Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture

The Momentum Transformer is described by its authors as an attention-LSTM hybrid that “outperforms benchmark time-series momentum and mean-reversion trading strategies”, improves performance on returns net of transaction costs, and adapts to new market regimes such as the SARS-CoV-2 crisis. The work comes from the Oxford-Man Institute of Quantitative Finance and the Oxford Internet Institute at the University of Oxford. It has stayed a preprint: v1 was submitted in December 2021 and v3 in November 2022, and no peer-reviewed venue could be found for it.

What the paper measures and what the released code can measure are not the same experiment. The paper’s results come from a portfolio of 50 of the most liquid continuous futures contracts over 1990–2020, drawn from the commercial Pinnacle Data Corp CLC database. The authors’ own MIT-licensed implementation instead runs on the free Nasdaq Data Link (Quandl) continuous-futures dataset, which needs an account but no paid subscription — so numbers reproduced from the repository should not be read as the paper’s numbers.

That implementation is the canonical one, named in the paper text itself, but it is effectively dormant: the last substantive code commit is March 2022 and everything since has been README edits. requirements.txt is pinned to 2021-era versions (absl-py 0.13.0, gpflow 2.2.1, h5py 2.10.0), so a clean install against a current Python and TensorFlow will need dependency work first.