advanced-algorithmic-trading

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from sklearn import linear_model
if __name__ == "__main__":
# 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
# 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
# Split up the features, X, and responses, y, into
# training and test arrays
X_train = X[:split]
X_test = X[split:]
y_train = y[:split]
y_test = y[split:]
# Open a scikit-learn linear regression model
# and fit it to the training data
lr_model = linear_model.LinearRegression()
lr_model.fit(X_train, y_train)
# Output the estimated parameters for the linear model
print(
"Estimated intercept, slope: %0.6f, %0.6f" % (
lr_model.intercept_,