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Trend Following With Float-encoding Genetic Algorithm
Jiahua Luo; Yain-Whar Si; Simon Fong
2012-12-31
Conference NameSeventh International Conference on Digital Information Management (ICDIM 2012)
Source PublicationSeventh International Conference on Digital Information Management (ICDIM 2012)
Pages173-176
Conference Date22-24 Aug. 2012
Conference PlaceMacau, China
PublisherIEEE
Abstract

Trend following plays an important role in technical analysis for trading financial instruments. In this paper, we propose a model based on Float-encoding Genetic Algorithm (FGA) to determine the best thresholds for trend following in financial time series. Trend following is based on the thresholds called P&Q which is calculated from the amount of an uptrend and downtrend to determine when to buy and sell at a particular time point. In our model, we first smooth the closing price by Exponential Moving Average (EMA) and partition the sample data into two parts respectively for training and testing. During the training session, FGA is used to find the best P&Q values which optimizes the average return based on a chosen EMA. The resulted P&Q is then evaluated against the testing data. Experiments conducted on Hang Sang Index (HSI) from Hong Kong shows promising results. 

KeywordExponential Moving Average Float-encoding Genetic Algorithm p&q Stock Trend Following
DOIhttps://doi.org/10.1109/ICDIM.2012.6360100
URLView the original
Language英语
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science, University of Macau Av. Padre Tomas Pereira, Taipa, Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jiahua Luo,Yain-Whar Si,Simon Fong. Trend Following With Float-encoding Genetic Algorithm[C]:IEEE,2012:173-176.
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