Classification-based Financial Markets Prediction using Deep Neural Networks

TL;DR

This paper explores the use of deep neural networks for predicting financial market directions and demonstrates their application in backtesting trading strategies on commodity and FX futures.

Contribution

It introduces a novel application of DNNs to algorithmic trading, including configuration, training, and an open-source implementation for financial prediction.

Findings

DNNs achieved robust prediction accuracy.

The implementation was 11.4x faster with co-processor acceleration.

Open source code enables reproducibility and further research.

Abstract

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. In particular we describe the configuration and training approach and then demonstrate their application to backtesting a simple trading strategy over 43 different Commodity and FX future mid-prices at 5-minute intervals. All results in this paper are generated using a C++ implementation on the Intel Xeon Phi co-processor which is 11.4x faster than the serial version and a Python strategy backtesting environment both of which are available as open source code written by the authors.