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Dynamic multi-objective optimization for financial markets
Dissertation (MEng)--University of Pretoria, 2019.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613504463175680 |
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| access_status_str | Open Access |
| author2 | Helbig, Marde |
| author_browse | Helbig, Marde |
| author_facet | Helbig, Marde |
| collection | Thesis |
| dc_rights_str_mv | © 2020 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 (MEng)--University of Pretoria, 2019. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/79571 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:12.164Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| 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:2263/79571 Dynamic multi-objective optimization for financial markets Helbig, Marde u16403381@tuks.co.za Bosman, Anna Sergeevna Atiah, Frederick Ditliac UCTD Dynamic multi-objective optimization nature-inspired computation technical indicators foreign exchange, Forex computational intelligence Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Dissertation (MEng)--University of Pretoria, 2019. The foreign exchange (Forex) market has over 5 trillion USD turnover per day. In addition, it is one of the most volatile and dynamic markets in the world. Market conditions continue to change every second. Algorithmic trading in Financial markets have received a lot of attention in recent years. However, only few literature have explored the applicability and performance of various dynamic multi-objective algorithms (DMOAs) in the Forex market. This dissertation proposes a dynamic multi-swarm multi-objective particle swarm optimization (DMS-MOPSO) to solve dynamic MOPs (DMOPs). In order to explore the performance and applicability of DMS-MOPSO, the algorithm is adapted for the Forex market. This dissertation also explores the performance of di erent variants of dynamic particle swarm optimization (PSO), namely the charge PSO (cPSO) and quantum PSO (qPSO), for the Forex market. However, since the Forex market is not only dynamic but have di erent con icting objectives, a single-objective optimization algorithm (SOA) might not yield pro t over time. For this reason, the Forex market was de ned as a multi-objective optimization problem (MOP). Moreover, maximizing pro t in a nancial time series, like Forex, with computational intelligence (CI) techniques is very challenging. It is even more challenging to make a decision from the solutions of a MOP, like automated Forex trading. This dissertation also explores the e ects of ve decision models (DMs) on DMS-MOPSO and other three state-of-the-art DMOAs, namely the dynamic vector-evaluated particle swarm optimization (DVEPSO) algorithm, the multi-objective particle swarm optimization algorithm with crowded distance (MOPSOCD) and dynamic non-dominated sorting genetic algorithm II (DNSGA-II). The e ects of constraints handling and the, knowledge sharing approach amongst sub-swarms were explored for DMS-MOPSO. DMS-MOPSO is compared against other state-of-the-art multi-objective algorithms (MOAs) and dynamic SOAs. A sliding window mechanism is employed over di erent types of currency pairs. The focus of this dissertation is to optimized technical indicators to maximized the pro t and minimize the transaction cost. The obtained results showed that both dynamic single-objective optimization (SOO) algorithms and dynamic multi-objective optimization (MOO) algorithms performed better than static algorithms on dynamic poroblems. Moreover, the results also showed that a multi-swarm approach for MOO can solve dynamic MOPs. bs2026 Computer Science MSc Unrestricted SDG-08: Decent work and economic growth SDG-09: Industry, innovation and infrastructure 2021-04-22T10:33:08Z 2021-04-22T10:33:08Z 2020/10/02 2019 Dissertation Atiah, FD 2019, Dynamic multi-objective optimization for financial markets, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/79571> S2020 http://hdl.handle.net/2263/79571 en © 2020 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 | UCTD Dynamic multi-objective optimization nature-inspired computation technical indicators foreign exchange, Forex computational intelligence Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Dynamic multi-objective optimization for financial markets |
| title | Dynamic multi-objective optimization for financial markets |
| title_full | Dynamic multi-objective optimization for financial markets |
| title_fullStr | Dynamic multi-objective optimization for financial markets |
| title_full_unstemmed | Dynamic multi-objective optimization for financial markets |
| title_short | Dynamic multi-objective optimization for financial markets |
| title_sort | dynamic multi objective optimization for financial markets |
| topic | UCTD Dynamic multi-objective optimization nature-inspired computation technical indicators foreign exchange, Forex computational intelligence Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 |
| url | http://hdl.handle.net/2263/79571 |