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Chapter 11/1_Indirect_LSTM_Model.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+from master_function import import_cot_data, data_preprocessing
+from master_function import plot_train_test_values, calculate_directional_accuracy
+from sklearn.metrics import mean_squared_error
+
+# Calling the function and preprocessing the data
+CAD = 'CANADIAN DOLLAR - CHICAGO MERCANTILE EXCHANGE'
+data = import_cot_data(2010, 2023, CAD)
+data = np.array(data.iloc[:, -1], dtype = np.float64)
+
+# Setting the hyperparameters
+num_lags = 100
+train_test_split = 0.80
+neurons = 200
+num_epochs = 200
+batch_size = 4
+
+# Creating the training and test sets
+x_train, y_train, x_test, y_test = data_preprocessing(data, num_lags, train_test_split)
+
+# Reshape the data to 3D for LSTM input
+x_train = x_train.reshape((-1, num_lags, 1))
+x_test = x_test.reshape((-1, num_lags, 1))
+
+# Create the LSTM model
+model = Sequential()
+
+# Adding a first layer
+model.add(LSTM(units = neurons, input_shape = (num_lags, 1)))
+
+# Adding a second layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding the output layer 
+model.add(Dense(units = 1))
+
+# Compiling the model
+model.compile(loss = 'mean_squared_error', optimizer = 'adam')
+
+# Fitting the model
+model.fit(x_train, y_train, epochs = num_epochs, batch_size = batch_size)
+
+# Predicting in the training set for illustrative purposes
+y_predicted_train = model.predict(x_train)
+
+# Predicting in the test set
+y_predicted = model.predict(x_test)
+
+# Plotting
+plot_train_test_values(100, 50, y_train, y_test, y_predicted)
+
+# Performance evaluation
+print('---')
+print('Directional Accuracy Train = ', round(calculate_directional_accuracy(y_predicted_train, y_train), 2), '%')
+print('Directional Accuracy Test = ', round(calculate_directional_accuracy(y_predicted, y_test), 2), '%')
+print('RMSE Train = ', round(np.sqrt(mean_squared_error(y_predicted_train, y_train)), 10))
+print('RMSE Test = ', round(np.sqrt(mean_squared_error(y_predicted, y_test)), 10))
+print('Correlation In-Sample Predicted/Train = ', round(np.corrcoef(np.reshape(y_predicted_train, (-1)), y_train)[0][1], 3))
+print('Correlation Out-of-Sample Predicted/Test = ', round(np.corrcoef(np.reshape(y_predicted, (-1)), np.reshape(y_test, (-1)))[0][1], 3))
+print('---')

Chapter 11/2_Direct_MPF_LSTM_Model.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+from master_function import import_cot_data, direct_mpf
+from master_function import calculate_directional_accuracy
+from sklearn.metrics import mean_squared_error
+
+# Calling the function and preprocessing the data
+CAD = 'CANADIAN DOLLAR - CHICAGO MERCANTILE EXCHANGE'
+data = import_cot_data(2010, 2023, CAD)
+data = np.array(data.iloc[:, -1], dtype = np.float64)
+
+# Setting the hyperparameters
+num_lags = 100
+train_test_split = 0.80
+neurons = 400
+num_epochs = 200
+batch_size = 10
+forecast_horizon = 100
+
+# Prepare the arrays
+x_train, y_train, x_test, y_test = direct_mpf(data, 
+                                              num_lags, 
+                                              train_test_split, 
+                                              forecast_horizon)
+
+# Reshape the data to 3D for LSTM input
+x_train = x_train.reshape((-1, num_lags, 1))
+x_test = x_test.reshape((-1, num_lags, 1))
+
+# Create the LSTM model
+model = Sequential()
+
+# Adding a first layer
+model.add(LSTM(units = neurons, input_shape = (num_lags, 1)))
+
+# Adding a second layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding the output layer 
+model.add(Dense(units = forecast_horizon))
+
+# Compiling the model
+model.compile(loss = 'mean_squared_error', optimizer = 'adam')
+
+# Fitting the model
+model.fit(x_train, y_train, epochs = num_epochs, batch_size = batch_size)
+
+# Predicting in the test set
+y_predicted = model.predict(x_test)
+
+# Plotting
+plt.plot(y_predicted[-1, 0:50], label = 'Predicted data', color = 'red', linewidth = 1)
