DI']) / (df['+DI'] + df['-DI']) * 100 | |
| df['ADX'] = df['DX'].ewm(com=13, adjust=False).mean() | |
| return df | |
| # 修正後的 calculate_volume_profile 函數 | |
| def calculate_volume_profile(df, num_bins=50): | |
| 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 | |
| # 確保沒有 NaN 值 | |
| df_clean = df.dropna(subset=['High', 'Low', 'Close', 'Volume']) | |
| if df_clean.empty: | |
| return None, None, None | |
| all_prices = np.concatenate([df_clean['High'].values, df_clean['Low'].values]) | |
| min_price, max_price = all_prices.min(), all_prices.max() | |
| # 使用典型價格 (High + Low + Close) / 3 作為價格指標 | |
| price_for_volume = (df_clean['High'] + df_clean['Low'] + df_clean['Close']) / 3 | |
| # 移除 NaN 值並確保對應的權重也被移除 | |
| price_indicator = price_for_volume.dropna() | |
| corresponding_volume = df_clean['Volume'].loc[price_indicator.index] | |
| # 再次檢查是否有空數據 | |
| if len(price_indicator) == 0 or len(corresponding_volume) == 0: | |
| return None, None, None | |
| try: | |
| hist, bin_edges = np.histogram( | |
| price_indicator.values, | |
| bins=num_bins, | |
| range=(min_price, max_price), | |
| weights=corresponding_volume.values | |
| ) | |
| price_centers = (bin_edges[:-1] + bin_edges[1:]) / 2 | |
| return bin_edges, hist, price_centers | |
| except Exception as e: | |
| print(f"Volume profile 計算錯誤: {e}") | |
| return None, None, None | |
| def get_business_climate_data(): | |
| try: | |
| if not os.path.exists('business_climate.csv'): return pd.DataFrame() | |
| df = pd.read_csv('business_climate.csv') | |
| if 'Date' not in df.columns: df.columns = ['Date', 'Index'] if len(df.columns) == 2 else df.columns | |
| if 'Date' in df.columns: | |
| try: df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce') | |
| except: df['Date'] = pd.to_datetime(df['Date'], errors='coerce') | |
| df = df.dropna(subset=['Date']) | |
| 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(): | |
| try: | |
| if not os.path.exists('taiwan_pmi.csv'): return pd.DataFrame() | |
| df = pd.read_csv('taiwan_pmi.csv') | |
| if 'DATE' in df.columns: df = df.rename(columns={'DATE': 'Date', 'INDEX': 'Index'}) | |
| elif len(df.columns) == 2: df.columns = ['Date', 'Index'] | |
| if 'Date' in df.columns: | |
| try: df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce') | |
| except: df['Date'] = pd.to_datetime(df['Date'], errors='coerce') | |
| df = df.dropna(subset=['Date']) | |
| return df | |
| except Exception as e: | |
| print(f"無法獲取 PMI 資料: {str(e)}") | |
| return pd.DataFrame() | |
| def generate_gemini_analysis(stock_name, stock_symbol, period, data): | |
| """ | |
| 使用 Gemini API 生成基本面和市場展望分析。 | |
| """ | |
| api_key = os.getenv("GEMINI_API_KEY") | |
| if not api_key: | |
| return "無法讀取 GEMINI API 金鑰", "請在系統環境變數中設定您的金鑰" | |
| try: | |
| genai.configure(api_key=api_key) | |
| model = genai.GenerativeModel('gemini-1.5-flash') | |
| price_change = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100 | |
| rsi_current = data['RSI'].iloc[-1] | |
| macd_current = data['MACD'].iloc[-1] | |
| macd_signal_current = data['MACD_Signal'].iloc[-1] | |
| industry = INDUSTRY_MAPPING.get(stock_symbol, '綜合') | |
| prompt = f""" | |
| 請扮演一位專業、資深的台灣股市金融分析師。 | |
| 我將提供一檔台股的即時技術指標數據，請你基於這些數據，結合你對這家公司、其所在產業以及當前市場趨勢的理解，為我生成一段專業的「基本面分析」和一段「市場展望與投資建議」。 | |
| **股票資訊:** | |
| - **公司名稱:** {stock_name} ({stock_symbol}) | |
| - **分析期間:** 最近 {period} | |
| - **所屬產業:** {industry} | |
| - **期間價格變動:** {price_change:+.2f}% | |
| - **目前 RSI 指標:** {rsi_current:.2f} | |
| - **目前 MACD 指標:** MACD線為 {macd_current:.3f}, 信號線為 {macd_signal_current:.3f} | |
| **你的任務:** | |
| 1. **基本面分析 (約 150 字):** | |
| - 評論這家公司的產業地位、近期營運亮點或挑戰。 | |
| - 提及任何可能影響其基本面的關鍵因素 (例如：財報、法說會、政策、供應鏈變化等)。 | |
| - 請用專業、客觀的語氣撰寫。 | |
| 2. **市場展望與投資建議 (約 150 字):** | |
