advanced-algorithmic-trading

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The goal of the exercise will be to estimate the slope and intercept values on the test set
solely using a trained linear regression model based on the training data. Scikit-Learn possesses
a clean API to carry this out, which will be demonstrated below.
The first task is to import the necessary libraries. Matplotlib and Seaborn are imported
for plotting. Importing Seaborn is not strictly necessary, but it does provide more aestheticallypleasing
plots. NumPy is imported to provide random number generation, while the linear_model
object is imported from Scikit-Learn:
# lin_reg_sklearn.py
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn import linear_model
In the __main__ function all of the parameters are set. The code is commented liberally, so
it should be straightforward to ascertain the parameters from the code:
# Create N values, with 80% used for training
# and 20% used for testing/evaluation
N = 500
split = int(0.8*N)
# Set the intercept and slope of the univariate
# linear regression simulated data
alpha = 2.0
beta = 3.0
# Set the mean and variance of the randomly
# distributed noise in the simulated dataset
eps_mu = 0.0
eps_sigma = 30.0
# Set the mean and variance of the X data
X_mu = 0.0
X_sigma = 10.0
The next step is to randomly simulate some data. Firstly an array of errors is created, which
is stored in the eps variable. Then normally-distributed feature values, x i , are created and used
to create linear responses y i with error. The final line X = X.reshape(-1, 1) is needed to
avoid a deprecation warning in earlier versions of Scikit-Learn. Leaving it out will actually cause
a ValueError in version 0.19. Be warned!
# Create the error/noise, X and y data
eps = np.random.normal(loc=eps_mu, scale=eps_sigma, size=N)
X = np.random.normal(loc=X_mu, scale=X_sigma, size=N)
y = alpha + beta*X + eps
X = X.reshape(-1, 1) # Needed to avoid deprecation warning