Algorithmic Trading Using Deep Neural Networks on High Frequency Data

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 742)


In this work, a high-frequency trading strategy using Deep Neural Networks (DNNs) is presented. The input information consists of: (i). Current time (hour and minute); (ii). The last n one-minute pseudo-returns, where n is the sliding window size parameter; (iii). The last n one-minute standard deviations of the price; (iv). The last n trend indicator, computed as the slope of the linear model fitted using the transaction prices inside a particular minute. The DNN predicts the next one-minute pseudo-return, this output is later transformed to obtain a the next predicted one-minute average price. This price is used to build a high-frequency trading strategy that buys (sells) when the next predicted average price is above (below) the last closing price.


Short-term forecasting High-frequency trading Computational finance Algorithmic trading Deep Neural Networks 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Universidad Nacional de ColombiaBogotáColombia
  2. 2.Universidad ExternadoBogotáColombia

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