| - 基於上述所有資訊，提供對該股票的短期和中期市場展望。 | |
| - 提出具體的投資建議，例如：適合何種類型的投資人、潛在的風險點。 | |
| - 請直接提供分析內容，不要包含任何問候語。 | |
| **輸出格式:** | |
| 請嚴格按照以下格式回傳，使用"$$"作為兩個段落之間的分隔符： | |
| [基本面分析內容]$$[市場展望與投資建議內容] | |
| """ | |
| response = model.generate_content(prompt) | |
| parts = response.text.split('$$') | |
| if len(parts) == 2: | |
| fundamental_analysis = parts[0].strip() | |
| market_outlook = parts[1].strip() | |
| return dcc.Markdown(fundamental_analysis), dcc.Markdown(market_outlook) | |
| else: | |
| # Fallback for unexpected response format | |
| return dcc.Markdown("無法解析 Gemini 回應，請稍後再試。"), dcc.Markdown(response.text) | |
| except Exception as e: | |
| error_message = f"呼叫 Gemini API 時發生錯誤: {str(e)}" | |
| print(error_message) | |
| return dcc.Markdown(error_message), dcc.Markdown("請檢查後台日誌或 API 金鑰設定") | |
| # 建立 Dash 應用程式 | |
| app = dash.Dash(__name__, suppress_callback_exceptions=True) | |
| try: | |
| print("正在初始化新聞情緒分析模型...") | |
| predictor = BertPredictor(max_news_per_keyword=5) | |
| print("新聞情緒分析模型初始化成功。") | |
| except Exception as e: | |
| print(f"錯誤：新聞情緒分析模型初始化失敗 - {e}") | |
| predictor = None | |
| # 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'}), | |
| 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}], | |
| 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'}), | |
| 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)'}), | |
| 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'}), | |
| 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='0050.TW', 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='1mo', 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'}), | |
| html.Div(id='stock-info-cards', style={'margin-bottom': '30px'}), | |
| html.Div([html.Div([dcc.Graph(id='price-chart')], style={'width': '100%', 'display': 'inline-block', 'vertical-align': 'top'})]), | |
| 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'},{'label': 'DMI 動向指標', 'value': 'DMI'}], | |
| 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)'}), | |
| html.Div([dcc.Graph(id='volume-chart')], style={'margin-top': '20px'}), | |
| html.Div([html.H3("產業表現分析"), dcc.Graph(id='industry-analysis')], style={'margin-top': '30px'}), | |
| html.Div([ | |
| html.H3("📊 分析師觀點與市場解讀", style={'color': '#2E86AB', 'margin-bottom': '20px'}), | |
| html.Div([ | |
| 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'}), | |
| html.Div([ | |
| html.H4("📈 基本面分析 (AI 生成)", 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'}) | |
| ]), | |
| html.Div([ | |
| html.H4("🎯 市場展望與投資建議 (AI 生成)", 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'}), | |
| 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=['0050.TW', '2330.TW', '2454.TW'], multi=True, style={'margin-bottom': '5px'}), | |
| html.Small('（元大台灣50 (0050.TW) 為固定比較基準，不可移除）', style={'display': 'block', 'font-style': 'italic', 'color': 'gray'}) | |
| ], 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%', 'vertical-align': 'top'}) | |
| ]), | |
| 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)'}), | |
| ]) | |
| def update_taiex_prediction(predict_days): | |
| data = get_stock_data('^TWII', '2y') | |
| if data.empty: return html.Div("無法獲取台指期資料"), {} | |
| # === 呼叫 get_prediction 控制器，它會自動選擇模型 === | |
| final_prediction = get_prediction(data, predict_days) | |
| if final_prediction is None: return html.Div("資料不足，無法進行預測"), {} | |
| current_price, last_date = data['Close'].iloc[-1], data.index[-1] | |
| predicted_price, change_pct, confidence = final_prediction['predicted_price'], final_prediction['change_pct'], final_prediction['confidence'] | |
| 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]) | |
| prediction_dates, prediction_prices = [last_date], [current_price] | |
