A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem


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.

In Arxiv
Zhengyao Jiang
Zhengyao Jiang
PhD Student of Machine Learning

I’m Zhengyao Jiang, a machine learning PhD student at UCL, supervised by Tim Rocktäschel and Edward Grefenstette. I’m interested in improving the data efficiency and generalization of reinforcement learning, pushing the RL to real-world applications. To reach those goals, my research focuses on both incorporating priors with neuro-symbolic methods and leveraging off-policy/offline data. I used to work on deep learning financial applications, having the experience of algorithmic live trading on the cryptocurrency market.