Hands-On Machine Learning for Algorithmic Trading

Randomized grid search

To explore the hyperparameter space, we specify values for key parameters that we would like to test in combination. The sklearn library supports RandomizedSearchCV to cross-validate a subset of parameter combinations that are sampled randomly from specified distributions. We will implement a custom version that allows us to leverage early stopping while monitoring the current best-performing combinations so we can abort the search process once satisfied with the result rather than specifying a set number of iterations beforehand.

To this end, we specify a parameter grid according to each library's parameters as before, generate all combinations using the built-in Cartesian product generator provided by the itertools ...