| for days in intervals_to_predict: | |
| # === 迴圈內也使用統一的預測控制器 === | |
| interim_prediction = get_prediction(data, days) | |
| if interim_prediction: | |
| prediction_dates.append(last_date + timedelta(days=days)) | |
| prediction_prices.append(interim_prediction['predicted_price']) | |
| # (後續繪圖邏輯不變) | |
| color, arrow = ('red', '📈') if change_pct >= 0 else ('green', '📉') | |
| 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)'}) | |
| fig = go.Figure() | |
| 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))) | |
| fig.add_trace(go.Scatter(x=prediction_dates, y=prediction_prices, mode='lines+markers', name=f'{predict_days}日預測路徑', 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 | |
| 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 | |
| stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0] | |
| color, arrow = ('red', '▲') if change >= 0 else ('green', '▼') | |
| 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"{arrow} {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'}) | |
| ]) | |
| # 修正後的 update_price_chart callback 函數的相關部分 | |
| def update_price_chart_fixed(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] | |
| fig = make_subplots(rows=1, cols=2, shared_yaxes=True, | |
| column_widths=[0.8, 0.2], horizontal_spacing=0.01) | |
| if chart_type == 'candlestick': | |
| fig.add_trace(go.Candlestick( | |
| x=data.index, | |
| open=data['Open'], | |
| high=data['High'], | |
| low=data['Low'], | |
| close=data['Close'], | |
| name=stock_name, | |
| increasing_line_color='red', | |
| decreasing_line_color='green' | |
| ), row=1, col=1) | |
| else: | |
| fig.add_trace(go.Scatter( | |
| x=data.index, | |
| y=data['Close'], | |
| mode='lines', | |
| name=stock_name | |
| ), row=1, col=1) | |
| # 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') | |
| ), row=1, col=1) | |
| fig.add_trace(go.Scatter( | |
| x=data.index, | |
| y=data['MA20'], | |
| mode='lines', | |
| name='MA20', | |
| line=dict(color='blue') | |
| ), row=1, col=1) | |
| # 修正後的 Volume Profile 計算 | |
| bin_edges, volume_per_bin, price_centers = calculate_volume_profile(data, num_bins=50) | |
| if volume_per_bin is not None and price_centers is not None: | |
| fig.add_trace(go.Bar( | |
| orientation='h', | |
| y=price_centers, | |
| x=volume_per_bin, | |
| name='Volume Profile', | |
| text=[f'{vol/1000:.0f}k' for vol in volume_per_bin], | |
| textposition='auto', | |
| marker=dict( | |
| color='rgba(173, 216, 230, 0.6)', | |
| line=dict(color='rgba(30, 144, 255, 0.8)', width=1) | |
| ) | |
| ), row=1, col=2) | |
| fig.update_layout( | |
| title_text=f'{stock_name} 股價走勢與成交量分佈', | |
| height=500, | |
| showlegend=True, | |
| xaxis1=dict(title='日期', type='date', rangeslider_visible=False), | |
| yaxis1=dict(title='價格 (TWD)'), | |
| xaxis2=dict(title='成交量', showticklabels=True), | |
| yaxis2=dict(showticklabels=False), | |
| bargap=0.05 | |
| ) | |
| return fig | |
| 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] | |
| fig = go.Figure() | |
| 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="green", annotation_text="超買線(70)") | |
| fig.add_hline(y=30, line_dash="dash", line_color="red", annotation_text="超賣線(30)") | |
| fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)") | |
| 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=('價格走勢', 'MACD 指標')) | |
| 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) | |
| 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) | |
| 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) | |
| colors = ['red' if x >= 0 else 'green' 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.update_layout(title_text=f'{stock_name} - MACD 指數平滑異同移動平均線', height=550) | |
| elif indicator == 'BB': | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=2))) | |
