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

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  • 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
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    References listed on IDEAS

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    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.
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