IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1909.10801.html
   My bibliography  Save this paper

WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series

Author

Listed:
  • Michael Poli
  • Jinkyoo Park
  • Ilija Ilievski

Abstract

Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability. Non-deliverable-forwards (NDF), a derivatives contract used in foreign exchange (FX) trading, presents additional difficulty in the form of long-term planning required for an effective selection of start and end date of the contract. In this work, we focus on tackling the problem of NDF tenor selection by leveraging high-dimensional sequential data consisting of spot rates, technical indicators and expert tenor patterns. To this end, we construct a dataset from the Depository Trust & Clearing Corporation (DTCC) NDF data that includes a comprehensive list of NDF volumes and daily spot rates for 64 FX pairs. We introduce WaveATTentionNet (WATTNet), a novel temporal convolution (TCN) model for spatio-temporal modeling of highly multivariate time series, and validate it across NDF markets with varying degrees of dissimilarity between the training and test periods in terms of volatility and general market regimes. The proposed method achieves a significant positive return on investment (ROI) in all NDF markets under analysis, outperforming recurrent and classical baselines by a wide margin. Finally, we propose two orthogonal interpretability approaches to verify noise stability and detect the driving factors of the learned tenor selection strategy.

Suggested Citation

  • Michael Poli & Jinkyoo Park & Ilija Ilievski, 2019. "WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series," Papers 1909.10801, arXiv.org.
    • Handle: RePEc:arx:papers:1909.10801
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1909.10801
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adamantios Ntakaris & Giorgio Mirone & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Feature Engineering for Mid-Price Prediction with Deep Learning," Papers 1904.05384, arXiv.org, revised Jun 2019.
    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. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
    2. Adamantios Ntakaris & Moncef Gabbouj & Juho Kanniainen, 2023. "Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting," Papers 2304.09840, arXiv.org, revised May 2023.
    3. Xuekui Zhang & Yuying Huang & Ke Xu & Li Xing, 2023. "Novel modelling strategies for high-frequency stock trading data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    4. Abbasimehr, Hossein & Paki, Reza, 2021. "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    5. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Mid-price prediction based on machine learning methods with technical and quantitative indicators," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-39, June.
    6. Dat Thanh Tran & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Data Normalization for Bilinear Structures in High-Frequency Financial Time-series," Papers 2003.00598, arXiv.org, revised Jul 2020.
    7. Martin Magris & Mostafa Shabani & Alexandros Iosifidis, 2022. "Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets," Papers 2203.03613, arXiv.org, revised Jan 2023.
    8. Parisa Golbayani & Dan Wang & Ionut Florescu, 2020. "Application of Deep Neural Networks to assess corporate Credit Rating," Papers 2003.02334, arXiv.org.
    9. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators," Papers 1907.09452, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:1909.10801. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.