| 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'))) | |
| fig.add_trace(go.Scatter(x=data.index, y=data['BB_Middle'], mode='lines', name='中軌(MA20)', line=dict(color='blue', width=1))) | |
| 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'))) | |
| 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')) | |
| 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) | |
| 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) | |
| fig.add_hline(y=80, line_dash="dash", line_color="green", annotation_text="超買線(80)", row=2, col=1) | |
| fig.add_hline(y=20, line_dash="dash", line_color="red", annotation_text="超賣線(20)", row=2, col=1) | |
| fig.update_layout(title=f'{stock_name} - KD 隨機指標 (9,3,3)', height=500, yaxis2_range=[0, 100]) | |
| 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')) | |
| 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) | |
| 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) | |
| fig.add_hline(y=-20, line_dash="dash", line_color="green", annotation_text="超買線(-20)", row=2, col=1) | |
| fig.add_hline(y=-80, line_dash="dash", line_color="red", annotation_text="超賣線(-80)", row=2, col=1) | |
| fig.update_layout(title=f'{stock_name} - 威廉指標 %R (14日)', height=500, yaxis2_range=[-100, 0]) | |
| elif indicator == 'DMI': | |
| 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', 'DMI 指標')) | |
| data_filtered = data.iloc[14:] | |
| fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1)), row=1, col=1) | |
| fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['+DI'], mode='lines', name='+DI', line=dict(color='red', width=2)), row=2, col=1) | |
| fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['-DI'], mode='lines', name='-DI', line=dict(color='green', width=2)), row=2, col=1) | |
| fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['ADX'], mode='lines', name='ADX', line=dict(color='blue', width=2, dash='dot')), row=2, col=1) | |
| fig.update_layout(title=f'{stock_name} - DMI 動向指標 (14日)', height=500, showlegend=True, yaxis2_range=[0, 100]) | |
| return fig | |
| 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] | |
| colors = ['red' if data['Close'].iloc[i] > data['Open'].iloc[i] else 'green' for i in range(len(data))] | |
| fig = go.Figure(go.Bar(x=data.index, y=data['Volume'], marker_color=colors, name='成交量')) | |
| fig.update_layout(title=f'{stock_name} 成交量', xaxis_title='日期', yaxis_title='成交量', height=300) | |
| return fig | |
| def update_industry_analysis(selected_stock): | |
| performance_data = [] | |
| for name, symbol in TAIWAN_STOCKS.items(): | |
| data = get_stock_data(symbol, '1mo') | |
| if not data.empty and len(data) > 1: | |
| return_pct = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100 | |
| performance_data.append({ | |
| '股票': name, | |
| '代碼': symbol, | |
| '月報酬率(%)': return_pct, | |
| '絕對波動': abs(return_pct) | |
| }) | |
| if not performance_data: | |
| fig = go.Figure().add_annotation(text="無法計算產業資料", showarrow=False) | |
| fig.update_layout(title="近一月市場波動最大標的", height=400) | |
| return fig | |
| df_performance = pd.DataFrame(performance_data) | |
| df_top_movers = df_performance.sort_values(by='絕對波動', ascending=False).head(10) | |
| fig = px.pie( | |
| df_top_movers, | |
| values='絕對波動', | |
| names='股票', | |
| title='近一月市場波動最大 Top 10 標的', | |
| hover_data={'月報酬率(%)': ':.2f'} | |
| ) | |
| fig.update_traces( | |
| textposition='inside', | |
| textinfo='percent+label', | |
| hovertemplate="<b>%{label}</b><br>月報酬率: %{customdata[0]:.2f}%<extra></extra>" | |
| ) | |
| fig.update_layout(height=400, showlegend=False) | |
| return fig | |
| def update_business_climate_chart(selected_stock): | |
| df = get_business_climate_data() | |
| if df.empty: | |
