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High-Frequency Trading Strategy Based on Deep Neural Networks

  • Andrés Arévalo
  • Jaime Niño
  • German Hernández
  • Javier Sandoval
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9773)

Abstract

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.

Keywords

Computational finance High-frequency trading Deep neural networks 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrés Arévalo
    • 1
  • Jaime Niño
    • 1
  • German Hernández
    • 1
  • Javier Sandoval
    • 2
  1. 1.Universidad Nacional de ColombiaBogotáColombia
  2. 2.Algocodex Research InstituteUniversidad ExternadoBogotáColombia

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