Have a personal or library account? Click to login
Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency Cover

Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency

Open Access
|Aug 2019

References

  1. Ardia D., Boudt K., Carl P., Mullen K. M., Peterson B.G. Differential Evolution with DEoptim: An Application to Non-Convex Portfolio Optimization. The R Journal, 2010.
  2. Ardia D., Boudt K., Carl P., Mullem K. M., Peterson B.G. Large-scale portfolio optimization with DEoptim CRAN R, 2011a.
  3. Breiman L. Statistical Modeling: The Two Cultures Statistical Science 2001, Vol. 16, No. 3, Pages 199–231, 2001.
  4. Brest J. et alSelf-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems IEEE Transactions on Evolutionary Computation, Volume 10, Issue 6, 2006.
  5. Choundhry R., Kumkum G. A Hybrid Machine Learning System for Stock Market Forecasting International Journal of Computer and Information Engineering Vol:2, No:3, 2008.
  6. Conceicao E. Differential Evolution Optimization in Pure R CRAN R Project, 2016.
  7. Dunis C.L, Nathani A. Quantitative trading of gold and silver using nonlinear models Neural Network World: International Journal on Neural and Mass – Parallel Computing and Information Systems, 2007.
  8. Gunasekarage A., Power D.M. The profitability of moving average trading rules in South Asian stock markets Emerging Markets Review, Volume 2, Issue 1, Pages 17–33, 2001.
  9. Hastie T., Tibshirani R., Friedman J. H. The Elements of Statistical Learning Springer, 2001.
  10. Hastie T., Tibshirani R., James G., Witten D. An Introduction to Statistical Learning: With Applications in R Springer, 2013.
  11. Shen S., Jiang H., Zhang T. Stock Market Forecasting Using Machine Learning Algorithms Department of Electrical Engineering, Stanford University, Stanford, CA, 1–5, 2012.
  12. Juels A., Wattenbergy M., Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms Advances in Neural Information Processing Systems 8, 1995.
  13. Dahlquist J.R., Kirkpatrick C.D. Technical Analysis: The Complete Resource for Financial Market Technicians FT Press, 2011.
  14. Patel J., Shah S., Thakkar P., Kotecha K. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques Expert Systems with Applications Volume 42, Issue 1, Pages 259–268, 2015.
  15. Mullen K. et alPackage ‘DEoptim’ – Global Optimization by Differential Evolution CRAN R Project, 2016.
  16. Pardo R. The Evaluation and Optimization of Trading Strategies Wiley Trading, 2011.
  17. Radford M.N. Probabilistic Inference Using Markov Chain Monte Carlo Methods Technical Report CRG-TR-93-1, Department of Computer Science University of Toronto, 1993. s Ritter G. Machine Learning for trading New York, 2017.
  18. Russell S.J., Nowig P. Artificial Intelligence – A Modern Approach, Second Edition. Pearson Education, Inc. 2003.
  19. Samuel A. *Some Studies in Machine Learning Using the Game of Checkers“*. IBM Journal of Research and Development 3(3): pages 210–229, 1959.
  20. Skiena S. S. The Algorithm Design Manual, Second Edition Springer-Verlag London Limited, 2008.
  21. Smola A., Vishwanathan S.V.N. Introduction to Machine Learning Cambridge University Press, 2008.
  22. Stanković J., Marković I., Stojanović M. Investment Strategy Optimization Using Technical Analysis and Predictive Modeling in Emerging Markets Procedia Economics and Finance Volume 19, 2015. Pages 51–62.
  23. Storn, R.M, Price, K.V Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces Journal of Global Optimization, 1997. Pages 341–359.
  24. Storn, R.M., Price, K.V. Lampinen J.A. Differential Evolution – A Practical Approach to Global Optimization. Berlin Heidelberg: Springer-Verlag, 2006.
  25. Valiant L. A theory of the learnable CACM, 1984.
DOI: https://doi.org/10.1515/ceej-2018-0021 | Journal eISSN: 2543-6821 | Journal ISSN: 2544-9001
Language: English
Page range: 206 - 229
Published on: Aug 9, 2019
Published by: Faculty of Economic Sciences, University of Warsaw
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Przemysław Ryś, Robert Ślepaczuk, published by Faculty of Economic Sciences, University of Warsaw
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.