國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Stock exchange trading using grid pa...

Martins, Tiago.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stock exchange trading using grid pattern optimized by a genetic algorithm with speciationby Tiago Martins, Rui Neves.
其他題名:	the case of S&P 500 /
作者:	Martins, Tiago.
其他作者:	Neves, Rui.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xv, 68 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Genetic algorithms.
電子資源:	https://doi.org/10.1007/978-3-030-76680-1
ISBN:	9783030766801$q(electronic bk.)

Stock exchange trading using grid pattern optimized by a genetic algorithm with speciationthe 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$q(electronic bk.)

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

898092
S&P Global (Firm)
Subjects--Topical Terms:

182939
Genetic algorithms.

LC Class. No.: QA402.5

Dewey Class. No.: 519.625

Stock exchange trading using grid pattern optimized by a genetic algorithm with speciationthe case of S&P 500 /
LDR:02520nmm a2200337 a 4500 001 602432
003 DE-He213
005 20210708144107.0
006 m d
007 cr nn 008maaau
008 211112s2021 sz s 0 eng d
020 $a 9783030766801$q(electronic bk.)
020 $a 9783030766795$q(paper)
024 7 $a 10.1007/978-3-030-76680-1 $2 doi
035 $a 978-3-030-76680-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 519.625 $2 23
090 $a QA402.5 $b .M386 2021
100 1 $a Martins, Tiago. $3 893462
245 1 0 $a Stock exchange trading using grid pattern optimized by a genetic algorithm with speciation $h [electronic resource] : $b the case of S&P 500 / $c by Tiago Martins, Rui Neves.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xv, 68 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology
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.
610 2 0 $a S&P Global (Firm) $3 898092
650 0 $a Genetic algorithms. $3 182939
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Financial Engineering. $3 744568
700 1 $a Neves, Rui. $3 806504
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in applied sciences and technology. $3 557662
856 4 0 $u https://doi.org/10.1007/978-3-030-76680-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)