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Competitive co-evolution of trend reversal indicators using particle swarm optimisation
Dissertation (MSc)--University of Pretoria, 2010.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613502428938240 |
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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 | © 2009, 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, 2010. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/23929 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:10.039Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| 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:2263/23929 Competitive co-evolution of trend reversal indicators using particle swarm optimisation Engelbrecht, Andries P. epapaco@googlemail.com Papacostantis, Evangelos Technical market indicators Stock exchange trading Evolutionary computation Competitive co-evolution Artificial neural networks Particle swarm optimization (PSO) Technical analysis Finance UCTD Dissertation (MSc)--University of Pretoria, 2010. Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisation and neural networks could be used to discover competitive security trading agents that could enable the timing of buying and selling securities to maximise net profit and minimise risk over time. The investigated model attempts to identify security trend reversals with the help of technical analysis methodologies. Technical market indicators provide the necessary market data to the agents and reflect information such as supply, demand, momentum, volatility, trend, sentiment and retracement. All this is derived from the security price alone, which is one of the strengths of technical analysis and the reason for its use in this study. The model proposed in this thesis evolves trading strategies within a single pop- ulation of competing agents, where each agent is represented by a neural network. The population is governed by a competitive co-evolutionary particle swarm optimi- sation algorithm, with the objective of optimising the weights of the neural networks. A standard feed forward neural network architecture is used, which functions as a market trend reversal confidence. Ultimately, the neural network becomes an amal- gamation of the technical market indicators used as inputs, and hence is capable of detecting trend reversals. Timely trading actions are derived from the confidence output, by buying and short selling securities when the price is expected to rise or fall respectively. No expert trading knowledge is presented to the model, only the technical market indicator data. The co-evolutionary particle swarm optimisation model facilitates the discovery of favourable technical market indicator interpretations, starting with zero knowledge. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed hybrid computational intelligence model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. This indicates that the introduced model is effective in finding favourable strategies, based on observed historical security price data. Transaction costs were considered in the evaluation of the computational intelligent agents, making this a feasible model for a real-world application. Copyright Computer Science unrestricted 2013-09-06T16:11:43Z 2010-05-27 2013-09-06T16:11:43Z 2010-04-12 2010-05-27 2010-01-18 Dissertation Papaconstantis, E 2009, Competitive co-evolution of trend reversal indicators using particle swarm optimisation, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/23929 > C10/60/gm http://hdl.handle.net/2263/23929 http://upetd.up.ac.za/thesis/available/etd-01182010-210715/ © 2009, 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 | Technical market indicators Stock exchange trading Evolutionary computation Competitive co-evolution Artificial neural networks Particle swarm optimization (PSO) Technical analysis Finance UCTD Competitive co-evolution of trend reversal indicators using particle swarm optimisation |
| title | Competitive co-evolution of trend reversal indicators using particle swarm optimisation |
| title_full | Competitive co-evolution of trend reversal indicators using particle swarm optimisation |
| title_fullStr | Competitive co-evolution of trend reversal indicators using particle swarm optimisation |
| title_full_unstemmed | Competitive co-evolution of trend reversal indicators using particle swarm optimisation |
| title_short | Competitive co-evolution of trend reversal indicators using particle swarm optimisation |
| title_sort | competitive co evolution of trend reversal indicators using particle swarm optimisation |
| topic | Technical market indicators Stock exchange trading Evolutionary computation Competitive co-evolution Artificial neural networks Particle swarm optimization (PSO) Technical analysis Finance UCTD |
| url | http://hdl.handle.net/2263/23929 http://upetd.up.ac.za/thesis/available/etd-01182010-210715/ |