國立虎尾科技大學 |

Stock Exchange Trading Using Grid Pattern Optimized by A Genetic Algorithm with Speciation = The Case of S&P 500 /

Record Type:	Language materials, printed : 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.
Description:	XV, 68 p. 42 illus., 41 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
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. - 1st ed. 2021. - XV, 68 p. 42 illus., 41 illus. in color.online resource. - SpringerBriefs in Computational Intelligence,2625-3704. - SpringerBriefs in Computational Intelligence,.

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--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Stock Exchange Trading Using Grid Pattern Optimized by A Genetic Algorithm with Speciation = The Case of S&P 500 /
LDR:02874nam a22003975i 4500 001 1046244
003 DE-He213
005 20210708144107.0
007 cr nn 008mamaa
008 220103s2021 gw | s |||| 0|eng d
020 $a 9783030766801 $9 978-3-030-76680-1
024 7 $a 10.1007/978-3-030-76680-1 $2 doi
035 $a 978-3-030-76680-1
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Martins, Tiago. $e editor. $1 https://orcid.org/0000-0003-2638-237X $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1323077
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.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XV, 68 p. 42 illus., 41 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Computational Intelligence, $x 2625-3704
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.
650 0 $a Computational intelligence. $3 568984
650 0 $a Financial engineering. $3 591542
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Financial Engineering. $3 1107684
700 1 $a Neves, Rui. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1288909
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030766795
776 0 8 $i Printed edition: $z 9783030766818
830 0 $a SpringerBriefs in Computational Intelligence, $x 2625-3704 $3 1254760
856 4 0 $u https://doi.org/10.1007/978-3-030-76680-1
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)