Evolving Hierarchical Temporal Memory-Based Trading Models

  • Patrick Gabrielsson
  • Rikard König
  • Ulf Johansson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7835)


We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was partitioned into multiple folds to enable a modified cross validation scheme. Artificial Neural Networks (ANNs) were used to benchmark HTM performance. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models on the chosen data set and both technologies yielded profitable results with above average accuracy.


Genetic Algorithm Trading Strategy Technical Indicator Algorithmic Trading Training Window 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Åslin, F.: Evaluation of Hierarchical Temporal Memory in algorithmic trading, Department of Computer and Information Science, University of Linköping (2010)Google Scholar
  2. 2.
    Doremale, J.V., Boves, L.: Spoken Digit Recognition using a Hierarchical Temporal Memory, Brisbane, Australia, pp. 2566–2569 (2008)Google Scholar
  3. 3.
    Gabrielsson, P., Konig, R., Johansson, U.: Hierarchical Temporal Memory-based algorithmic trading of financial markets. In: 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), pp. 1–8 (2012)Google Scholar
  4. 4.
    George, D., Hawkins, J.: A hierarchical Bayesian model of invariant pattern recognition in the visual cortex, in. In: Proceedings of the IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 3, pp. 1812–1817 (2005)Google Scholar
  5. 5.
    George, D., Jaros, B.: The HTM Learning Algorithms. Numenta Inc. (2007),
  6. 6.
    George, D., et al.: Sequence memory for prediction, inference and behaviour. Philosophical Transactions - Royal Society. Biological Sciences 364, 1203–1209 (2009)CrossRefGoogle Scholar
  7. 7.
    George, D., Hawkins, J.: Towards a mathematical theory of cortical micro-circuits. PLoS Computational Biology 5, 1000532 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hawkins, J., Blakeslee, S.: On Intelligence. Times Books (2004)Google Scholar
  9. 9.
    Hawkins, J., George, D.: Hierarchical Temporal Memory - Concepts, Theory and Terminology. Numenta Inc. (2006),
  10. 10.
    Maxwell, J., et al.: Hierarchical Sequential Memory for Music: A Cognitive Model. International Society for Music Information Retrieval, 429–434 (2009)Google Scholar
  11. 11.
    Mountcastle, V.: An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System, pp. 7–50. MIT Press (1978)Google Scholar
  12. 12.
    Murphy, J.: Technical Analysis of the Financial Markets, NY Institute of Finance (1999)Google Scholar
  13. 13.
  14. 14.
    Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann (1988)Google Scholar
  15. 15.
    Rozado, D., Rodriguez, F.B., Varona, P.: Optimizing Hierarchical Temporal Memory for Multivariable Time Series. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part II. LNCS, vol. 6353, pp. 506–518. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Slickcharts, E-mini Futures (2012),
  17. 17.
    Tan, P.N., et al.: Introduction to Data Mining. Addison Wesley (2009)Google Scholar
  18. 18.
    Thornton, J., Gustafsson, T., Blumenstein, M., Hine, T.: Robust Character Recognition Using a Hierarchical Bayesian Network. In: Sattar, A., Kang, B.-H. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1259–1264. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Thornton, J., Faichney, J., Blumenstein, M., Hine, T.: Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 562–572. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Patrick Gabrielsson
    • 1
  • Rikard König
    • 1
  • Ulf Johansson
    • 1
  1. 1.School of Business and Information TechnologyUniversity of BoråsSweden

Personalised recommendations