An automatic stock trading system using Particle Swarm Optimization

Conference proceedings article

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Publication Details

Author list: Worasucheep C., Nuannimnoi S., Khamvichit R., Attagonwantana P.

Publisher: Hindawi

Publication year: 2017

Start page: 497

End page: 500

Number of pages: 4

ISBN: 9781538604496

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039916410&doi=10.1109%2fECTICon.2017.8096283&partnerID=40&md5=521fba3d91288ac42af7339164ae24cc

Languages: English-Great Britain (EN-GB)

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Abstract

This paper proposes a trading strategy based on a learning method to combine a set of technical trading signals. The learning employs a modified Particle Swarm Optimization to optimize the weights of signals. The set of weighted signals is then used to determine trading decisions, i.e. To buy, to sell or to hold. A trading simulation is conducted using historical daily stock prices of twenty stocks from NYSE and SET markets. The performance is evaluated using the return on investment with the testing subset of such data. The results are compared with buy-and-hold strategy and the signal follow strategy of each individual signal. ฉ 2017 IEEE.

Keywords

Technical Indicators, Trading strategy