# -------------------------------------------------------# # 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. # # - 2-dimensional convolutional layer # # - recurrent network with LSTM block # # 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_pricedelt_data_norm.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]-3 targerts=1 train_data=data[:,0:inputs] train_target=data[:,inputs:inputs+1] callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=20) # 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, to_file=os.path.join(path,'model1.png'),dpi=72,show_layer_names=False,rankdir='LR') # Add the LSTM block 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((-1,2)), # LSTM block contains 40 elements and returns results at each step keras.layers.LSTM(40, return_sequences=False, 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(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, to_file=os.path.join(path,'model2.png'),dpi=72,show_layer_names=False,rankdir='LR',expand_nested=True) # Model with 2-dimensional convolutional layer 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((-1,1,1)), # Convolutional later with 8 filters keras.layers.Conv2D(2,(2,1),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, to_file=os.path.join(path,'model3.png'),dpi=72,show_layer_names=False,rankdir='LR') # Model LSTM block without fully connected layers model4 = 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)), # 2 consecutive LSTM blocks # 2st contains 40 elements keras.layers.LSTM(40, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5), return_sequences=False), # 2nd produces output instead of fully connected layer keras.layers.Reshape((-1,2)), keras.layers.LSTM(targerts) ]) model4.summary() #keras.utils.plot_model(model4, show_shapes=True, to_file=os.path.join(path,'model4.png'),dpi=72,show_layer_names=False,rankdir='LR') # Model LSTM block without fully connected layers model5 = 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)), # 2 consecutive LSTM blocks # 1st one contains 40 elements and returns the result at each step keras.layers.LSTM(40, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5), return_sequences=True), # 2nd produces output instead of fully connected layer keras.layers.LSTM(targerts) ]) model5.summary() #keras.utils.plot_model(model5, show_shapes=True, to_file=os.path.join(path,'model5.png'),dpi=72,show_layer_names=False,rankdir='LR') 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=0.2, shuffle=True) model1.save(os.path.join(path,'rnn1.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=0.2, shuffle=False) model2.save(os.path.join(path,'rnn2.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=0.2, shuffle=True) model3.save(os.path.join(path,'rnn3.h5')) model4.compile(optimizer='Adam', loss='mean_squared_error', metrics=['accuracy']) history4 = model4.fit(train_data, train_target, epochs=500, batch_size=1000, callbacks=[callback], verbose=2, validation_split=0.2, shuffle=False) model4.save(os.path.join(path,'rnn4.h5')) model5.compile(optimizer='Adam', loss='mean_squared_error', metrics=['accuracy']) history5 = model5.fit(train_data, train_target, epochs=500, batch_size=1000, callbacks=[callback], verbose=2, validation_split=0.2, shuffle=False) model5.save(os.path.join(path,'rnn5.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(history3.history['loss'], label='Conv2D train') plt.plot(history3.history['val_loss'], label='Conv2D validation') plt.plot(history2.history['loss'], label='LSTM train') plt.plot(history2.history['val_loss'], label='LSTM validation') plt.plot(history4.history['loss'], label='LSTM only train') plt.plot(history4.history['val_loss'], label='LSTM only validation') plt.plot(history5.history['loss'], label='LSTM sequences train') plt.plot(history5.history['val_loss'], label='LSTM sequences validation') plt.ylabel('$MSE$ $loss$') plt.xlabel('$Epochs$') plt.title('Model training dynamics') plt.legend(loc='upper right', ncol=2) plt.figure() plt.plot(history1.history['accuracy'], label='Perceptron train') plt.plot(history1.history['val_accuracy'], label='Perceptron validation') plt.plot(history3.history['accuracy'], label='Conv2D train') plt.plot(history3.history['val_accuracy'], label='Conv2D validation') plt.plot(history2.history['accuracy'], label='LSTM train') plt.plot(history2.history['val_accuracy'], label='LSTM validation') plt.plot(history4.history['accuracy'], label='LSTM only train') plt.plot(history4.history['val_accuracy'], label='LSTM only validation') plt.plot(history5.history['accuracy'], label='LSTM sequences train') plt.plot(history5.history['val_accuracy'], label='LSTM sequences validation') plt.ylabel('$Accuracy$') plt.xlabel('$Epochs$') plt.title('Model training dynamics') plt.legend(loc='lower right', ncol=2) # Load testing dataset test_filename = os.path.join(path,'test_pricedelt_data_norm.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:inputs+1] # 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) test_loss4, test_acc4 = model4.evaluate(test_data, test_target, verbose=2) test_loss5, test_acc5 = model5.evaluate(test_data, test_target, verbose=2) # Log testing results print('Perceptron model') print('Test accuracy:', test_acc1) print('Test loss:', test_loss1) print('Conv2D model') print('Test accuracy:', test_acc3) print('Test loss:', test_loss3) print('LSTM model') print('Test accuracy:', test_acc2) print('Test loss:', test_loss2) print('LSTM only model') print('Test accuracy:', test_acc4) print('Test loss:', test_loss4) print('LSTM sequences model') print('Test accuracy:', test_acc5) print('Test loss:', test_loss5) plt.figure() plt.bar(['Perceptron','Conv2D','LSTM', 'LSTM only', 'LSTM sequences'],[test_loss1,test_loss3,test_loss2,test_loss4,test_loss5]) plt.ylabel('$MSE$ $loss$') plt.title('Test results') plt.figure() plt.bar(['Perceptron','Conv2D','LSTM', 'LSTM only', 'LSTM sequences'],[test_acc1,test_acc3,test_acc2,test_acc4,test_acc5]) plt.ylabel('$Accuracy$') plt.title('Test results') 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') 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') 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') mm=model4.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 model4') mm=model5.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 model5') plt.show()