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Transferring trading strategy knowledge to deep learning models

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Abstract

Trading strategies are constantly being employed in the financial markets in order to increase consistency, reduce human errors of judgment and boost the probability of taking profitable market positions. In this work, we attempt to transfer the knowledge of several different types of trading strategies to deep learning models. The trading strategies are applied on price data of foreign exchange trading pairs and are actual strategies used in production trading environments. Along with our approach to transfer the strategy knowledge, we introduce a preprocessing method of the original price candles making it suitable for use with Neural Networks. Our results suggest that the deep models that are tested perform better than simpler models and they can accurately learn a variety of trading strategies.

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Acknowledgements

We kindly thank SpeedLab AG for providing their expertise on the matter of FOREX trading and the comprehensive dataset of FOREX currency pairs. This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (Project code: T2EDK-02094). Avraam Tsantekidis was solely funded by a scholarship from the State Scholarship Foundation (IKY) according to the “Strengthening Human Research Resources through the Pursuit of Doctoral Research” act, with resources from the “Human Resources Development, Education and Lifelong Learning 2014–2020” program.

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  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Avraam Tsantekidis & Anastasios Tefas

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  1. Avraam Tsantekidis
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  2. Anastasios Tefas
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Correspondence to Avraam Tsantekidis.

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Tsantekidis, A., Tefas, A. Transferring trading strategy knowledge to deep learning models. Knowl Inf Syst 63, 87–104 (2021). https://doi.org/10.1007/s10115-020-01510-y

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