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.

本文提出了基于一维卷积神经网络的金融市场预测模型，并通过历史交易数据严格回测，证明了该模型可以更有效地提取更一般化和信息丰富的特征，实现比之前的机器学习方法更强大且更有利润性的金融绩效。