Meta-Labeling:

Code base for the meta-labeling papers published with the Journal of Financial Data Science

Theory and Framework (Journal of Financial Data Science)

Meta-labeling is a machine learning (ML) layer that sits on top of a base primary strategy to help size positions, filter out false-positive signals, and improve metrics such as the Sharpe ratio and maximum drawdown. This article consolidates the knowledge of several publications into a single work, providing practitioners with a clear framework to support the application of meta-labeling to investment strategies. The relationships between binary classification metrics and strategy performance are explained, alongside answers to many frequently asked questions regarding the technique. The author also deconstructs meta-labeling into three components, using a controlled experiment to show how each component helps to improve strategy metrics and what types of features should be considered in the model specification phase.

Calibration and Position Sizing (Working Paper)

Working on this paper

Meta-Labeling Model Architectures (Working Paper)

Working on this paper

Ensemble Model Selection Framework for Meta-Labeling (Working Paper)

Working on this paper