300 lines
12 KiB
Python
300 lines
12 KiB
Python
|
# -------------------------------------------------------#
|
||
|
|
# Script for creating and testing different models of #
|
||
|
|
# fully connected perceptron with the same dataset. #
|
||
|
|
# The script creates three models: #
|
||
|
|
# - fully connected perceptron with one hidden layer, #
|
||
|
|
# - fully connected perceptron with three hidden layers, #
|
||
|
|
# - fully connected perceptron with three hidden layers #
|
||
|
|
# & regularization. #
|
||
|
|
# When training models, from training dataset, script #
|
||
|
|
# allocates 20% 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({'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
|
||
|
|
})
|
||
|
|
|
||
|
|
# Load training dataset
|
||
|
|
if not mt5.initialize():
|
||
|
|
print("initialize() failed, error code =",mt5.last_error())
|
||
|
|
quit()
|
||
|
|
|
||
|
|
path=os.path.join(mt5.terminal_info().data_path,r'MQL5\Files')
|
||
|
|
mt5.shutdown()
|
||
|
|
filename = os.path.join(path,'study_pricedelt_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))
|
||
|
|
|
||
|
|
# Split training dataset to input data and target
|
||
|
|
|
||
|
|
#df = pd.DataFrame(data)
|
||
|
|
#correlation_matrix =df.corr()
|
||
|
|
#print(correlation_matrix)
|
||
|
|
#filename = os.path.join(path,'corr.csv')
|
||
|
|
#correlation_matrix.to_csv(filename, encoding='utf-8')
|
||
|
|
|
||
|
|
inputs=data.shape[1]-3
|
||
|
|
targerts=1
|
||
|
|
train_data=data[:,0:inputs]
|
||
|
|
train_target=data[:,inputs+1:inputs+2]
|
||
|
|
|
||
|
|
# Create the first model with one hidden layer
|
||
|
|
callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5)
|
||
|
|
model1 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
|
||
|
|
keras.layers.Dense(200, activation=tf.nn.swish),
|
||
|
|
keras.layers.Dense(targerts, activation=tf.nn.relu)
|
||
|
|
])
|
||
|
|
model1.compile(optimizer='Adam',
|
||
|
|
loss='mean_squared_error',
|
||
|
|
metrics=['accuracy'])
|
||
|
|
history1 = model1.fit(train_data, train_target,
|
||
|
|
epochs=200, batch_size=1000,
|
||
|
|
callbacks=[callback],
|
||
|
|
verbose=2,
|
||
|
|
validation_split=0.2,
|
||
|
|
shuffle=False)
|
||
|
|
model1.save(os.path.join(path,'perceptron1.h5'))
|
||
|
|
|
||
|
|
# Create a model with three hidden layers
|
||
|
|
model2 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
|
||
|
|
keras.layers.Dense(50, activation=tf.nn.swish),
|
||
|
|
#keras.layers.Dropout(0.5),
|
||
|
|
keras.layers.Dense(50, activation=tf.nn.swish),
|
||
|
|
#keras.layers.Dropout(0.5),
|
||
|
|
keras.layers.Dense(50, activation=tf.nn.swish),
|
||
|
|
#keras.layers.Dropout(0.5),
|
||
|
|
keras.layers.Dense(targerts, activation=tf.nn.relu)
|
||
|
|
])
|
||
|
|
model2.compile(optimizer='Adam',
|
||
|
|
loss='mean_squared_error',
|
||
|
|
metrics=['accuracy'])
|
||
|
|
history2 = model2.fit(train_data, train_target,
|
||
|
|
epochs=200, batch_size=1000,
|
||
|
|
callbacks=[callback],
|
||
|
|
verbose=2,
|
||
|
|
validation_split=0.2,
|
||
|
|
shuffle=False)
|
||
|
|
model2.save(os.path.join(path,'perceptron2.h5'))
|
||
|
|
|
||
|
|
|
||
|
|
# Add regularization to the model with three hidden layers
|
||
|
|
model3 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
|
||
|
|
keras.layers.Dense(50, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-5, l2=1e-5)),
|
||
|
|
keras.layers.Dropout(0.5),
|
||
|
|
keras.layers.Dense(50, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-5, l2=1e-5)),
|
||
|
|
keras.layers.Dropout(0.5),
|
||
|
|
keras.layers.Dense(50, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-5, l2=1e-5)),
|
||
