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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

Central European Economic Journal
Volume 5 (2018): Issue 52 (January 2018)
By: Przemysław Ryś and  Robert Ślepaczuk  
Open Access
|Aug 2019

Authors

Przemysław Ryś

przrys@gmail.com

Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw and Labyrinth HF project, Warsaw, Poland

Robert Ślepaczuk

Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw and Labyrinth HF project, Warsaw, Poland
The views presented in this text are those of the authors and do not necessarily represent those of Circus Consulting Group nor LHF project, Warsaw, Poland
DOI: https://doi.org/10.1515/ceej-2018-0021 | Journal eISSN: 2543-6821 | Journal ISSN: 2544-9001
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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
Keywords:
Algorithmic trading,
investment strategy,
machine learning,
optimization,
investment strategy,
differential evolutionary method,
cross-validation,
overfitting
Related subjects:
Business and economics,
Political economics,
Economic theory, systems and structures,
Microeconomics,
Macroeconomics,
Economic policy

© 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.

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