  We exploit cutting-edge deep learning methodologies to explore the
predictability of high-frequency Limit Order Book mid-price changes for a
heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we
release `LOBFrame', an open-source code base to efficiently process large-scale
Limit Order Book data and quantitatively assess state-of-the-art deep learning
models' forecasting capabilities. Our results are twofold. We demonstrate that
the stocks' microstructural characteristics influence the efficacy of deep
learning methods and that their high forecasting power does not necessarily
correspond to actionable trading signals. We argue that traditional machine
learning metrics fail to adequately assess the quality of forecasts in the
Limit Order Book context. As an alternative, we propose an innovative
operational framework that evaluates predictions' practicality by focusing on
the probability of accurately forecasting complete transactions. This work
offers academics and practitioners an avenue to make informed and robust
decisions on the application of deep learning techniques, their scope and
limitations, effectively exploiting emergent statistical properties of the
Limit Order Book.