| fig = go.Figure().add_annotation(text="無法載入景氣燈號資料", showarrow=False) | |
| fig.update_layout(title="台灣景氣燈號", height=300) | |
| return fig | |
| def get_light_color(score): | |
| if score >= 32: return 'red' | |
| elif score >= 24: return 'orange' | |
| elif score >= 17: return 'yellow' | |
| elif score >= 10: return 'lightgreen' | |
| else: return 'blue' | |
| 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')))) | |
| fig.add_hline(y=32, line_dash="dash", line_color="red", annotation_text="紅燈(32)") | |
| fig.add_hline(y=17, line_dash="dash", line_color="yellow", annotation_text="黃燈(17)") | |
| 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 | |
| def update_analysis_text(selected_stock, period): | |
| cache_key = f"{selected_stock}-{period}" | |
| current_time = time.time() | |
| if cache_key in ANALYSIS_CACHE: | |
| cached_data = ANALYSIS_CACHE[cache_key] | |
| if current_time - cached_data['timestamp'] < CACHE_DURATION_SECONDS: | |
| print(f"從快取載入分析: {cache_key}") | |
| return cached_data['technical'], cached_data['fundamental'], cached_data['outlook'] | |
| print(f"重新生成分析: {cache_key}") | |
| 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 or len(data) < 20: | |
| return "資料不足，無法分析", "資料不足，無法分析", "資料不足，無法分析" | |
| data = calculate_technical_indicators(data) | |
| price_change = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100 | |
| rsi_current = data['RSI'].iloc[-1] if not pd.isna(data['RSI'].iloc[-1]) else 50 | |
| 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 | |
| technical_text = html.Div([ | |
| html.P([html.Strong("價格趨勢："), f"在最近 {period} 期間內，{stock_name} 股價呈現", html.Span(f"{'上漲' if price_change > 5 else '下跌' if price_change < -5 else '盤整'}", style={'color': 'red' if price_change > 5 else 'green' 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"目前的 RSI 值為 {rsi_current:.1f}，", html.Span("處於超買區(>70)" if rsi_current > 70 else "處於超賣區(<30)" if rsi_current < 30 else "在正常範圍內", style={'color': 'green' if rsi_current > 70 else 'red' if rsi_current < 30 else 'blue', 'font-weight': 'bold'}), "。"]), | |
| html.P([html.Strong("MACD 指標："), f"MACD 快線 ({macd_current:.3f}) 目前", html.Span("高於" if macd_current > macd_signal_current else "低於", style={'color': 'red' if macd_current > macd_signal_current else 'green', 'font-weight': 'bold'}), f" Signal 慢線 ({macd_signal_current:.3f})，", f"顯示市場動能偏向{'多頭' if macd_current > macd_signal_current else '空頭'}。"]), | |
| ]) | |
| fundamental_text, market_outlook_text = generate_gemini_analysis(stock_name, selected_stock, period, data) | |
| ANALYSIS_CACHE[cache_key] = { | |
| 'technical': technical_text, | |
| 'fundamental': fundamental_text, | |
| 'outlook': market_outlook_text, | |
| 'timestamp': current_time | |
| } | |
| return technical_text, fundamental_text, market_outlook_text | |
| def update_pmi_chart(selected_stock): | |
| df = get_pmi_data() | |
| if df.empty: | |
| fig = go.Figure().add_annotation(text="無法載入PMI資料", showarrow=False) | |
| fig.update_layout(title="台灣PMI指數", height=300) | |
| return fig | |
| colors = ['red' if value >= 50 else 'green' 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')))) | |
| fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線(50)") | |
| 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 | |
| def summarize_news_with_gemini(news_list: list) -> str: | |
| """ | |
| 使用 Gemini API 將英文新聞標題列表摘要成一段繁體中文。 | |
| """ | |
| api_key = os.getenv("GEMINI_API_KEY") | |
| if not api_key: | |
| return "錯誤：找不到 GEMINI_API_KEY。請在 Hugging Face Secrets 中設定。" | |
| try: | |
| genai.configure(api_key=api_key) | |
| model = genai.GenerativeModel('gemini-1.5-flash') | |
| formatted_news = "\n".join([f"- {news}" for news in news_list]) | |
| prompt = f""" | |
| 請扮演一位專業的金融市場分析師。 | |
