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Co-evolved genetic program for stock market trading
Dissertation (MSc)--University of Pretoria, 2018.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2019
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| _version_ | 1867613559112859648 |
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| access_status_str | Open Access |
| author2 | Engelbrecht, Andries P. |
| author_browse | Engelbrecht, Andries P. |
| author_facet | Engelbrecht, Andries P. |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2018. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/68477 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:03.802Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/68477 Co-evolved genetic program for stock market trading Engelbrecht, Andries P. u98028571@tuks.co.za Nicholls, Jason Frederick Evolutionary Programming Stock market Genetic Algorithms Co-evolution Stock market trading rule optimisation Machine learning UCTD Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Dissertation (MSc)--University of Pretoria, 2018. This thesis compares the profitability of trading rules evolved by a single population genetic program (GP), a co-operative co-evolved GP, and a competitive co-evolved GP. Profitability was determined by trading thirteen listed shares on the Johannesburg Stock Exchange (JSE) over a period of April 2003 to June 2008. The GP parameters were optimised using a response surface methodology known as factorial design. A compound excess return over the buy-and-hold strategy was determined as the preferred fitness function via an empirical process. Various selection strategies to select individuals for the crossover and mutation operators were compared. It was found rank selection was the preferred strategy. The optimised GPs were tested on market data using a real world fee structure. The results were compared to a buy-and-hold strategy and a random-walk. The results of this thesis show that the co-operative co-evolved GP generates trading rules that perform significantly worse than a single population GP and a competitively co-evolved GP. The results also show that a competitive co-evolved GP and the single population GP produce similar trading rules. The evolved trading rules significantly outperform the buy-and-hold strategy when the market, including fees, was trending downwards. No significant difference was found between the buy-and-hold strategy, the competitive co-evolved GP, and single population GP when the market (including fees) was trending upwards. bs2026 Computer Science MSc Unrestricted SDG-08: Decent work and economic growth SDG-09: Industry, innovation and infrastructure 2019-02-20T07:02:51Z 2019-02-20T07:02:51Z 2019-04-09 2018 Dissertation Nicholls, JF 2018, Co-evolved genetic program for stock market trading, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/68477> http://hdl.handle.net/2263/68477 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Evolutionary Programming Stock market Genetic Algorithms Co-evolution Stock market trading rule optimisation Machine learning UCTD Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Co-evolved genetic program for stock market trading |
| title | Co-evolved genetic program for stock market trading |
| title_full | Co-evolved genetic program for stock market trading |
| title_fullStr | Co-evolved genetic program for stock market trading |
| title_full_unstemmed | Co-evolved genetic program for stock market trading |
| title_short | Co-evolved genetic program for stock market trading |
| title_sort | co evolved genetic program for stock market trading |
| topic | Evolutionary Programming Stock market Genetic Algorithms Co-evolution Stock market trading rule optimisation Machine learning UCTD Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 |
| url | http://hdl.handle.net/2263/68477 |