Convolution preprocess consuming more RAM Python

Question

I am trying to implement this CNN model for stock prize prediction: https://github.com/ZezhouLi/Convolutional-Networks-for-Stock-Predicting
But I am facing the issue while preprocessing the data for the implementation. The preprocess step is consuming a lot of RAM. (My system has 32 GB RAM and 256 GB SSD Hard disk)
Here is the file that is consuming the RAM and at last gives a Memory Error:

import numpy as np
import matplotlib.pyplot as plt
import glob
import math
from PIL import Image
import statsmodels.api as sm


def r_squared(y_true, y_hat):
    ssr = 0
    sst = 0
    e = np.subtract(y_true, y_hat)
    y_mean = np.mean(y_true)
    for item in e:
        ssr += item**2
    for item in y_true:
        sst += (item - y_mean)**2
    r2 = 1 - ssr / sst
    return r2


def data_process(data):
    processed_data = []
    for item in data:
        m = np.mean(item)
        s = np.std(item)
        normal_item = [(float(i)-m)/s for i in item]
        normal_item.insert(0, 1)
        processed_data.append(normal_item)
    return processed_data


def get_pixel_values():
    file_name = r'\figures'
    pixels = []
    for filename in glob.glob(file_name + '\*.png'):
        im = Image.open(filename)
        temp_pixels = list(im.getdata())
        pixels.append(temp_pixels)
    return pixels


def find_returns(data):
    returns = []
    for group in data:
        count = 30
        while count <= (len(group)-5):
            current_data = group[count-1]
            future_data = group[count+4]
            p1 = np.mean(current_data)
            p2 = np.mean(future_data)
            returns.append(math.log(p2/p1))
            count += 1
    return returns


def convert_image():
    size = 54, 32
    file_name = r'\figures'
    for filename in glob.glob(file_name + '\*.png'):
        img = Image.open(filename)
        img.thumbnail(size)
        img = img.convert('L')
        img.save(filename)


def plot_data(data):
    t = np.arange(0, 29, 1)
    file_name_number = 0
    fig = plt.figure(frameon=False)
    for group in data:
        count = 30
        while count <= (len(group)-5):
            high = []
            low = []
            for item in group[count-30:count]:
                high.append(item[0])
                low.append(item[1])
            file_name = r'\fig_' + str(file_name_number)
            ax = plt.Axes(fig, [0., 0., 1., 1.])
            ax.set_axis_off()
            fig.add_axes(ax)
            ax.plot(t, high[0:-1], 'b', t, low[0:-1], 'g')
            fig.savefig(r'\figures' + file_name)
            fig.clf()
            file_name_number += 1
            count += 1
    print('Created %d files!' % file_name_number)


def extract_useful_data(data):
    groups = []
    for group in data:
        temp_buffer = []
        for item in group:
            temp = [item[2], item[3]]
            temp = [float(i) for i in temp]
            temp_buffer.append(temp)
        groups.append(temp_buffer)
    return groups


def split_data(data):
    groups = []
    for item in data:
        temp_buffer = []
        for string in item:
            number = string.split(',')
            temp_buffer.append(number)
        groups.append(temp_buffer)
    return groups


def extract_data():
    file_name = r'\data.txt'
    infile = open(file_name, 'r')
    temp_buffer = []
    for line in infile:
        temp_buffer.append(line.strip('\n'))
    temp_buffer = temp_buffer[8:]
    i = 0
    groups = []
    temp = []
    for item in temp_buffer:
        if i != 390:
            temp.append(item)
            i += 1
        else:
            groups.append(temp)
            temp = []
            i = 0
    groups.append(temp)
    infile.close()
    return groups


def main():
    original_data = extract_data()
    splitted_data = split_data(original_data)
    useful_data = extract_useful_data(splitted_data)
    plot_data(useful_data)
    convert_image()
    returns = np.asarray(find_returns(useful_data))
    training_data = np.asarray(get_pixel_values())
    training_data = sm.add_constant(training_data, has_constant='add')
    results = sm.OLS(returns[0:4340], training_data[0:4340]).fit()
    y_in_sample = results.predict(training_data[0:4340])
    r2 = r_squared(returns[0:4340], y_in_sample)
    print r2


if __name__ == "__main__":
    main()

I have got Memory Error, which occurs when the program consumes all of the RAM memory of the system. Please improve on the program.