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DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data

Author

Listed:
  • Aiusha Sangadiev
  • Rodrigo Rivera-Castro
  • Kirill Stepanov
  • Andrey Poddubny
  • Kirill Bubenchikov
  • Nikita Bekezin
  • Polina Pilyugina
  • Evgeny Burnaev

Abstract

This work proposes DeepFolio, a new model for deep portfolio management based on data from limit order books (LOB). DeepFolio solves problems found in the state-of-the-art for LOB data to predict price movements. Our evaluation consists of two scenarios using a large dataset of millions of time series. The improvements deliver superior results both in cases of abundant as well as scarce data. The experiments show that DeepFolio outperforms the state-of-the-art on the benchmark FI-2010 LOB. Further, we use DeepFolio for optimal portfolio allocation of crypto-assets with rebalancing. For this purpose, we use two loss-functions - Sharpe ratio loss and minimum volatility risk. We show that DeepFolio outperforms widely used portfolio allocation techniques in the literature.

Suggested Citation

  • Aiusha Sangadiev & Rodrigo Rivera-Castro & Kirill Stepanov & Andrey Poddubny & Kirill Bubenchikov & Nikita Bekezin & Polina Pilyugina & Evgeny Burnaev, 2020. "DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data," Papers 2008.12152, arXiv.org.
    • Handle: RePEc:arx:papers:2008.12152
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    References listed on IDEAS

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    7. Ioanid Rosu, 2010. "Liquidity and Information in Order Driven markets," Post-Print hal-00543544, HAL.
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    Cited by:

    1. Zihao Zhang & Stefan Zohren, 2021. "Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units," Papers 2105.10430, arXiv.org, revised Aug 2021.

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