Optimizing macro trading signals – A practical introduction

Based on theory and empirical evidence, point-in-time indicators of macroeconomic trends and states are strong candidates for trading signals. A key challenge is to select and condense them into a single signal. The simplest (and often successful) approach is conceptual risk parity, i.e., an equally weighted average of normalized scores. However, there is scope for optimization. Statistical learning offers methods for sequentially choosing the best model class and other hyperparameters for signal generation, thus supporting realistic backtests and automated operation of strategies.

The post and Jupyter Notebook below show sequential signal optimization implementations with the scikit-learn package and some specialized extensions. In particular, the post applies statistical learning to sequential optimization of three important tasks: feature selection, return prediction, and market regime classification.

View the full post here on Macrosynergy Research.

A Jupyter notebook for audit and replication of the research results can be downloaded here. The notebook operation requires access to J.P. Morgan DataQuery. For free data access, use an equivalent Jupyter Notebook on Kaggle here.

J.P. Morgan offers free trials for institutional clients. Also, an academic research support program sponsors data sets for relevant projects.