High-Frequency Trading Strategy Based on Deep Neural Networks

By Andrés Arévalo, Jaime Niño, G. Hernández, Javier Sandoval. 2016

97 citations recorded by the source corpus.

This paper presents a high-frequency strategy based on Deep Neural Networks (DNNs). The DNN was trained on current time (hour and minute), and \( n \)-lagged one-minute pseudo-returns, price standard deviations and trend indicators in order to forecast the next one-minute average price. The DNN predictions are used to build a high-frequency trading strategy that buys (sells) when the next predicted average price is above (below) the last closing price. The data used for training and testing are the AAPL tick-by-tick transactions from September to November of 2008. The best-found DNN has a 66 % of directional accuracy. This strategy yields an 81 % successful trades during testing period.

Subfields: Management Science and Operations Research, Signal Processing

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Citations in the corpus

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018, background): “Most of the related articles use the traditional data partitioning method; that is, the entire dataset is directly split into training set and testing set [12, 22, 40, 42].”
A Double-Layer Neural Network Framework for High-Frequency Forecasting (2017, background): “2009; Pai and Lin 2005; Tsai and Hsiao 2010], and the machine learning-based approaches [Kara et al. 2011; Patel et al. 2015; Arévalo et al. 2016; Pai and Lin 2005; Hsu et al. 2009].”
Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications (2018, methodology): “Closer to the problem faced in this paper, Arévalo, Niño, Hernández and Sandoval [14] propose a High-Frequency Trading (HFT) strategy based on the output given by a DNN used to forecast the next 1-minute average price.”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “In (Ar´evalo et al., 2016), authors used a deep ANN with ﬁve hidden layers to forecast Apple Inc. ' s stock price.”
A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning (2019, methodology): “• Stock Market Analysis Arevalo et al. [86] proposed a method for the stock market prediction, analysis and precision by using data of US Apple stock (3 × {2 − 15} + 2) and target output will be Stock price for 19,109 numbers of samples.”