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import numpy import matplotlib.pyplot as plt import pandas import pandas_datareader as web import datetime from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM, Dropout # Load Data: company = 'FB' start = datetime.datetime(2012, 1, 1) end = datetime.datetime(2020, 1, 1) data = web.DataReader(company, 'yahoo', start, end) # Prepare Data: scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1)) prediction_days = 60 x_train = [] y_train = [] for x in range(prediction_days, len(scaled_data)): x_train.append(scaled_data[x - prediction_days:x, 0]) y_train.append(scaled_data[x, 0]) x_train, y_train = numpy.array(x_train), numpy.array(y_train) x_train = numpy.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) # Build Model: 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=True)) model.add(Dropout(0.2)) model.add(LSTM(units=50)) 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=25, batch_size=32) '''# Test Model:''' test_start = datetime.datetime(2020, 1, 1) test_end = datetime.datetime.now() test_data = web.DataReader(company, 'yahoo', test_start, test_end) actual_prices = test_data['Close'].values total_dataset = pandas.concat((data['Close'], test_data['Close']), axis=0) model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values model_inputs = model_inputs.reshape(-1, 1) model_inputs = scaler.transform(model_inputs) x_test = [] for x in range(prediction_days, len(model_inputs)): x_test.append(model_inputs[x - prediction_days:x, 0]) x_test = numpy.array(x_test) x_test = numpy.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) # Plot Data: plt.plot(actual_prices, color='black', label=f'Actual {company} Price') plt.plot(predicted_prices, color='green', label=f'Predicted {company} Price') plt.title(f'{company} Share Price') plt.xlabel('Time') plt.ylabel(f'{company} Share Price') plt.legend() plt.show() # Predict Next Day: real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs + 1), 0]] real_data = numpy.array(real_data) real_data = numpy.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1)) prediction = model.predict(real_data) prediction = scaler.inverse_transform(prediction) print(f'Prediction: {prediction}')
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