Hands-On Machine Learning for Algorithmic Trading

How to run linear regression in practice

The accompanying notebook linear_regression_intro.ipynb illustrates a simple and then a multiple linear regression, the latter using both OLS and gradient descent. For the multiple regression, we generate two random input variables x₁ and x₂ that range from -50 to +50, and an outcome variable calculated as a linear combination of the inputs plus random Gaussian noise to meet the normality assumption GMT 6: