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

2020/08/27 by Sangadiev, Aiusha, Rivera-Castro, Rodrigo, Stepanov, Kirill +5
#Applications (stat.AP) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Trading and Market Microstructure (q-fin.TR)

paper · doi:10.48550/arxiv.2008.12152

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.

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