  The present work addresses theoretical and practical questions in the domain
of Deep Learning for High Frequency Trading. State-of-the-art models such as
Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention
mask, CNN-LSTMs and MLPs are reviewed and compared on the same tasks, feature
space and dataset, and then clustered according to pairwise similarity and
performance metrics. The underlying dimensions of the modeling techniques are
hence investigated to understand whether these are intrinsic to the Limit Order
Book's dynamics. We observe that the Multilayer Perceptron performs comparably
to or better than state-of-the-art CNN-LSTM architectures indicating that
dynamic spatial and temporal dimensions are a good approximation of the LOB's
dynamics, but not necessarily the true underlying dimensions.