+plt.plot(y_test[-1, 0:50], label = 'Test data', color = 'black', linestyle = 'dashed', linewidth = 2)
+plt.grid()
+plt.legend()
+
+'''
+y_predicted = np.transpose(y_predicted[-1, 0:50])
+y_test = np.transpose(y_test[-1, 0:50])
+
+# Performance evaluation
+print('---')
+print('Directional Accuracy Test = ', round(calculate_directional_accuracy(y_predicted, y_test), 2), '%')
+print('RMSE Test = ', round(np.sqrt(mean_squared_error(y_predicted, y_test)), 10))
+print('Correlation Out-of-Sample Predicted/Test = ', round(np.corrcoef(np.reshape(y_predicted, (-1)), np.reshape(y_test, (-1)))[0][1], 3))
+print('---')
+'''

Chapter 11/3_Recursive_MPF_LSTM_Model.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+from master_function import import_cot_data, data_preprocessing, recursive_mpf
+from master_function import plot_train_test_values, calculate_directional_accuracy
+from sklearn.metrics import mean_squared_error
+
+# Calling the function and preprocessing the data
+CAD = 'CANADIAN DOLLAR - CHICAGO MERCANTILE EXCHANGE'
+data = import_cot_data(2010, 2023, CAD)
+data = np.array(data.iloc[:, -1], dtype = np.float64)
+
+# Setting the hyperparameters
+num_lags = 10
+train_test_split = 0.80
+neurons = 5
+num_epochs = 10
+batch_size = 20
+
+# Creating the training and test sets
+x_train, y_train, x_test, y_test = data_preprocessing(data, num_lags, train_test_split)
+
+# Reshape the data to 3D for LSTM input
+x_train = x_train.reshape((-1, num_lags, 1))
+x_test = x_test.reshape((-1, num_lags, 1))
+
+# Create the LSTM model
+model = Sequential()
+
+# Adding a first layer
+model.add(LSTM(units = neurons, input_shape = (num_lags, 1)))
+
+# Adding a second layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding the output layer 
+model.add(Dense(units = 1))
+
+# Compiling the model
+model.compile(loss = 'mean_squared_error', optimizer = 'adam')
+
+# Fitting the model
+model.fit(x_train, y_train, epochs = num_epochs, batch_size = batch_size)
+
+# Predicting in the test set on a recursive basis
+x_test, y_predicted = recursive_mpf(x_test, y_test, num_lags, model)
+
+# Plotting
+plot_train_test_values(100, 50, y_train, y_test, y_predicted)
+
+# Performance evaluation
+print('---')
+print('Directional Accuracy Test = ', round(calculate_directional_accuracy(y_predicted, y_test), 2), '%')
+print('RMSE Test = ', round(np.sqrt(mean_squared_error(y_predicted, y_test)), 10))
+print('Correlation Out-of-Sample Predicted/Test = ', round(np.corrcoef(np.reshape(y_predicted, (-1)), np.reshape(y_test, (-1)))[0][1], 3))
+print('---')

Chapter 11/4_LSTM_Model_Technical_Indicators_Inputs.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+from master_function import mass_import, rsi, ma, calculate_accuracy
+from master_function import plot_train_test_values, multiple_data_preprocessing
+from sklearn.metrics import mean_squared_error
+from master_function import add_column, delete_column
+
+# Calling the function and preprocessing the data
+data = mass_import(0, 'W1')[:, -1]
+data = rsi(np.reshape(data, (-1, 1)), 5, 0, 1)
+data = ma(data, 5, 0, 2)
+data[:, 2] = data[:, 0] - data[:, 2]
+data = add_column(data, 1)
+for i in range(len(data)):
+    data[i, 3] = data[i, 0] - data[i - 1, 0]
+data[:, 0] = data[:, -1]
+data = delete_column(data, 3, 1)
+
+# Setting the hyperparameters
+num_lags = 6
+train_test_split = 0.80
+neurons = 500
+num_epochs = 500
+batch_size = 200
+
+x_train, y_train, x_test, y_test = multiple_data_preprocessing(data, train_test_split)
+
+# Reshape the data to 3D for LSTM input
+x_train = x_train.reshape((-1, num_lags, 1))
+x_test = x_test.reshape((-1, num_lags, 1))
+
+# Create the LSTM model
+model = Sequential()
+
+# Adding a first layer
+model.add(LSTM(units = neurons, input_shape = (num_lags, 1)))
+
+# Adding a second layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding a third layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding a fourth layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding a fifth layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding the output layer 
