Computational Intelligence has found a challenging testbed for various paradigms inthe financial sector. Extensive research has resulted in numerous financial applicationsusing neural networks and evolutionary computation, mainly genetic algorithms andgenetic programming. More recent advances in the field of computational intelligencehave not yet been applied as extensively or have not become available in the publicdomain, 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 competitivesecurity trading agents that could enable the timing of buying and selling securitiesto maximise net profit and minimise risk over time. The investigated model attemptsto identify security trend reversals with the help of technical analysis methodologies.
Technical market indicators provide the necessary market data to the agents andreflect information such as supply, demand, momentum, volatility, trend, sentimentand retracement. All this is derived from the security price alone, which is one of thestrengths 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 amarket trend reversal confidence. Ultimately, the neural network becomes an amal-gamation of the technical market indicators used as inputs, and hence is capable ofdetecting trend reversals. Timely trading actions are derived from the confidenceoutput, by buying and short selling securities when the price is expected to rise orfall respectively.
No expert trading knowledge is presented to the model, only the technical marketindicator data. The co-evolutionary particle swarm optimisation model facilitates thediscovery of favourable technical market indicator interpretations, starting with zeroknowledge. A competitive fitness function is defined that allows the evaluation ofeach solution relative to other solutions, based on predefined performance metricobjectives. The relative fitness function in this study considers net profit and theSharpe ratio as a risk measure.
For the purposes of this study, the stock prices of eight large market capitalisationcompanies 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 benchmarksby 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 © 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.
Please cite as follows:
Papaconstantis, E 2009, Competitive co-evolution of trend reversal indicators usingparticle swarm optimisation, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-01182010-210715/ >
C10/60/gm