Hands-On Machine Learning for Algorithmic Trading

OLS with statsmodels

We use statsmodels to estimate a multiple regression model that accurately reflects the data generating process as follows:

from statsmodels.api import X_ols = add_constant(X)model = OLS(y, X_ols).fit()model.summary()

This yields the following OLS Regression Results summary:

Summary of OLS Regression Results

The upper part of the summary displays the dataset characteristics, namely the estimation method, the number of observations and parameters, and indicates that standard error estimates do not account for heteroskedasticity. The middle panel shows the coefficient values that closely reflect the artificial data generating ...