Long-only cryptocurrency portfolio management by ranking the assets: a neural network approach

TL;DR

This paper introduces a neural network-based ranking method for managing cryptocurrency portfolios, leveraging cross-sectional relationships to outperform existing strategies across various market conditions.

Contribution

It proposes a novel ranking approach that manages multiple cryptocurrencies simultaneously, incorporating relative relationships to improve portfolio performance.

Findings

Achieved a Sharpe ratio of 1.01 and an annualized return of 64.26%.

Outperformed existing methods across bullish, bearish, and stagnant markets.

Demonstrated robustness to increased transaction fees.

Abstract

This paper will propose a novel machine learning based portfolio management method in the context of the cryptocurrency market. Previous researchers mainly focus on the prediction of the movement for specific cryptocurrency such as the bitcoin(BTC) and then trade according to the prediction. In contrast to the previous work that treats the cryptocurrencies independently, this paper manages a group of cryptocurrencies by analyzing the relative relationship. Specifically, in each time step, we utilize the neural network to predict the rank of the future return of the managed cryptocurrencies and place weights accordingly. By incorporating such cross-sectional information, the proposed methods is shown to profitable based on the backtesting experiments on the real daily cryptocurrency market data from May, 2020 to Nov, 2023. During this 3.5 years, the market experiences the full cycle of bullish, bearish and stagnant market conditions. Despite under such complex market conditions, the proposed method outperforms the existing methods and achieves a Sharpe ratio of 1.01 and annualized return of 64.26%. Additionally, the proposed method is shown to be robust to the increase of transaction fee.