how to retrain model with periodic new features?
The way gradient boosting is constructed and trained, there is not an obvious solution for this without just training from scratch. Other models might be more suitable for this (Adding this to a neural network and retraining this will take less time than from scratch I think). Another approach would be to use these time-based lag features as a time series, that way you just input your features and it will learn relationships between the past few weeks instead.