  Financial markets are difficult to predict due to its complex systems
dynamics. Although there have been some recent studies that use machine
learning techniques for financial markets prediction, they do not offer
satisfactory performance on financial returns. We propose a novel
one-dimensional convolutional neural networks (CNN) model to predict financial
market movement. The customized one-dimensional convolutional layers scan
financial trading data through time, while different types of data, such as
prices and volume, share parameters (kernels) with each other. Our model
automatically extracts features instead of using traditional technical
indicators and thus can avoid biases caused by selection of technical
indicators and pre-defined coefficients in technical indicators. We evaluate
the performance of our prediction model with strictly backtesting on historical
trading data of six futures from January 2010 to October 2017. The experiment
results show that our CNN model can effectively extract more generalized and
informative features than traditional technical indicators, and achieves more
robust and profitable financial performance than previous machine learning
approaches.
