data_utils.py

from sklearn import preprocessing
import numpy as np


def normalize_data(df):
    min_max_scaler = preprocessing.MinMaxScaler()
    df['open'] = min_max_scaler.fit_transform(df.open.values.reshape(-1, 1))
    df['high'] = min_max_scaler.fit_transform(df.high.values.reshape(-1, 1))
    df['low'] = min_max_scaler.fit_transform(df.low.values.reshape(-1, 1))
    df['volume'] = min_max_scaler.fit_transform(df.volume.values.reshape(-1, 1))
    df['adj close'] = min_max_scaler.fit_transform(df['adj close'].values.reshape(-1, 1))
    return df


def load_data(stock, seq_len):
    amount_of_features = len(stock.columns)
    data = stock.as_matrix()
    sequence_length = seq_len + 1
    result = []
    for index in range(len(data) - sequence_length):
        result.append(data[index: index + sequence_length])
    result = np.array(result)
    row = round(0.9 * result.shape[0])
    train = result[:int(row), :]
    x_train = train[:, :-1]
    y_train = train[:, -1][:, -1]
    x_test = result[int(row):, :-1]
    y_test = result[int(row):, -1][:, -1]
    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], amount_of_features))
    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], amount_of_features))
    return [x_train, y_train, x_test, y_test]