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| 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() | |