IDEAS home Printed from https://ideas.repec.org/a/gam/jfinte/v1y2022i2p8-124d780873.html
   My bibliography  Save this article

An Ensembling Architecture Incorporating Machine Learning Models and Genetic Algorithm Optimization for Forex Trading

Author

Listed:
  • Leonard Kin Yung Loh

    (Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119516, Singapore
    These authors contributed equally to this work.)

  • Hee Kheng Kueh

    (Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119516, Singapore
    These authors contributed equally to this work.)

  • Nirav Janak Parikh

    (Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119516, Singapore
    These authors contributed equally to this work.)

  • Harry Chan

    (Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119516, Singapore
    These authors contributed equally to this work.)

  • Nicholas Jun Hui Ho

    (Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119516, Singapore)

  • Matthew Chin Heng Chua

    (Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119516, Singapore)

Abstract

Algorithmic trading has become the standard in the financial market. Traditionally, most algorithms have relied on rule-based expert systems which are a set of complex if/then rules that need to be updated manually to changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading because it can directly learn market patterns and behaviors from historical trading data and factor this into trading decisions. In this paper, a complete end-to-end system is proposed for automated low-frequency quantitative trading in the foreign exchange (Forex) markets. The system utilizes several State of the Art (SOTA) machine learning strategies that are combined under an ensemble model to derive the market signal for trading. Genetic Algorithm (GA) is used to optimize the strategies for maximizing profits. The system also includes a money management strategy to mitigate risk and a back-testing framework to evaluate system performance. The models were trained on EUR–USD pair Forex data from Jan 2006 to Dec 2019, and subsequently evaluated on unseen samples from Jan 2020 to Dec 2020. The system performance is promising under ideal conditions. The ensemble model achieved about 10% nett P&L with −0.7% drawdown level based on 2020 trading data. Further work is required to calibrate trading costs & execution slippage in real market conditions. It is concluded that with the increased market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to a changing market environment will become even stronger.

Suggested Citation

  • Leonard Kin Yung Loh & Hee Kheng Kueh & Nirav Janak Parikh & Harry Chan & Nicholas Jun Hui Ho & Matthew Chin Heng Chua, 2022. "An Ensembling Architecture Incorporating Machine Learning Models and Genetic Algorithm Optimization for Forex Trading," FinTech, MDPI, vol. 1(2), pages 1-25, March.
  • Handle: RePEc:gam:jfinte:v:1:y:2022:i:2:p:8-124:d:780873
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2674-1032/1/2/8/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2674-1032/1/2/8/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    2. Lean Yu & Shouyang Wang & Kin Keung Lai, 2007. "Foreign-Exchange-Rate Forecasting With Artificial Neural Networks," International Series in Operations Research and Management Science, Springer, number 978-0-387-71720-3, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    2. Jaydip Sen & Sidra Mehtab & Abhishek Dutta & Saikat Mondal, 2022. "Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model," Papers 2203.01326, arXiv.org.
    3. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
    4. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    5. Adebayo Oshingbesan & Eniola Ajiboye & Peruth Kamashazi & Timothy Mbaka, 2022. "Model-Free Reinforcement Learning for Asset Allocation," Papers 2209.10458, arXiv.org.
    6. Rian Dolphin & Barry Smyth & Ruihai Dong, 2024. "Contrastive Learning of Asset Embeddings from Financial Time Series," Papers 2407.18645, arXiv.org.
    7. Tomoshiro Ochiai & Jose C. Nacher, 2020. "Unveiling the directional network behind the financial statements data using volatility constraint correlation," Papers 2008.07836, arXiv.org, revised Jun 2023.
    8. Junyi Li & Xitong Wang & Yaoyang Lin & Arunesh Sinha & Micheal P. Wellman, 2020. "Generating Realistic Stock Market Order Streams," Papers 2006.04212, arXiv.org.
    9. James Wallbridge, 2020. "Transformers for Limit Order Books," Papers 2003.00130, arXiv.org.
    10. Antoine Proteau & Antoine Tahan & Ryad Zemouri & Marc Thomas, 2023. "Predicting the quality of a machined workpiece with a variational autoencoder approach," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 719-737, February.
    11. Yang Qiao & Yiping Xia & Xiang Li & Zheng Li & Yan Ge, 2023. "Higher-order Graph Attention Network for Stock Selection with Joint Analysis," Papers 2306.15526, arXiv.org.
    12. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
    13. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
    14. Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
    15. Raphael Paulo Beal Piovezan & Pedro Paulo Andrade Junior & Sérgio Luciano Ávila, 2024. "Machine Learning Method for Return Direction Forecast of Exchange Traded Funds (ETFs) Using Classification and Regression Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1827-1852, May.
    16. Kambombo Mtonga & Santhi Kumaran & Chomora Mikeka & Kayalvizhi Jayavel & Jimmy Nsenga, 2019. "Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems," Future Internet, MDPI, vol. 11(11), pages 1-24, November.
    17. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    18. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    19. Pegah Eslamieh & Mehdi Shajari & Ahmad Nickabadi, 2023. "User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks," Mathematics, MDPI, vol. 11(13), pages 1-26, July.
    20. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jfinte:v:1:y:2022:i:2:p:8-124:d:780873. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.