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. / Dissertation (MEng)--University of Pretoria, 2019. / Computer Science / MSc / Unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/79571 |
| Date | January 2019 |
| Creators | Atiah, Frederick Ditliac |
| Contributors | Helbig, Marde, u16403381@tuks.co.za, Bosman, Anna Sergeevna |
| Publisher | University of Pretoria |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Rights | © 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. |
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