|
|
keras.layers.Dropout(0.5),
|
||
|
|
keras.layers.Dense(targerts, activation=tf.nn.relu)
|
||
|
|
])
|
||
|
|
model3.compile(optimizer='Adam',
|
||
|
|
loss='mean_squared_error',
|
||
|
|
metrics=['accuracy'])
|
||
|
|
history3 = model3.fit(train_data, train_target,
|
||
|
|
epochs=200, batch_size=1000,
|
||
|
|
callbacks=[callback],
|
||
|
|
verbose=2,
|
||
|
|
validation_split=0.2,
|
||
|
|
shuffle=False)
|
||
|
|
model3.save(os.path.join(path,'perceptron3.h5'))
|
||
|
|
|
||
|
|
# Plotting the first model training results
|
||
|
|
|
||
|
|
plt.plot(history1.history['loss'], label='Train 1 hidden layer')
|
||
|
|
plt.plot(history1.history['val_loss'], label='Validation 1 hidden layer')
|
||
|
|
plt.ylabel('$MSE$ $loss$')
|
||
|
|
plt.xlabel('$Epochs$')
|
||
|
|
plt.title('Model training dynamics')
|
||
|
|
plt.legend(loc='upper right')
|
||
|
|
|
||
|
|
plt.figure()
|
||
|
|
plt.plot(history1.history['accuracy'], label='Train 1 hidden layer')
|
||
|
|
plt.plot(history1.history['val_accuracy'], label='Validation 1 hidden layer')
|
||
|
|
plt.ylabel('$Accuracy$')
|
||
|
|
plt.xlabel('$Epochs$')
|
||
|
|
plt.title('Model training dynamics')
|
||
|
|
plt.legend(loc='lower right')
|
||
|
|
|
||
|
|
# Plotting the training results of the second model
|
||
|
|
plt.figure()
|
||
|
|
plt.plot(history1.history['loss'], label='Train 1 hidden layer')
|
||
|
|
plt.plot(history1.history['val_loss'], label='Validation 1 hidden layer')
|
||
|
|
plt.plot(history2.history['loss'], label='Train 3 hidden layers')
|
||
|
|
plt.plot(history2.history['val_loss'], label='Validation 3 hidden layers')
|
||
|
|
plt.ylabel('$MSE$ $loss$')
|
||
|
|
plt.xlabel('$Epochs$')
|
||
|
|
plt.title('Model training dynamics')
|
||
|
|
plt.legend(loc='lower left',ncol=2)
|
||
|
|
|
||
|
|
plt.figure()
|
||
|
|
plt.plot(history1.history['accuracy'], label='Train 1 hidden layer')
|
||
|
|
plt.plot(history1.history['val_accuracy'], label='Validation 1 hidden layer')
|
||
|
|
plt.plot(history2.history['accuracy'], label='Train 3 hidden layers')
|
||
|
|
plt.plot(history2.history['val_accuracy'], label='Validation 3 hidden layers')
|
||
|
|
plt.ylabel('$Accuracy$')
|
||
|
|
plt.xlabel('$Epochs$')
|
||
|
|
plt.title('Model training dynamics')
|
||
|
|
plt.legend(loc='lower left',ncol=2)
|
||
|
|
|
||
|
|
# Plotting the training results of the third model
|
||
|
|
plt.figure()
|
||
|
|
plt.plot(history1.history['loss'], label='Train 1 hidden layer')
|
||
|
|
plt.plot(history1.history['val_loss'], label='Validation 1 hidden layer')
|
||
|
|
plt.plot(history2.history['loss'], label='Train 3 hidden layers')
|
||
|
|
plt.plot(history2.history['val_loss'], label='Validation 3 hidden layers')
|
||
|
|
plt.plot(history3.history['loss'], label='Train 3 hidden layers\nvs regularization')
|
||
|
|
plt.plot(history3.history['val_loss'], label='Validation 3 hidden layers\nvs regularization')
|
||
|
|
plt.ylabel('$MSE$ $Loss$')
|
||
|
|
plt.xlabel('$Epochs$')
|
||
|
|
plt.title('Model training dynamics')
|
||
|
|
plt.legend(loc='lower left',ncol=2)
|
||
|
|
|
||
|
|
plt.figure()
|
||
|
|
plt.plot(history1.history['accuracy'], label='Train 1 hidden layer')
|
||
|
|
plt.plot(history1.history['val_accuracy'], label='Validation 1 hidden layer')
|
||
|
|
plt.plot(history2.history['accuracy'], label='Train 3 hidden layers')
|
||
|
|
plt.plot(history2.history['val_accuracy'], label='Validation 3 hidden layers')
|
||
|
|
plt.plot(history3.history['accuracy'], label='Train 3 hidden layers\nvs regularization')
|
||
|
|
plt.plot(history3.history['val_accuracy'], label='Validation 3 hidden layers\nvs regularization')
|
||
|
|
