Accelerating Machine Learning Training Time for Limit Order Book Prediction

TL;DR

This paper demonstrates how GPU acceleration significantly reduces training time for a limit order book prediction algorithm, enabling more efficient model development in financial machine learning.

Contribution

It introduces GPU-based acceleration for a specific financial machine learning task, improving training speed and facilitating extensive model iteration.

Findings

GPU acceleration reduces training time substantially.

Faster training enables more comprehensive model development.

GPU deployment is effective in high-frequency trading data analysis.

Abstract

Financial firms are interested in simulation to discover whether a given algorithm involving financial machine learning will operate profitably. While many versions of this type of algorithm have been published recently by researchers, the focus herein is on a particular machine learning training project due to the explainable nature and the availability of high frequency market data. For this task, hardware acceleration is expected to speed up the time required for the financial machine learning researcher to obtain the results. As the majority of the time can be spent in classifier training, there is interest in faster training steps. A published Limit Order Book algorithm for predicting stock market direction is our subject, and the machine learning training process can be time-intensive especially when considering the iterative nature of model development. To remedy this, we deploy Graphical Processing Units (GPUs) produced by NVIDIA available in the data center where the computer architecture is geared to parallel high-speed arithmetic operations. In the studied configuration, this leads to significantly faster training time allowing more efficient and extensive model development.