Using Deep Learning for price prediction by exploiting stationary limit order book features

Understand

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.