plt.ylabel('$Accuracy$')
|
||
|
|
plt.xlabel('$Epochs$')
|
||
|
|
plt.title('Model training dynamics')
|
||
|
|
plt.legend(loc='upper left',ncol=2)
|
||
|
|
|
||
|
|
# Load testing dataset
|
||
|
|
test_filename = os.path.join(path,'test_pricedelt_data.csv')
|
||
|
|
test = np.asarray( pd.read_table(test_filename,
|
||
|
|
sep=',',
|
||
|
|
header=None,
|
||
|
|
skipinitialspace=True,
|
||
|
|
encoding='utf-8',
|
||
|
|
float_precision='high',
|
||
|
|
dtype=np.float64,
|
||
|
|
low_memory=False))
|
||
|
|
# Split test dataset to input data and target
|
||
|
|
test_data=test[:,0:inputs]
|
||
|
|
test_target=test[:,inputs+1:inputs+2]
|
||
|
|
|
||
|
|
# Check model results on a test dataset
|
||
|
|
test_loss1, test_acc1 = model1.evaluate(test_data, test_target, verbose=2)
|
||
|
|
test_loss2, test_acc2 = model2.evaluate(test_data, test_target, verbose=2)
|
||
|
|
test_loss3, test_acc3 = model3.evaluate(test_data, test_target, verbose=2)
|
||
|
|
|
||
|
|
|
||
|
|
mm=model1.predict(test_data)
|
||
|
|
rr = {}
|
||
|
|
rr=pd.DataFrame(rr)
|
||
|
|
test_target = pd.DataFrame(test_target)
|
||
|
|
mm = pd.DataFrame(mm)
|
||
|
|
rr['real'] = test_target
|
||
|
|
rr['predict'] = mm
|
||
|
|
rs = rr.head(50)
|
||
|
|
#rs = rr
|
||
|
|
|
||
|
|
plt.figure()
|
||
|
|
plt.plot(rs['real'], label='real')
|
||
|
|
plt.plot(rs['predict'], label='predict')
|
||
|
|
plt.legend(loc='upper left',ncol=2)
|
||
|
|
plt.ylabel('PREDICT')
|
||
|
|
plt.title('predict results model1')
|
||
|
|
plt.axhline(y = 0.6, color = 'r', linestyle = '--')
|
||
|
|
|
||
|
|
mm=model2.predict(test_data)
|
||
|
|
rr = {}
|
||
|
|
rr=pd.DataFrame(rr)
|
||
|
|
test_target = pd.DataFrame(test_target)
|
||
|
|
mm = pd.DataFrame(mm)
|
||
|
|
rr['real'] = test_target
|
||
|
|
rr['predict'] = mm
|
||
|
|
rs = rr.head(50)
|
||
|
|
#rs = rr
|
||
|
|
|
||
|
|
plt.figure()
|
||
|
|
plt.plot(rs['real'], label='real')
|
||
|
|
plt.plot(rs['predict'], label='predict')
|
||
|
|
plt.legend(loc='upper left',ncol=2)
|
||
|
|
plt.ylabel('PREDICT')
|
||
|
|
plt.title('predict results model2')
|
||
|
|
plt.axhline(y = 0.6, color = 'r', linestyle = '--')
|
||
|
|
|
||
|
|
mm=model3.predict(test_data)
|
||
|
|
rr = {}
|
||
|
|
rr=pd.DataFrame(rr)
|
||
|
|
test_target = pd.DataFrame(test_target)
|
||
|
|
mm = pd.DataFrame(mm)
|
||
|
|
rr['real'] = test_target
|
||
|
|
rr['predict'] = mm
|
||
|
|
rs = rr.head(50)
|
||
|
|
#rs = rr
|
||
|
|
|
||
|
|
plt.figure()
|
||
|
|
plt.plot(rs['real'], label='real')
|
||
|
|
plt.plot(rs['predict'], label='predict')
|
||
|
|
plt.legend(loc='upper left',ncol=2)
|
||
|
|
plt.ylabel('PREDICT')
|
||
|
|
plt.title('predict results model3')
|
||
|
|
plt.axhline(y = 0.6, color = 'r', linestyle = '--')
|
||
|
|
|
||
|
|
# Log testing results
|
||
|
|
print('Model 1 hidden layer')
|
||
|
|
print('Test accuracy:', test_acc1)
|
||
|
|
print('Test loss:', test_loss1)
|
||
|
|
|
||
|
|
print('Model 3 hidden layers')
|
||
|
|
print('Test accuracy:', test_acc2)
|
||
|
|
print('Test loss:', test_loss2)
|
||
|
|
|
||
|
|
print('Model 3 hidden layers vs regularization')
|
||
|
|
print('Test accuracy:', test_acc3)
|
||
|
|
print('Test loss:', test_loss3)
|
||
|
|
|
||
|
|
plt.figure()
|
||
|
|
plt.bar(['1 hidden layer','3 hidden layers', '3 hidden layers\nvs regularization'],[test_loss1,test_loss2,test_loss3])
|
||
|
|
plt.ylabel('$MSE$ $loss$')
|
||
|
|
plt.title('Test results')
|
||
|
|
plt.figure()
|
||
|
|
plt.bar(['1 hidden layer','3 hidden layers', '3 hidden layers\nvs regularization'],[test_acc1,test_acc2,test_acc3])
|
||
|
|
plt.ylabel('$Accuracy$')
|
||
|
|
plt.title('Test results')
|
||
|
|
|
||
|
|
plt.show()
|