Machine Learning for Algorithmic Trading - Second Edition

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Random Forests – A Long-Short Strategy for Japanese Stocks

In this chapter, we will learn how to use two new classes of machine learning models for trading: decision trees and random forests. We will see how decision trees learn rules from data that encode nonlinear relationships between the input and the output variables. We will illustrate how to train a decision tree and use it for prediction with regression and classification problems, visualize and interpret the rules learned by the model, and tune the model's hyperparameters to optimize the bias-variance trade-off and prevent overfitting.

Decision trees are not only important standalone models but are also frequently used as components in other models. In the second part of this chapter, ...