  The recent surge in Deep Learning (DL) research of the past decade has
successfully provided solutions to many difficult problems. The field of
quantitative analysis has been slowly adapting the new methods to its problems,
but due to problems such as the non-stationary nature of financial data,
significant challenges must be overcome before DL is fully utilized. In this
work a new method to construct stationary features, that allows DL models to be
applied effectively, is proposed. These features are thoroughly tested on the
task of predicting mid price movements of the Limit Order Book. Several DL
models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks
and Convolutional Neural Networks (CNN). Finally a novel model that combines
the ability of CNNs to extract useful features and the ability of LSTMs' to
analyze time series, is proposed and evaluated. The combined model is able to
outperform the individual LSTM and CNN models in the prediction horizons that
are tested.
