QxEAI: Assisting equity trading with quantum-like evolutionary algorithm
DOI:
https://doi.org/10.51094/jxiv.1011Keywords:
probabilistic forecasting, genetic programming, quantum-like evolutionary algorithmAbstract
Knowing when to buy or sell at the right time is crucial when it comes to trading on the stock market, but many times this is easier said than done. This paper proposes QxEAI, a quantum-like evolutionary algorithm, that produces action sequences to assist traders to buy or sell in the decision-making process. QxEAI utilizes the quantum principle of superposition and Darwinian natural selection to confront the dual uncertainty of the market and the traders’ actions as well as the interactivity between the traders participating and the market. Using the Dow Jones Index as a medium, QxEAI is able to produce a forecast of the following week with odds of 80% by training the preceding four weeks of data.
Conflicts of Interest Disclosure
The authors declare that they may have relevant commercial interests in the sales and profits of the commercial tool they developed for their corporation XINVISIONQ, INC. that is based off the methodology presented in this paper but this did not, in any way influence the legitimacy and authenticity of the results and conclusions presented in this paper in accordance to the ethical requirements of academic research. The authors have no other conflicting academic interests to declare as the methodology formulated in this paper are the authors’ original work.Downloads *Displays the aggregated results up to the previous day.
References
Jan G. De Gooijer, Rob J. Hyndman, 25 years of time series forecasting, International Journal of Forecasting, Volume 22, Issue 3, 2006, Pages 443-473.
Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco: Holden Day (revised ed. 1976).
Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted averages. O.N.R. Memorandum 52/1957, Carnegie Institute of Technology. Reprinted with discussion in 2004. International Journal of Forecasting, 20, 5–13.
Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324–342.
Gardner Jr., E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4, 1–38.
Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(3), 1–22.
Alsharef, A., Aggarwal, K., Sonia et al. Review of ML and AutoML Solutions to Forecast Time-Series Data. Arch Computate Methods Eng 29, 5297–5311 (2022).
David Salinas, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, DeepAR: Probabilistic forecasting with autoregressive recurrent networks, International Journal of Forecasting, Volume 36, Issue 3, 2020, Pages 1181-1191.
Wastergaard, G.; Erden, U.; Mateo, O.A.; Lampo, S.M.; Akinci, T.C.; Topsakal, O. Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets. Information 2024, 15, 39.
Meisenbacher, S., Turowski, M., Phipps, K., Rätz, M., Müller, D., Hagenmeyer, V., & Mikut, R. (2022). Review of automated time series forecasting pipelines. WIREs Data Mining and Knowledge Discovery, 12(6), e1475.
Administrative Behavior Herbert A. Simon. The Free Press, 1997.
Matthias Klaes, Esther-Mirjam Sent; A Conceptual History of the Emergence of Bounded Rationality. History of Political Economy 1 March 2005; 37 (1): 27–59. doi: https://doi.org/10.1215/00182702-37-1-27
Feynman, Richard P. The Feynman Lectures on Physics. Volume III, Quantum Mechanics. Basic Books, 2015.
Dirac, P.A.M. The Principles of Quantum Mechanics; Oxford University Press: Oxford, UK, 1958.
Von Neumann, J. Mathematical Foundations of Quantum Theory; Princeton University Press: Princeton, NJ, USA, 1932.
Xin, L.; Xin, H. Decision-making under uncertainty—A quantum value operator approach. Int. J. Theor. Phys. 2023, 62, 48.
Xin, L.; Xin, K.; Xin, H. On Laws of Thought—A Quantum-like Machine Learning Approach. Entropy 2023, 25, 1213. https://doi.org/ 10.3390/e25081213
Holland, J. Adaptation in Natural and Artificial System; University of Michigan Press: Ann Arbor, MI, USA, 1975.
Koza, J.R. Genetic Programming, on the Programming of Computers by Means of Natural Selection; MIT Press: Cambridge, MA, USA, 1992.
Koza, J.R. Genetic Programming II, Automatic Discovery of Reusable Programs; MIT Press: Cambridge, MA, USA, 1994.
Darwin, Charles, 1809-1882. On the Origin of Species by Means of Natural Selection, or Preservation of Favoured Races in the Struggle for Life. London: John Murray, 1859.
Von Neumann, J.; Morgenstern, O. Theory of Games and Economic Behavior; Princeton University Press: Princeton, NJ, USA, 1944.
Smith, Adam. An Inquiry into the Nature and Causes of the Wealth of Nations. London: Harriman House, 2007.
Mankiw, N Gregory. Principles of Microeconomics 10th Edition. Cengage Learning, 2023.
Kevin Xin, Lizhi Xin. QxEAI - Automated probabilistic forecasting with Quantum-like evolutionary algorithm, 06 May 2024, PREPRINT (Version 1) available at ResearchSquare [https://doi.org/10.21203/rs.3.rs-4366243/v1]
Xin, Kevin and Lizhi Xin. “QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting.” (2024).
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Submitted: 2024-12-27 03:09:07 UTC
Published: 2025-01-30 09:06:17 UTC
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Kevin Xin
Lizhi Xin
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