  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.
