Deep reinforcement learning for portfolio management of markets with a dynamic number of assets

Betancourt, Carlos; Chen, Wenhui

doi:10.1016/j.eswa.2020.114002

Cited by 49 publications

(46 citation statements)

References 18 publications

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“…This work follows that line of study and tackles some important issues that were not covered in the mentioned study. One of the differences between this study and [22] is the architecture of the NN. In [22], the combination between the extracted features from the assets is a softmax layer.…”

Section: Literature Reviewmentioning

confidence: 84%

“…One of the differences between this study and [22] is the architecture of the NN. In [22], the combination between the extracted features from the assets is a softmax layer. That layer simply normalizes the values proposed by the feature extractor to generate the final output.…”

Section: Literature Reviewmentioning

confidence: 84%

“…Therefore, the new blending mechanism has a general overview of the market as well as the individual information of the assets, allowing it to generate more reliable outputs. Another difference between this work and [22] is the inclusion of IAT in the asset exchange process. This allows the new method to spent the optimal amount of capital during the transactions, which is an important issue when assets are exchanged constantly.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In a previous work [22], we proposed a DRL method for cryptocurrency trading, which can adapt to new assets introduced suddenly to the market. This work follows that line of study and tackles some important issues that were not covered in the mentioned study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Betancourt and Chen [22] 2021 Proposed a NN architecture for markets with a dynamic number of assets.…”

Section: Year Main Contributionsmentioning

confidence: 99%

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Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading

Betancourt

Chen

2021

Applied Sciences

Self Cite

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This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financial assets such as stocks. To overcome the aforementioned problems, we propose a deep reinforcement learning (DRL) approach for cryptocurrency trading. The proposed trading system contains a self-attention network trained using an actor-critic DRL algorithm. Cryptocurrency markets contain hundreds of assets, allowing greater investment diversification, which can be accomplished if all the assets are analyzed against one another. Self-attention networks are suitable for dealing with the problem because the attention mechanism can process long sequences of data and focus on the most relevant parts of the inputs. Transaction fees are also considered in formulating the studied problem. Systems that perform trades in high frequencies cannot overlook this issue, since, after many trades, small fees can add up to significant expenses. To validate the proposed approach, a DRL environment is built using data from an important cryptocurrency market. We test our method against a state-of-the-art baseline in two different experiments. The experimental results show the proposed approach can obtain higher daily profits and has several advantages over existing methods.

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Section: Literature Reviewmentioning

confidence: 84%

Section: Literature Reviewmentioning

confidence: 84%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…Betancourt and Chen [22] 2021 Proposed a NN architecture for markets with a dynamic number of assets.…”

Section: Year Main Contributionsmentioning

confidence: 99%

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Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading

Betancourt

Chen

2021

Applied Sciences

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Cryptocurrencies trading algorithms: A review

Silva

Hadad

Oliveira

2022

Journal of Forecasting

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This study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies.

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Equity Portfolio Optimization Using Reinforcement Learning: Emerging Market Case

Candar

Üstündağ

2022

Lecture Notes in Networks and Systems

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Deep reinforcement learning for portfolio management of markets with a dynamic number of assets

Cited by 49 publications

References 18 publications

Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading

Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading

Cryptocurrencies trading algorithms: A review

Equity Portfolio Optimization Using Reinforcement Learning: Emerging Market Case

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