NeuroBook/Scripts/template.py

<EFBFBD><EFBFBD># -------------------------------------------------------#

# Template for creating and testing different models of  #

# neural networks using the same dataset.                #

# When training models, from training dataset, script    #

# allocates 10% to validate the outputs.                 #

# After training, the script tests the performance       #

# of the model on a test dataset (separate data file)    #

# -------------------------------------------------------#

# Import Libraries

import os

import pandas as pd

import numpy as np

import tensorflow as tf 

from tensorflow import keras 

import matplotlib as mp

import matplotlib.pyplot as plt

import matplotlib.font_manager as fm

import MetaTrader5 as mt5



# Add fonts

font_list=fm.findSystemFonts()

for f in font_list:

    if(f.__contains__('ClearSans')):

        fm.fontManager.addfont(f)



# Set parameters for output graphs

mp.rcParams.update({'font.family':'serif',

                    'font.serif':'Clear Sans',

                    'axes.titlesize': 'x-large',

                    'axes.labelsize':'medium',

                    'xtick.labelsize':'small',

                    'ytick.labelsize':'small',

                    'legend.fontsize':'small',

                    'figure.figsize':[6.0,4.0],

                    'axes.titlecolor': '#707070',

                    'axes.labelcolor': '#707070',

                    'axes.edgecolor': '#707070',

                    'xtick.labelcolor': '#707070',

                    'ytick.labelcolor': '#707070',

                    'xtick.color': '#707070',

                    'ytick.color': '#707070',

                    'text.color': '#707070',

                    'lines.linewidth': 0.8,

                    'axes.linewidth': 0.5

                   })



# connect to the MetaTrader 5 terminal

if not mt5.initialize():

    print("initialize() failed, error code =",mt5.last_error())

    quit()



# request the path to the sandbox

path=os.path.join(mt5.terminal_info().data_path,r'MQL5\Files')

mt5.shutdown()



# Load training dataset

filename = os.path.join(path,'study_data.csv')

data = np.asarray( pd.read_table(filename,

                   sep=',',

                   header=None,

                   skipinitialspace=True,

                   encoding='utf-8',

                   float_precision='high',

                   dtype=np.float64,

                   low_memory=False))