import numpy as np import pandas as pd import yfinance as yf from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import LSTM, Dense, Dropout import gradio as gr import datetime # Function to fetch stock data def get_stock_data(ticker, period): data = yf.download(ticker, period=period) return data # Function to prepare the data for LSTM def prepare_data(data, time_step=60): scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1)) X, y = [], [] for i in range(time_step, len(scaled_data)): X.append(scaled_data[i-time_step:i, 0]) y.append(scaled_data[i, 0]) X, y = np.array(X), np.array(y) X = np.reshape(X, (X.shape[0], X.shape[1], 1)) return X, y, scaler # Function to build and train LSTM model def train_lstm_model(X_train, y_train): model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(Dropout(0.2)) model.add(LSTM(units=50, return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(units=1)) model.compile(optimizer='adam', loss='mean_squared_error') model.fit(X_train, y_train, epochs=10, batch_size=32) return model # Function to predict stock prices def predict_stock(model, data, scaler, time_step=60): inputs = scaler.transform(data['Close'].values.reshape(-1, 1)) X_test = [] for i in range(time_step, len(inputs)): X_test.append(inputs[i-time_step:i, 0]) X_test = np.array(X_test) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) predicted_prices = model.predict(X_test) predicted_prices = scaler.inverse_transform(predicted_prices) return predicted_prices # Function to fetch Taiwan 50 and Small 100 stocks def get_tw0050_stocks(): return [ "2330.TW", "2317.TW", "2454.TW", "2308.TW", "2881.TW", "2382.TW", "2303.TW", "2882.TW", "2891.TW", "3711.TW", "2412.TW", "2886.TW", "2884.TW", "1216.TW", "2357.TW", "2885.TW", "2892.TW", "3034.TW", "2890.TW", "2327.TW", "5880.TW", "2345.TW", "3231.TW", "2002.TW", "2880.TW", "3008.TW", "2883.TW", "1303.TW", "4938.TW", "2207.TW", "2887.TW", "2379.TW", "1101.TW", "2603.TW", "2301.TW", "1301.TW", "5871.TW", "3037.TW", "3045.TW", "2912.TW", "3017.TW", "6446.TW", "4904.TW", "3661.TW", "6669.TW", "1326.TW", "5876.TW", "2395.TW", "1590.TW", "6505.TW" ] def get_tw0051_stocks(): return [ "2371.TW", "3533.TW", "2618.TW", "3443.TW", "2347.TW", "3044.TW", "2834.TW", "2385.TW", "1605.TW", "2105.TW", "6239.TW", "6176.TW", "9904.TW", "1519.TW", "9910.TW", "1513.TW", "1229.TW", "9945.TW", "2313.TW", "1477.TW", "3665.TW", "2354.TW", "4958.TW", "8464.TW", "9921.TW", "2812.TW", "2059.TW", "1504.TW", "2542.TW", "6770.TW", "5269.TW", "2344.TW", "3023.TW", "1503.TW", "2049.TW", "2610.TW", "2633.TW", "3036.TW", "2368.TW", "3035.TW", "2027.TW", "9914.TW", "2408.TW", "2809.TW", "1319.TW", "2352.TW", "2337.TW", "2006.TW", "2206.TW", "4763.TW", "3005.TW", "1907.TW", "2915.TW", "1722.TW", "6285.TW", "6472.TW", "6531.TW", "3406.TW", "9958.TW", "9941.TW", "1795.TW", "2201.TW", "9917.TW", "2492.TW", "6890.TW", "2845.TW", "8454.TW", "8046.TW", "6789.TW", "2388.TW", "6526.TW", "1802.TW", "5522.TW", "6592.TW", "2204.TW", "2540.TW", "2539.TW", "3532.TW" ] # Function to fetch S&P 500 component stocks def get_sp500_stocks(): return [ "AAPL", "MSFT", "GOOGL", "AMZN", "FB", "TSLA", "BRK-B", "JNJ", "V", "WMT", "JPM", "MA", "PG", "NVDA", "UNH", "DIS", "HD", "PYPL", "VZ", "ADBE", "NFLX", "CMCSA", "PEP", "KO", "MRK", "INTC", "T", "CRM", "CSCO", "PFE", "XOM", "COST", "NKE", "CVX", "WFC", "MCD", "AMGN", "MDT", "IBM", "DHR", "LLY", "HON", "BA", "MMM", "NEE", "ACN", "UPS", "TMO", "AVGO", "PM" ] # Function