
A statistical learning workflow for macro trading strategies
Statistical learning for macro trading involves model training, model validation and learning method testing. A simple workflow [1] determines form and parameters of trading models, [2] chooses the best of these models based on past out-of-sample performance, and [3] assesses the value of the deployed learning method based on further out-of-sample results. A convenient technology is the ‘list-column workflow’ based on the tidyverse packages in R. It stores all related objects in a single data table, including models and nested data sets, and implements statistical learning through functional programming on that table. Key steps are [1] the creation of point-in-time data sets that represent information available at a particular date in the past, [2] the estimation of different model types based on initial training sets prior to each point in time, [3] the evaluation of these different model types based on subsequent validation data just before each point in time, and [4] the testing of the overall learning method based on testing data at each point in time.