LEADER 03596nam  22007215  450 
001 9910438058903321
005 20200702193415.0
010   $a1-283-90903-0
010   $a3-642-33110-6
024 7 $a10.1007/978-3-642-33110-7
035   $a(CKB)3400000000086050
035   $a(EBL)1082649
035   $a(OCoLC)812289658
035   $a(SSID)ssj0000767069
035   $a(PQKBManifestationID)11421292
035   $a(PQKBTitleCode)TC0000767069
035   $a(PQKBWorkID)10732972
035   $a(PQKB)10844750
035   $a(DE-He213)978-3-642-33110-7
035   $a(MiAaPQ)EBC1082649
035   $a(PPN)168323559
035   $a(EXLCZ)993400000000086050
100   $a20120928d2013      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aInvestment Strategies Optimization based on a SAX-GA Methodology /$fby António M.L. Canelas, Rui F.M.F. Neves, Nuno C.G. Horta
205   $a1st ed. 2013.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013.
215   $a1 online resource (89 p.)
225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3704
300   $aDescription based upon print version of record.
311   $a3-642-33109-2 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Market Analysis Background and Related Work -- SAX-GA Approach -- Results -- Conclusions and Future Work.
330   $aThis book presents a new computational finance approach combining a Symbolic Aggregate approXimation (SAX) technique with an optimization kernel based on genetic algorithms (GA). While the SAX representation is used to describe the financial time series, the evolutionary optimization kernel is used in order to identify the most relevant patterns and generate investment rules. The proposed approach considers several different chromosomes structures in order to achieve better results on the trading platform The methodology presented in this book has great potential on investment markets.
410  0$aSpringerBriefs in Computational Intelligence,$x2625-3704
606   $aComputational intelligence
606   $aArtificial intelligence
606   $aMacroeconomics
606   $aEconomics, Mathematical 
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aMacroeconomics/Monetary Economics//Financial Economics$3https://scigraph.springernature.com/ontologies/product-market-codes/W32000
606   $aQuantitative Finance$3https://scigraph.springernature.com/ontologies/product-market-codes/M13062
615  0$aComputational intelligence.
615  0$aArtificial intelligence.
615  0$aMacroeconomics.
615  0$aEconomics, Mathematical .
615 14$aComputational Intelligence.
615 24$aArtificial Intelligence.
615 24$aMacroeconomics/Monetary Economics//Financial Economics.
615 24$aQuantitative Finance.
676   $a332.60285
700   $aCanelas$b António M.L$4aut$4http://id.loc.gov/vocabulary/relators/aut$01060091
702   $aNeves$b Rui F.M.F$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aHorta$b Nuno C.G$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910438058903321
996   $aInvestment Strategies Optimization based on a SAX-GA Methodology$92510986
997   $aUNINA