to fetch NASDAQ component stocks def get_nasdaq_stocks(): return [ "AAPL", "MSFT", "AMZN", "TSLA", "GOOGL", "GOOG", "FB", "NVDA", "PYPL", "ADBE", "CMCSA", "NFLX", "COST", "PEP", "CSCO", "INTC", "TXN", "AVGO", "AMGN", "QCOM", "CHTR", "TMUS", "SBUX", "MDLZ", "ISRG", "BKNG", "MRNA", "FISV", "CSX", "ADI", "VRTX", "ATVI", "GILD", "ILMN", "ADP", "MU", "KLAC", "LRCX", "EA", "KHC", "JD", "MAR", "BIDU", "MELI", "ROST", "NXPI", "SPLK", "ALGN", "DOCU", "PDD" ] # Function to fetch Philadelphia Semiconductor Index component stocks def get_sox_stocks(): return [ "AMD", "AVGO", "TXN", "INTC", "MU", "NVDA", "QCOM", "ASML", "LRCX", "TSM", "AMAT", "ON", "NXPI", "ADI", "KLAC", "SWKS", "QRVO", "MCHP", "SLAB", "ENTG", "TER", "COHU", "UCTT", "ACLS", "LSCC", "MRVL", "SYNA", "MPWR", "FORM", "UCTT" ] # Function to fetch Dow Jones Industrial Average component stocks def get_dji_stocks(): return [ "AAPL", "MSFT", "JPM", "V", "UNH", "PG", "JNJ", "WMT", "DIS", "VZ", "INTC", "KO", "MRK", "GS", "TRV", "IBM", "MMM", "CAT", "RTX", "CVX", "MCD", "HON", "AXP", "WBA", "NKE", "DOW", "BA", "HD", "CRM", "AMGN" ] # Function to get top 10 potential stocks def get_top_10_potential_stocks(period, selected_indices): stock_list = [] if "台灣50" in selected_indices: stock_list += get_tw0050_stocks() if "台灣中型100" in selected_indices: stock_list += get_tw0051_stocks() if "S&P" in selected_indices: stock_list += get_sp500_stocks() if "NASDAQ" in selected_indices: stock_list += get_nasdaq_stocks() if "費城半導體" in selected_indices: stock_list += get_sox_stocks() if "道瓊" in selected_indices: stock_list += get_dji_stocks() stock_predictions = [] time_step = 60 for ticker in stock_list: data = get_stock_data(ticker, period) if data.empty or len(data) < time_step: # 如果數據為空或不足以生成訓練樣本，則跳過該股票 continue try: # Prepare data X_train, y_train, scaler = prepare_data(data, time_step=time_step) # Train model model = train_lstm_model(X_train, y_train) # Predict future prices predicted_prices = predict_stock(model, data, scaler, time_step=time_step) # Calculate the potential (e.g., last predicted price vs last actual price) potential = (predicted_prices[-1] - data['Close'].values[-1]) / data['Close'].values[-1] stock_predictions.append((ticker, potential, data['Close'].values[-1], predicted_prices[-1][0])) except Exception as e: print(f"股票 {ticker} 發生錯誤: {str(e)}") continue # Sort by potential and get top 10 top_10_stocks = sorted(stock_predictions, key=lambda x: x[1], reverse=True)[:10] return top_10_stocks # Gradio interface function def stock_prediction_app(period, selected_indices): # Get top 10 potential stocks top_10_stocks = get_top_10_potential_stocks(period, selected_indices) # Create a dataframe for display df = pd.DataFrame(top_10_stocks, columns=["股票代號", "潛力 (百分比)", "現價", "預測價格"]) return df # Define Gradio interface inputs = [ gr.Dropdown(choices=["3mo", "6mo", "9mo", "1yr"], label="時間範圍"), gr.CheckboxGroup(choices=["台灣50", "台灣中型100", "S&P", "NASDAQ", "費城半導體", "道瓊"], label="指數選擇", value=["台灣50", "台灣中型100"]) ] outputs = gr.Dataframe(label="潛力股推薦結果") gr.Interface(fn=stock_prediction_app, inputs=inputs, outputs=outputs, title="台股美股潛力股推薦系統 - LSTM模型")\ .launch()