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AURORA: an autonomous agent-oriented hybrid trading service

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Abstract

Stock markets play an essential role in the economy and offer companies opportunities to grow, and insightful investors to make profits. Many tools and techniques have been proposed and applied to analyze the overall market behavior to seize such opportunities. However, understanding the stock exchange’s intrinsic rules and taking opportunities are not trivial tasks. With that in mind, this work proposes AURORA: a new hybrid service to trade equities in the stock market, using an autonomous agent-based approach. The goal is to offer a reliable service based on technical and fundamental analysis with precision and stability in the decision-making process. For this, AURORA’s intelligence is modeled using a rational agent capable of perceiving the market and acting upon its perception autonomously. When compared with other solutions in the literature, the proposed service shows that it can predict the gain or loss of value at the price of a stock with an accuracy higher than 82.86% in the worst case and 89.23% in the best case. Furthermore, the proposed service can achieve a profitability of 11.74%, overcoming fixed-income investments, and portfolios built with the Markowitz Mean-Variance model.

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Notes

  1. Available at https://github.com/EmpyreanAI/AURORA.

  2. Available at: https://spinningup.openai.com.

  3. Available at: https://gym.openai.com.

  4. Available at http://www.b3.com.br/.

  5. Available at https://github.com/hyperopt/hyperopt.

  6. Parameterized with \(C=0.1\) and \(\gamma =1\).

  7. Parameterized using the DTW metric.

  8. Values from: http://www.yahii.com.br/poupanca.html.

  9. Values from: http://www.yahii.com.br/cetip13a21.html.

  10. Strategy used by investors who decide to buy an asset for the long term.

  11. It is worth emphasizing that not all selected actions are performed. If a purchase action is required, and the agent does not have the amount of money to buy the asset, it will not be performed. With this, the agent receives punishment for the action performed incorrectly. The same logic applies similarly to sales.

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Acknowledgements

The authors thank the Coordination for the Improvement of Higher Education Personnel (CAPES) and the São Paulo Research Foundation (FAPESP) grants 19/14429-5, 15/50122-0 for the financial support to develop this research.

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Authors and Affiliations

  1. Department of Computer Science, University of Brasília (UnB), Brasilia, DF, Brazil

    Renato A. Nobre, Khalil C. do Nascimento & Geraldo P. Rocha Filho

  2. Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom

    Patricia A. Vargas

  3. Department of Computing, Federal University of São Carlos (UFSCar), São Carlos, Brazil

    Alan Demétrius Baria Valejo

  4. Vale Institute of Technology, Robotics Laboratory, Ouro Preto, MG, Brazil

    Gustavo Pessin

  5. Institute of Computing, University of Campinas (UNICAMP), Campinas, SP, Brazil

    Leandro A. Villas

  6. Computer Science Department, Università degli Studi di Milano, Milano, Italy

    Renato A. Nobre

Authors
  1. Renato A. Nobre
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  2. Khalil C. do Nascimento
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  3. Patricia A. Vargas
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  4. Alan Demétrius Baria Valejo
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  5. Gustavo Pessin
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  6. Leandro A. Villas
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  7. Geraldo P. Rocha Filho
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Corresponding author

Correspondence to Geraldo P. Rocha Filho.

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Conflicts of interest/Competing interests

The authors (Renato A. Nobre, Khalil C. do Nascimento, Patricia A. Vargas, Alan D. B. Valejo, Gustavo Pessin, Leandro A. Villas and Geraldo P. Rocha Filho) declare that there is no conflict of interest.

Availability of data and material

All used data are public available on the B3 stock market website: http://www.b3.com.br/.

Code availability

Every code developed is public available on Github: https://github.com/EmpyreanAI.

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Nobre, R.A., Nascimento, K.C.d., Vargas, P.A. et al. AURORA: an autonomous agent-oriented hybrid trading service. Neural Comput & Applic 34, 2217–2232 (2022). https://doi.org/10.1007/s00521-021-06508-3

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  • DOI: https://doi.org/10.1007/s00521-021-06508-3

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