Fintech Bitcoin Smart Investment Based on the Random Neural Network with a Genetic Algorithm

  • Will SerranoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)


This paper presents the Random Neural Network in a Deep Learning Cluster structure with a new learning algorithm based on the genome model, where information is transmitted in the combination of genes rather than the genes themselves. The proposed genetic model transmits information to future generations in the network weights rather than the neurons. The innovative genetic algorithm is implanted in a complex deep learning structure that emulates the human brain: Reinforcement Learning takes fast and local decisions, Deep Learning Clusters provide identity and memory, Deep Learning Management Clusters take final strategic decisions and finally Genetic Learning transmits the learned information to future generations. This structure has been applied and validated in Fintech, a Bitcoin Smart Investment application based in an Intelligent Banker that performs Buy and Sell decisions on several Cryptocurrencies with an associated exchange and risk. Our results are promising; we have connected the human brain and genetics with Machine Learning based on the Random Neural Network model where Artificial Intelligence, similar as biology, is learning gradually and continuously while adapting to the environment.


Genetic learning Deep Learning Clusters Reinforcement Learning Random Neural Network Smart Investment Bitcoin Fintech 


  1. 1.
    Kirschner, M., Gerhart, J.: The Plausibility of Life Resolving Darwin’s Dilemma. Yale University Press, New Haven (2005)Google Scholar
  2. 2.
    Parter, M., Kashtan, N., Alon, U.: Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments. Department of Molecular Cell Biology, Weizmann Institute of Science (2008)Google Scholar
  3. 3.
    Hinton, G., Nowlan, S.: How learning can guide evolution. Adaptive individuals in evolving populations, pp. 447–454 (1996)Google Scholar
  4. 4.
    Smith, D., Bullmore, E.: Small-world brain networks. Neuroscientist 12, 512–523 (2007)Google Scholar
  5. 5.
    Sporns, O., Chialvo, D., Kaiser, M., Hilgetag, C.: Organization, development and function of complex brain networks. Trends Cogn. Sci. 8(9), 418–425 (2004)CrossRefGoogle Scholar
  6. 6.
    Leshno, M., Spector, Y.: Neural network prediction analysis: the bankruptcy case. Neurocomputing 10(2), 125–147 (1996)CrossRefGoogle Scholar
  7. 7.
    Chen, W., Du, Y.: Using neural networks and data mining techniques for the financial distress prediction model. Expert Syst. Appl. 36, 4075–4086 (2009)CrossRefGoogle Scholar
  8. 8.
    Kara, Y., Acar, M., Kaan, Ö.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Syst. Appl. 38, 5311–5319 (2011)CrossRefGoogle Scholar
  9. 9.
    Guresen, E., Kayakutlu, G., Daim, T.U.: Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38, 10389–10397 (2011)CrossRefGoogle Scholar
  10. 10.
    Zhang, G., Hu, M., Patuwo, B., Indro, D.: Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. Eur. J. Oper. Res. 116, 16–32 (1999)CrossRefGoogle Scholar
  11. 11.
    Kohara, K., Ishikawa, T., Fukuhara, Y., Nakamura, Y.: Stock price prediction using prior knowledge and neural networks. Intell. Syst. Account. Finance Manage. 6, 11–22 (1997)CrossRefGoogle Scholar
  12. 12.
    Sheta, A., Ahmed, S., Faris, H.: A comparison between regression, artificial neural networks and support vector machines for predicting stock market index. Int. J. Adv. Res. Artif. Intell. 4(7), 55–63 (2015)Google Scholar
  13. 13.
    Khuat, T., Le, M.: An application of artificial neural networks and fuzzy logic on the stock price prediction problem. Int. J. Inform. Vis. 1(2), 40–49 (2017)Google Scholar
  14. 14.
    Naeini, M., Taremian, H., Hashemi, H.: Stock market value prediction using neural networks In: International Conference on Computer Information Systems and Industrial Management Applications, pp. 132–136 (2010)Google Scholar
