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Stock exchange trading using grid pa...

Martins, Tiago.

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Stock exchange trading using grid pattern optimized by a genetic algorithm with speciation = the case of S&P 500 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stock exchange trading using grid pattern optimized by a genetic algorithm with speciation/ by Tiago Martins, Rui Neves.
Reminder of title:	the case of S&P 500 /
Author:	Martins, Tiago.
other author:	Neves, Rui.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xv, 68 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Related Work -- 3. Architecture -- 4. Test Scenarios and Evaluation -- 5. Conclusions and Future Work.
Contained By:	Springer Nature eBook
Subject:	Genetic algorithms. -
Online resource:	https://doi.org/10.1007/978-3-030-76680-1
ISBN:	9783030766801

Stock exchange trading using grid pattern optimized by a genetic algorithm with speciation = the case of S&P 500 /
Martins, Tiago.

Stock exchange trading using grid pattern optimized by a genetic algorithm with speciationthe case of S&P 500 /[electronic resource] :by Tiago Martins, Rui Neves. - Cham :Springer International Publishing :2021. - xv, 68 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology. - SpringerBriefs in applied sciences and technology..

1. Introduction -- 2. Related Work -- 3. Architecture -- 4. Test Scenarios and Evaluation -- 5. Conclusions and Future Work.

This book presents a genetic algorithm that optimizes a grid template pattern detector to find the best point to trade in the SP 500. The pattern detector is based on a template using a grid of weights with a fixed size. The template takes in consideration not only the closing price but also the open, high, and low values of the price during the period under testing in contrast to the traditional methods of analysing only the closing price. Each cell of the grid encompasses a score, and these are optimized by an evolutionary genetic algorithm that takes genetic diversity into consideration through a speciation routine, giving time for each individual of the population to be optimized within its own niche. With this method, the system is able to present better results and improves the results compared with other template approaches. The tests considered real data from the stock market and against state-of-the-art solutions, namely the ones using a grid of weights which does not have a fixed size and non-speciated approaches. During the testing period, the presented solution had a return of 21.3% compared to 10.9% of the existing approaches. The use of speciation was able to increase the returns of some results as genetic diversity was taken into consideration.

ISBN: 9783030766801

Standard No.: 10.1007/978-3-030-76680-1doiSubjects--Corporate Names:

3505972
S&P Global (Firm)
Subjects--Topical Terms:

533907
Genetic algorithms.

LC Class. No.: QA402.5

Dewey Class. No.: 519.625

Stock exchange trading using grid pattern optimized by a genetic algorithm with speciation = the case of S&P 500 /
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505 0 $a 1. Introduction -- 2. Related Work -- 3. Architecture -- 4. Test Scenarios and Evaluation -- 5. Conclusions and Future Work.
520 $a This book presents a genetic algorithm that optimizes a grid template pattern detector to find the best point to trade in the SP 500. The pattern detector is based on a template using a grid of weights with a fixed size. The template takes in consideration not only the closing price but also the open, high, and low values of the price during the period under testing in contrast to the traditional methods of analysing only the closing price. Each cell of the grid encompasses a score, and these are optimized by an evolutionary genetic algorithm that takes genetic diversity into consideration through a speciation routine, giving time for each individual of the population to be optimized within its own niche. With this method, the system is able to present better results and improves the results compared with other template approaches. The tests considered real data from the stock market and against state-of-the-art solutions, namely the ones using a grid of weights which does not have a fixed size and non-speciated approaches. During the testing period, the presented solution had a return of 21.3% compared to 10.9% of the existing approaches. The use of speciation was able to increase the returns of some results as genetic diversity was taken into consideration.
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