001438206 000__ 03369cam\a2200529\a\4500
001438206 001__ 1438206
001438206 003__ OCoLC
001438206 005__ 20230309004253.0
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001438206 008__ 210717s2021\\\\sz\\\\\\ob\\\\000\0\eng\d
001438206 019__ $$a1260193855$$a1284943994
001438206 020__ $$a9783030766801$$q(electronic bk.)
001438206 020__ $$a3030766802$$q(electronic bk.)
001438206 020__ $$z9783030766795
001438206 0247_ $$a10.1007/978-3-030-76680-1$$2doi
001438206 035__ $$aSP(OCoLC)1260344723
001438206 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dOCLCF$$dQGK$$dVLB$$dOCLCQ$$dOCLCO$$dOCLCQ
001438206 049__ $$aISEA
001438206 050_4 $$aQA402.5$$b.M27 2021eb
001438206 08204 $$a519.6/25$$223
001438206 1001_ $$aMartins, Tiago.
001438206 24510 $$aStock exchange trading using grid pattern optimized by a genetic algorithm with speciation :$$bthe case of S & P 500 /$$cTiago Martins, Rui Neves.
001438206 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001438206 264_4 $$c©2021
001438206 300__ $$a1 online resource (xv, 68 pages)
001438206 336__ $$atext$$btxt$$2rdacontent
001438206 337__ $$acomputer$$bc$$2rdamedia
001438206 338__ $$aonline resource$$bcr$$2rdacarrier
001438206 4901_ $$aSpringerBriefs in Applied Sciences and Technology
001438206 504__ $$aIncludes bibliographical references.
001438206 50500 $$tIntroduction --$$tRelated Work --$$tArchitecture --$$tEvaluation --$$tConclusions and future work.
001438206 506__ $$aAccess limited to authorized users.
001438206 520__ $$aThis book presents a genetic algorithm that optimizes a grid template pattern detector to find the best point to trade in the SP 500. The pattern detector is based on a template using a grid of weights with a fixed size. The template takes in consideration not only the closing price but also the open, high, and low values of the price during the period under testing in contrast to the traditional methods of analysing only the closing price. Each cell of the grid encompasses a score, and these are optimized by an evolutionary genetic algorithm that takes genetic diversity into consideration through a speciation routine, giving time for each individual of the population to be optimized within its own niche. With this method, the system is able to present better results and improves the results compared with other template approaches. The tests considered real data from the stock market and against state-of-the-art solutions, namely the ones using a grid of weights which does not have a fixed size and non-speciated approaches. During the testing period, the presented solution had a return of 21.3% compared to 10.9% of the existing approaches. The use of speciation was able to increase the returns of some results as genetic diversity was taken into consideration.
001438206 61020 $$aS&P Global (Firm)
001438206 650_0 $$aGenetic algorithms.
001438206 650_6 $$aAlgorithmes génétiques.
001438206 655_0 $$aElectronic books.
001438206 7001_ $$aNeves, Rui.
001438206 77608 $$iPrint version:$$aMartins, Tiago.$$tStock Exchange Trading Using Grid Pattern Optimized by a Genetic Algorithm with Speciation.$$dCham : Springer International Publishing AG, ©2021$$z9783030766795
001438206 830_0 $$aSpringerBriefs in applied sciences and technology.
001438206 852__ $$bebk
001438206 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-76680-1$$zOnline Access$$91397441.1
001438206 909CO $$ooai:library.usi.edu:1438206$$pGLOBAL_SET
001438206 980__ $$aBIB
001438206 980__ $$aEBOOK
001438206 982__ $$aEbook
001438206 983__ $$aOnline
001438206 994__ $$a92$$bISE