{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-deep-reinforcement-learning-framework-for","title":"A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem","arxiv_id":"1706.10059","date":"2017-06-30","proceeding":null,"authors":["Zhengyao Jiang","Dixing Xu","Jinjun Liang"],"abstract":"Financial portfolio management is the process of constant redistribution of a\nfund into different financial products. This paper presents a\nfinancial-model-free Reinforcement Learning framework to provide a deep machine\nlearning solution to the portfolio management problem. The framework consists\nof the Ensemble of Identical Independent Evaluators (EIIE) topology, a\nPortfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL)\nscheme, and a fully exploiting and explicit reward function. This framework is\nrealized in three instants in this work with a Convolutional Neural Network\n(CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory\n(LSTM). They are, along with a number of recently reviewed or published\nportfolio-selection strategies, examined in three back-test experiments with a\ntrading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are\nelectronic and decentralized alternatives to government-issued money, with\nBitcoin as the best-known example of a cryptocurrency. All three instances of\nthe framework monopolize the top three positions in all experiments,\noutdistancing other compared trading algorithms. Although with a high\ncommission rate of 0.25% in the backtests, the framework is able to achieve at\nleast 4-fold returns in 50 days.","url_abs":"http://arxiv.org/abs/1706.10059v2","url_pdf":"http://arxiv.org/pdf/1706.10059v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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