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Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018)
Subtitle of host publicationProceedings of a meeting held 18-21 November 2018, Bangalore, India
EditorsSuresh Sundaram
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Pages1876-1883
Number of pages8
ISBN (Electronic)9781538692769
ISBN (Print)9781538692776
DOIs
DateAccepted/In press - 1 Sep 2018
DateE-pub ahead of print - 18 Nov 2018
DatePublished (current) - 28 Jan 2019
Event8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India
Duration: 18 Nov 201821 Nov 2018

Conference

Conference8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
CountryIndia
CityBangalore
Period18/11/1821/11/18

Abstract

Here we report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.

    Research areas

  • Financial Engineering, Financial Markets, Automated Trading, Intelligent Agents, Deep Learning

Event

8th IEEE Symposium Series on Computational Intelligence, SSCI 2018

Duration18 Nov 201821 Nov 2018
CityBangalore
CountryIndia

Event: Conference

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/8628854 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 484 KB, PDF-document

DOI

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