+model.add(Dense(units = 1))
+
+# Compiling the model
+model.compile(loss = 'mean_squared_error', optimizer = 'adam')
+
+# Fitting the model
+model.fit(x_train, y_train, epochs = num_epochs, batch_size = batch_size)
+
+# Predicting in the training set for illustrative purposes
+y_predicted_train = model.predict(x_train)
+
+# Predicting in the test set
+y_predicted = model.predict(x_test)
+
+# Plotting
+plot_train_test_values(100, 50, y_train, y_test, y_predicted)
+
+# Performance evaluation
+print('---')
+print('Accuracy Train = ', round(calculate_accuracy(y_predicted_train, y_train), 2), '%')
+print('Accuracy Test = ', round(calculate_accuracy(y_predicted, y_test), 2), '%')
+print('RMSE Train = ', round(np.sqrt(mean_squared_error(y_predicted_train, y_train)), 10))
+print('RMSE Test = ', round(np.sqrt(mean_squared_error(y_predicted, y_test)), 10))
+print('Correlation In-Sample Predicted/Train = ', round(np.corrcoef(np.reshape(y_predicted_train, (-1)), y_train)[0][1], 3))
+print('Correlation Out-of-Sample Predicted/Test = ', round(np.corrcoef(np.reshape(y_predicted, (-1)), np.reshape(y_test, (-1)))[0][1], 3))
+print('---')

Chapter 11/5_LSTM_Volatility_Model_Bitcoin.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+from master_function import import_cot_data, data_preprocessing
+from master_function import plot_train_test_values, calculate_directional_accuracy
+from sklearn.metrics import mean_squared_error
+from master_function import add_column, delete_row, volatility
+
+# Calling the function and preprocessing the data
+data = pd.read_excel('Bitcoin_Daily_Historical_Data.xlsx').values
+data = volatility(data, 10, 0, 1)
+data = data[:, -1]
+
+# Differencing for stationarity
+data = np.diff(data)
+
+# Checking for stationarity
+from statsmodels.tsa.stattools import adfuller
+print('p-value: %f' % adfuller(data)[1])
+
+# Setting the hyperparameters
+num_lags = 300
+train_test_split = 0.80
+neurons = 80
+num_epochs = 100
+batch_size = 500
+
+# Creating the training and test sets
+x_train, y_train, x_test, y_test = data_preprocessing(data, num_lags, train_test_split)
+
+# Reshape the data to 3D for LSTM input
+x_train = x_train.reshape((-1, num_lags, 1))
+x_test = x_test.reshape((-1, num_lags, 1))
+
+# Create the LSTM model
+model = Sequential()
+
+# Adding a first layer
+model.add(LSTM(units = neurons, input_shape = (num_lags, 1)))
+
+# Adding a second layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding a third layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding a fourth layer
+model.add(Dense(neurons, activation = 'relu')) 
+
+# Adding the output layer 
+model.add(Dense(units = 1))
+
+# Compiling the model
+model.compile(loss = 'mean_squared_error', optimizer = 'adam')
+
+# Fitting the model
+model.fit(x_train, y_train, epochs = num_epochs, batch_size = batch_size)
+
+# Predicting in the training set for illustrative purposes
+y_predicted_train = model.predict(x_train)
+
+# Predicting in the test set
+y_predicted = model.predict(x_test)
+
+# Plotting
+plot_train_test_values(100, 50, y_train, y_test, y_predicted)
+
+# Performance evaluation
+print('---')
+print('Directional Accuracy Train = ', round(calculate_directional_accuracy(y_predicted_train, y_train), 2), '%')
+print('Directional Accuracy Test = ', round(calculate_directional_accuracy(y_predicted, y_test), 2), '%')
+print('RMSE Train = ', round(np.sqrt(mean_squared_error(y_predicted_train, y_train)), 10))
+print('RMSE Test = ', round(np.sqrt(mean_squared_error(y_predicted, y_test)), 10))
+print('Correlation In-Sample Predicted/Train = ', round(np.corrcoef(np.reshape(y_predicted_train, (-1)), y_train)[0][1], 3))
+print('Correlation Out-of-Sample Predicted/Test = ', round(np.corrcoef(np.reshape(y_predicted, (-1)), np.reshape(y_test, (-1)))[0][1], 3))
+print('---')