import yfinance as yf import pandas as pd

gme = yf.Ticker('GME') gme = gme.history(period='max') gme.to_csv('gme.csv')

gme = pd.read_csv('gme.csv') gme

del gme['Dividends'] del gme['Stock Splits'] gme

gme['Close'].plot()

import pandas as pd output_var = pd.DataFrame(gme['Close']) features = ['Open', 'High', 'Low', 'Volume']

from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() feature_transform = scaler.fit_transform(gme[features]) feature_transform = pd.DataFrame(columns=features, data=feature_transform, index=gme.index) feature_transform

from sklearn.model_selection import TimeSeriesSplit timesplit = TimeSeriesSplit(n_splits=10) for train_index, test_index in timesplit.split(feature_transform): x_train, x_test = feature_transform[:len(train_index)], feature_transform[len(train_index): (len(train_index)+len(test_index))] y_train, y_test = output_var[:len(train_index)].values.ravel(), output_var[len(train_index): (len(train_index)+len(test_index))].values.ravel()

import numpy as np train_x = np.array(x_train) test_x = np.array(x_test) x_train = train_x.reshape(x_train.shape[0], 1, x_train.shape[1]) x_test = test_x.reshape(x_test.shape[0], 1, x_test.shape[1])

from tensorflow.keras.optimizers import Adam from keras.models import Sequential from keras.layers import Dense, Activation from keras.layers import LSTM from keras.utils.vis_utils import plot_model from sklearn.metrics import mean_squared_error, r2_score

model = Sequential() model.add(LSTM(32, input_shape=(1, train_x.shape[1]), activation='relu', return_sequences=False)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(x_train, y_train, epochs=30, batch_size=8, verbose=1, shuffle=False)

y_pred = model.predict(x_test)

import matplotlib.pyplot as plt plt.plot(y_test, label='True Value') plt.plot(y_pred, label='LSTM Value') plt.title('Prediction by LSTM Value') plt.xlabel('Time Scale') plt.ylabel('Scaled USD') plt.legend() plt.show()