Stock Order Analysis using Deep Learning and High Frequency Data

Abstract

For algorithmic trading, it is important to reduce market impact and opportunity costs that closely related to market liquidity. In this work, we propose a tick-based approach to prediction of the liquidity. Our method utilizes order data encoded according to its exibility and a Long Short-Term Memory(LSTM) that predict a next order. Accuracy of the model outperforms by a large margin maximum occurrence ratio of order labels. Furthermore, we examine the embedding layer of the trained model and find out that it obtains difference and similarity between each order.