  15. 15.
    Iuhasz, G., Tirea, M., Negru, V.: Neural network predictions of stock price fluctuations. In: International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pp. 505–512 (2012)Google Scholar
  16. 16.
    Nicholas, A., Zapranis, A., Francis, G.: Stock performance modeling using neural networks: a comparative study with regression models. Neural Networks 7(2), 375–388 (1994)CrossRefGoogle Scholar
  17. 17.
    Duarte, V.: Macro, Finance, and Macro Finance: Solving Nonlinear Models in Continuous Time with Machine Learning. Massachusetts Institute of Technology, Sloan School of Management, pp. 1–27 (2017)Google Scholar
  18. 18.
    Stefani, J., Caelen, O., Hattab, D., Bontempi, G.: Machine learning for multi-step ahead forecasting of volatility proxies. In: Workshop on Mining Data for Financial Applications, pp. 1–12 (2017)Google Scholar
  19. 19.
    Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. FAU Discuss. Pap. Econ. 11, 1–32 (2017)zbMATHGoogle Scholar
  20. 20.
    Hasan, A., Kalıpsız, O., Akyokuş, S.: Predicting financial market in big data: deep learning. In: International Conference on Computer Science and Engineering, pp. 510–515 (2017)Google Scholar
  21. 21.
    Arifovic, J.: Genetic algorithms and inflationary economies. J. Monetary Econ. 36, 219–243 (1995)CrossRefGoogle Scholar
  22. 22.
    Kim, K., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst. Appl. 19, 125–132 (2000)CrossRefGoogle Scholar
  23. 23.
    Ticona, W., Figueiredo, K., Vellasco, M.: Hybrid model based on genetic algorithms and neural networks to forecast tax collection: application using endogenous and exogenous variables. In: International Conference on Electronics, Electrical Engineering and Computing, pp. 1–4 (2017)Google Scholar
  24. 24.
    Hossain, D., Capi, G.: Genetic algorithm based deep learning parameters tuning for robot object recognition and grasping. Int. Sch. Sci. Res. Innov. 11(3), 629–633 (2017)Google Scholar
  25. 25.
    Tirumala, S.: Implementation of evolutionary algorithms for deep architectures. In: Artificial Intelligence and Cognition, pp. 164–171 (2014)Google Scholar
  26. 26.
    David, O., Greental, I.: Genetic algorithms for evolving deep neural networks. ACM Genetic and Evolutionary Computation Conference, pp. 1451–1452 (2014)Google Scholar
  27. 27.
    Gelenbe, E.: Random Neural Networks with negative and positive signals and product form solution. Neural Comput. 1, 502–510 (1989)CrossRefGoogle Scholar
  28. 28.
    Gelenbe, E.: Learning in the recurrent Random Neural Network. Neural Comput. 5, 154–164 (1993)CrossRefGoogle Scholar
  29. 29.
    Gelenbe, E.: A class of genetic algorithms with analytical solution. Rob. Auton. Syst. 22(1), 59–64 (1997)CrossRefGoogle Scholar
  30. 30.
    Gelenbe, E.: Steady-state solution of probabilistic gene regulatory networks. Phys. Rev. 76(1), 031903 (2007). 1–8Google Scholar
  31. 31.
    Gelenbe, E., Yin, Y.: Deep learning with Random Neural Networks. In: International Joint Conference on Neural Networks, pp. 1633–1638 (2016)Google Scholar
  32. 32.
    Serrano, W., Gelenbe, E.: The deep learning Random Neural Network with a management cluster. In: International Conference on Intelligent Decision Technologies, pp. 185–195 (2017)Google Scholar
  33. 33.
    Kasun, L., Zhou, H., Huang, G.: Representational learning with extreme learning machine for big data. IEEE Intell. Syst. 28(6), 31–34 (2013)Google Scholar
  34. 34.
    Gelenbe, E.: Cognitive Packet Network. US Patent, Washington (2004). 6804201 B1Google Scholar
  35. 35.
    Gelenbe, E., Xu, Z., Seref, E.: Cognitive packet networks. In: International Conference on Tools with Artificial Intelligence, pp. 47–54 (1999)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Intelligent Systems and Networks GroupElectrical and Electronic Engineering Imperial College LondonLondonUK

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