A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

TL;DR

This paper introduces a deep reinforcement learning framework for financial portfolio management, demonstrating its effectiveness in cryptocurrency trading with significant returns and outperforming existing strategies.

Contribution

The paper proposes a novel RL framework with an ensemble topology, memory, and learning scheme, applied to cryptocurrency trading with three neural network architectures.

Findings

Outperforms other trading algorithms in backtests

Achieves at least 4-fold returns in 50 days

Monopolizes top three positions in all experiments

Abstract

Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 50 days.