  The success of deep learning-based limit order book forecasting models is
highly dependent on the quality and the robustness of the input data
representation. A significant body of the quantitative finance literature
focuses on utilising different deep learning architectures without taking into
consideration the key assumptions these models make with respect to the input
data representation. In this paper, we highlight the issues associated with the
commonly-used representations of limit order book data from both a theoretical
and practical perspectives. We also show the fragility of the representations
under adversarial perturbations and propose two simple modifications to the
existing representations that match the theoretical assumptions of deep
learning models. Finally, we show experimentally how our proposed
representations lead to state-of-the-art performance in both accuracy and
robustness utilising very simple neural network architectures.
