DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data
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.
Cite
@article{arxiv.2008.12152,
title = {DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data},
author = {Aiusha Sangadiev and Rodrigo Rivera-Castro and Kirill Stepanov and Andrey Poddubny and Kirill Bubenchikov and Nikita Bekezin and Polina Pilyugina and Evgeny Burnaev},
journal= {arXiv preprint arXiv:2008.12152},
year = {2020}
}