# -------------------------------------------------------# # Script for the creation and comparative testing of # # a fully connected perceptron model with different # # convolutional models using the same dataset. # # The script creates three models: # # - fully connected perceptron with three hidden layers # # & regularization. # # - 1-dimensional convolutional layer # # - 2-dimensional convolutional layer # # When training models, from training dataset, script # # allocates 1% 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 }) # 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_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 inputs=data.shape[1]-2 targerts=2 train_data=data[:,0:inputs] train_target=data[:,inputs:] callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=10) # Creating a perceptron model with three hidden layers and regularization model1 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(targerts, activation=tf.nn.tanh) ]) model1.summary() #keras.utils.plot_model(model1, show_shapes=True) # Add a 1D convolutional layer to the model model2 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs), # Reformat tensor to a 3-dimensional one. Specify 2 dimensions as 3rd one is defined by batch size keras.layers.Reshape((-1,4)), # Convolutional later with 8 filters keras.layers.Conv1D(8,1,1,activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), # Pooling layer keras.layers.MaxPooling1D(2,strides=1), # Reformat tensor to a 2-dimensional one for fully connected layers keras.layers.Flatten(), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(targerts, activation=tf.nn.tanh) ]) model2.summary() #keras.utils.plot_model(model2, show_shapes=True) # Replace the convolutional layer in the model with a 2-dimensional one model3 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs), # Reformat tensor into 4-dimensional. Specify 3 dimensions as the 4th dimension is determined by the batch size keras.layers.Reshape((-1,4,1)), # Convolutional later with 8 filters keras.layers.Conv2D(8,(3,1),1,activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), # Pooling layer keras.layers.MaxPooling2D((2,1),strides=1), # Reformat tensor to a 2-dimensional one for fully connected layers keras.layers.Flatten(), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)), keras.layers.Dense(targerts, activation=tf.nn.tanh) ]) model3.summary() #keras.utils.plot_model(model3, show_shapes=True) model1.compile(optimizer='Adam', loss='mean_squared_error', metrics=['accuracy']) history1 = model1.fit(train_data, train_target, epochs=500, batch_size=1000, callbacks=[callback], verbose=2, validation_split=0.01, shuffle=True) model1.save(os.path.join(path,'convolution1.h5')) model2.compile(optimizer='Adam', loss='mean_squared_error', metrics=['accuracy']) history2 = model2.fit(train_data, train_target, epochs=500, batch_size=1000, callbacks=[callback], verbose=2, validation_split=0.01, shuffle=True) model2.save(os.path.join(path,'convolution2.h5')) model3.compile(optimizer='Adam', loss='mean_squared_error', metrics=['accuracy']) history3 = model3.fit(train_data, train_target, epochs=500, batch_size=1000, callbacks=[callback], verbose=2, validation_split=0.01, shuffle=True) model3.save(os.path.join(path,'convolution3.h5')) # Render model training results plt.figure() plt.plot(history1.history['loss'], label='Perceptron train') plt.plot(history1.history['val_loss'], label='Perceptron validation') plt.plot(history2.history['loss'], label='Conv1D train') plt.plot(history2.history['val_loss'], label='Conv1D validation') plt.plot(history3.history['loss'], label='Conv2D train') plt.plot(history3.history['val_loss'], label='Conv2D validation') plt.ylabel('$MSE$ $loss$') plt.xlabel('$Epochs$') plt.title('Model training dynamics') plt.legend(loc='upper right',ncol=3) plt.figure() plt.plot(history1.history['accuracy'], label='Perceptron train') plt.plot(history1.history['val_accuracy'], label='Perceptron validation') plt.plot(history2.history['accuracy'], label='Conv1D train') plt.plot(history2.history['val_accuracy'], label='Conv1D validation') plt.plot(history3.history['accuracy'], label='Conv2D train') plt.plot(history3.history['val_accuracy'], label='Conv2D validation') plt.ylabel('$Accuracy$') plt.xlabel('$Epochs$') plt.title('Model training dynamics') plt.legend(loc='lower right',ncol=3) # Load testing dataset test_filename = os.path.join(path,'test_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:] # 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) # Log testing results print('Perceptron model') print('Test accuracy:', test_acc1) print('Test loss:', test_loss1) print('Conv1D model') print('Test accuracy:', test_acc2) print('Test loss:', test_loss2) print('Conv2D model') print('Test accuracy:', test_acc3) print('Test loss:', test_loss3) plt.figure() plt.bar(['Perceptron','Conv1D', 'Conv2D'],[test_loss1,test_loss2,test_loss3]) plt.ylabel('$MSE$ $loss$') plt.title('Test results') plt.figure() plt.bar(['Perceptron','Conv1D', 'Conv2D'],[test_acc1,test_acc2,test_acc3]) plt.ylabel('$Accuracy$') plt.title('Test results') plt.show()