Scalability of a Methodology for Generating Technical Trading Rules with GAPs Based on Risk-Return Adjustment and Incremental Training View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

E. A. de la Cal , E. M. Fernández , R. Quiroga , J. R. Villar , J. Sedano

ABSTRACT

In previous works a methodology was defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. The GAP algorithm implements the automatic search for crisp trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. This work probes that the proposed methodology is valid for different assets in a different market than previous work. More... »

PAGES

143-150

Book

TITLE

Hybrid Artificial Intelligence Systems

ISBN

978-3-642-13802-7
978-3-642-13803-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-13803-4_18

DOI

http://dx.doi.org/10.1007/978-3-642-13803-4_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029441935


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