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Machine Learning-Based Trading Robot for Foreign Exchange (FOREX)

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Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 721))

Abstract

Financial markets are extremely complex due to their non-linear, non- stationary, and time-variant nature. Researchers are increasingly investigating the automation of financial trading as AI capabilities advance particularly in complicated markets such as Foreign Exchange. (FOREX). Traders typically use three types of trading analysis: technical analysis, which uses historical price data to perform mathematical computations; fundamental analysis, which investigates economic factors influencing price movement; and sentiment analysis, which investigates the emotional movement due to market news. There have been a variety of integration techniques utilized in prior attempts to combine AI prediction models with these approaches. Federated learning, which might integrate the learning capacity of dispersed models, has not yet been used by anyone. This study proposes a FOREX trading robot that uses federated learning to aggregate trading analysis methodologies, specifically analyzing market news sentiment and technical calculations based on historical prices for a specific currency pair to determine when to buy or sell for maximum profit. The implementation of federated learning is a future work that we intend to test in the near future. This is the first research work to study the deployment of federated learning in the financial trading field.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates

    Fatima Mohamad Dakalbab & Manar Abu Talib

  2. Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Qassim Nasir

Authors
  1. Fatima Mohamad Dakalbab
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  2. Manar Abu Talib
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  3. Qassim Nasir
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Corresponding author

Correspondence to Manar Abu Talib .

Editor information

Editors and Affiliations

  1. University of Detroit Mercy, Farmington Hills, MI, USA

    Kevin Daimi

  2. Asia Pacific International College, Sydney, NSW, Australia

    Abeer Al Sadoon

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Dakalbab, F.M., Talib, M.A., Nasir, Q. (2023). Machine Learning-Based Trading Robot for Foreign Exchange (FOREX). In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_17

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