| 以下是幾則最新的英文財經新聞標題，請將它們整合成一段簡潔、流暢、約 200 字的繁體中文市場動態摘要，與利多哪些產業，利空哪些產業。 | |
| 提供3段重點， | |
| 請專注於可能影響市場情緒和股價的關鍵資訊，並直接提供摘要內容，不要包含任何額外的問候語或說明。 | |
| 英文新聞標題如下： | |
| {formatted_news} | |
| """ | |
| response = model.generate_content(prompt) | |
| return response.text | |
| except Exception as e: | |
| print(f"呼叫 Gemini API 時發生錯誤: {e}") | |
| return f"無法生成新聞摘要，請稍後再試。錯誤訊息：{e}" | |
| def update_comparison_analysis(selected_stocks, period): | |
| fixed_stock = '0050.TW' | |
| if not selected_stocks: selected_stocks = [fixed_stock] | |
| elif fixed_stock not in selected_stocks: selected_stocks.insert(0, fixed_stock) | |
| 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 = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == stock), stock) | |
| 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))) | |
| total_return = ((data['Close'].iloc[-1] / data['Close'].iloc[0]) - 1) * 100 | |
| volatility = data['Close'].pct_change().std() * np.sqrt(252) * 100 | |
| 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') | |
| if comparison_data: | |
| table_rows = [] | |
| for item in sorted(comparison_data, key=lambda x: x['return'], reverse=True): | |
| color = 'red' if item['return'] > 0 else 'green' | |
| 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"), html.Th("報酬率"), html.Th("波動率"), html.Th("現價")])), html.Tbody(table_rows)], style={'width': '100%'}) | |
| return fig, table | |
| return fig, html.Div("無可比較資料") | |
| def update_sentiment_analysis(selected_stock): | |
| if predictor is None: | |
| error_fig = go.Figure().add_annotation(text="情緒指標模型載入失敗", showarrow=False) | |
| error_fig.update_layout(height=200) | |
| return dcc.Graph(figure=error_fig), html.P("新聞分析模型載入失敗，請檢查後台日誌。") | |
| sentiment_score_raw = predictor.get_news_index() | |
| if sentiment_score_raw is not None: | |
| sentiment_score_normalized = (sentiment_score_raw + 1) * 50 | |
| sentiment_score_normalized = max(0, min(100, sentiment_score_normalized)) | |
| if sentiment_score_normalized >= 65: | |
| bar_color, level_text = "#5cb85c", "樂觀" | |
| elif sentiment_score_normalized >= 35: | |
| bar_color, level_text = "#f0ad4e", "中性" | |
| else: | |
| bar_color, level_text = "#d9534f", "悲觀" | |
| gauge_fig = go.Figure(go.Indicator( | |
| mode = "gauge+number", value = sentiment_score_normalized, | |
| domain = {'x': [0, 1], 'y': [0, 1]}, | |
| title = {'text': f"昨日市場情緒: {level_text}", 'font': {'size': 18}}, | |
| gauge = {'axis': {'range': [0, 100]}, 'bar': {'color': bar_color}, | |
| 'steps': [{'range': [0, 35], 'color': "rgba(217, 83, 79, 0.2)"}, | |
| {'range': [35, 65], 'color': "rgba(240, 173, 78, 0.2)"}, | |
| {'range': [65, 100], 'color': "rgba(92, 184, 92, 0.2)"}]} | |
| )) | |
| gauge_fig.update_layout(height=200, margin=dict(l=30, r=30, t=50, b=20)) | |
| gauge_content = dcc.Graph(figure=gauge_fig) | |
| else: | |
| error_fig = go.Figure().add_annotation(text="今日尚無情緒分數", showarrow=False) | |
| error_fig.update_layout(height=200) | |
| gauge_content = dcc.Graph(figure=error_fig) | |
| top_news_list = predictor.get_news() | |
| news_content = None | |
| if top_news_list and isinstance(top_news_list, list): | |
| summary_text = summarize_news_with_gemini(top_news_list) | |
| news_content = dcc.Markdown(summary_text, style={ | |
| 'margin': '8px 0', 'padding-left': '5px', | |
| 'font-size': '15px', 'line-height': '1.7' | |
| }) | |
| elif top_news_list == []: | |
| news_content = html.P("昨日無重大相關新聞。", style={'text-align': 'center', 'padding-top': '50px'}) | |
| else: | |
| news_content = html.P("讀取新聞時發生錯誤。", style={'text-align': 'center', 'padding-top': '50px'}) | |
| return gauge_content, news_content | |
| # 主程式執行 | |
| if __name__ == '__main__': | |
| app.run(host="0.0.0.0", port=7860, debug=False) |