Meta-Labeling Architecture

Abstract

Separating the side and size of a position allows for sophisticated strategy structures to be developed. Modeling the size component can be done through a meta-labeling approach. This article establishes several heterogeneous architectures to account for key aspects of meta-labeling. They serve as a guide for practitioners in the model development process, as well as for researchers to further build on these ideas. An architecture can be developed through the lens of feature- and/or strategy-driven approaches. The feature-driven approach exploits the way the information in the data is structured and how the selected models use that information, whereas a strategy-driven approach specifically aims to incorporate unique characteristics of the underlying trading strategy. Furthermore, the concept of inverse meta-labeling is introduced as a technique to improve the quantity and quality of the side forecasts.