The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa

Recently, the use of genetic algorithm for the optimization of technical trading strategies has been receiving a great deal of attention. A technical trading strategy involves the study of past behavior in order to draw conclusions concerning the direction and magnitude of future price movement....

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Main Author: Musa, Khairunnisa
Format: Thesis
Language:English
Published: 2006
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/1520/1/TD_KHAIRUNNISA%20MUSA%20CS%2006_5%20P01.pdf
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spelling my-uitm-ir.15202019-11-13T08:14:11Z The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa 2006 Musa, Khairunnisa Electronic Computers. Computer Science T Technology (General) Recently, the use of genetic algorithm for the optimization of technical trading strategies has been receiving a great deal of attention. A technical trading strategy involves the study of past behavior in order to draw conclusions concerning the direction and magnitude of future price movement. Technical models are designed to keep investor trading with the trend. Understanding the best trend could produce a promising lucrative investment. This research is about an application of technical trading strategy to foreign exchange market by using Standard Genetic Algorithm (STDGA). Genetic algorithm is used as a tool to efficiently search for the most attractive solution as a suggestion for the trader to trade in foreign currencies. Results from the function optimization shows that STDGA is effective and efficient in locating the optimal solution (the maximum value of the Sharpe Ratio). COPYRIGHT © 2006 Thesis https://ir.uitm.edu.my/id/eprint/1520/ https://ir.uitm.edu.my/id/eprint/1520/1/TD_KHAIRUNNISA%20MUSA%20CS%2006_5%20P01.pdf text en public degree Universiti Teknologi MARA Faculty of Computer and Mathematical Sciences
institution Universiti Teknologi MARA
collection UiTM Institutional Repository
language English
topic Electronic Computers
Computer Science
T Technology (General)
spellingShingle Electronic Computers
Computer Science
T Technology (General)
Musa, Khairunnisa
The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa
description Recently, the use of genetic algorithm for the optimization of technical trading strategies has been receiving a great deal of attention. A technical trading strategy involves the study of past behavior in order to draw conclusions concerning the direction and magnitude of future price movement. Technical models are designed to keep investor trading with the trend. Understanding the best trend could produce a promising lucrative investment. This research is about an application of technical trading strategy to foreign exchange market by using Standard Genetic Algorithm (STDGA). Genetic algorithm is used as a tool to efficiently search for the most attractive solution as a suggestion for the trader to trade in foreign currencies. Results from the function optimization shows that STDGA is effective and efficient in locating the optimal solution (the maximum value of the Sharpe Ratio). COPYRIGHT ©
format Thesis
qualification_level Bachelor degree
author Musa, Khairunnisa
author_facet Musa, Khairunnisa
author_sort Musa, Khairunnisa
title The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa
title_short The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa
title_full The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa
title_fullStr The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa
title_full_unstemmed The optimization of technical trading strategy using genetic algorithm approach / Khairunnisa Musa
title_sort optimization of technical trading strategy using genetic algorithm approach / khairunnisa musa
granting_institution Universiti Teknologi MARA
granting_department Faculty of Computer and Mathematical Sciences
publishDate 2006
url https://ir.uitm.edu.my/id/eprint/1520/1/TD_KHAIRUNNISA%20MUSA%20CS%2006_5%20P01.pdf
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