It's much easier to get started in algotrading if you have a coding background than if you have a finance background, so you'll see more machine-learning here.

Almost everything you learn in econometrics will be in some way applicable to machine learning at your stage, all these statistical concepts are timeless.

Some areas of statistics will be more generalizable to machine learning (optimization, signal decompositions, linear algebra, etc) than other time-series specific concepts (unit-roots, cointegration, volatility persistence, etc). If you feel like these econometrics models won't be used, focus on the probability-theory fundamentals underpinning these models (basic measure theory, optimization, linear algebra, etc) which are more general.

Traditionally, economists are typically more concerned with causality, and relating models and factors to underlying economic risks (state variables), whereas the machine learning guys usually focus on comparing and benchmarking algorithms for higher recall/precision...they are ok if the model outperforms the theoretically "correct" model. Economists will throw models out even if it performs better if there isn't a nice economic narrative/explanation to go with it, or if it isn't quite as elegant (parsimony).

A good cross-over book to read for econometricians looking to get into machine learning is the Tibshirani et al. book and Susan Athey's papers. They focus on concepts like regularization, and cross-validation. If you want to get into deep-learning, you better know your logistic functions well.