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Continual portfolio selection in dynamic environments via incremental reinforcement learning

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Abstract

Portfolio selection, as an important topic in the finance community, has attracted increased attention from artificial intelligence practitioners. Recently, the reinforcement learning (RL) paradigm, with the self-learning and model-free property, provides a promising candidate to solve complex portfolio selection tasks. Traditional research on RL-based portfolio selection focuses on batch-mode stationary problems, where all the market data is assumed to be available for the one-time training process. However, the real-world financial markets are often dynamic where the streaming data increments with new patterns keep emerging continually. In the paper, we address the continual portfolio selection problem in such dynamic environments. We propose to utilize the incremental RL approach with a two-step solution for efficiently adjusting the existing portfolio policy to a new one when the market changes as a new data increment comes. The first step, policy relaxation, forces the agent to execute a relaxed policy for encouraging a sufficient exploration in the new market. The second step, importance weighting, puts emphasis on learning samples consisting of more new information for stimulating the existing portfolio policy to more rapidly adapt to the new market. Evaluation results on real-world portfolio tasks verify the effectiveness and superiority of our method for addressing the continual portfolio selection in dynamic environments.

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Notes

  1. https://www.kaggle.com/camnugent/sandp500.

  2. Poloniex’s official API: https://poloniex.com/support/api/.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant no. 62006111, and the Natural Science Foundation of Jiangsu Province of China under Grant BK20200330.

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Authors and Affiliations

  1. School of Finance, Zhongnan University of Economics and Law, Wuhan, 430073, China

    Shu Liu

  2. School of Management and Engineering, Nanjing University, Nanjing, 210093, China

    Bo Wang, Huaxiong Li, Chunlin Chen & Zhi Wang

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  1. Shu Liu
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  3. Huaxiong Li
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Correspondence to Zhi Wang.

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Liu, S., Wang, B., Li, H. et al. Continual portfolio selection in dynamic environments via incremental reinforcement learning. Int. J. Mach. Learn. & Cyber. 14, 269–279 (2023). https://doi.org/10.1007/s13042-022-01639-y

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