%0 Report %1 becker:2003-09 %A Becker, Lee A. %A Seshadri, Mukund %D 2003 %K , Occam's S&P500, algorithms, analysis, complexity comprehensibility genetic market overfitting, penalising, programming, razor, technical timing %T Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules %U http://citeseer.ist.psu.edu/574013.html %X This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehensibility. Several of the learned technical trading rules outperform a buy and hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs.

@techreport{becker:2003-09, abstract = {This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehensibility. Several of the learned technical trading rules outperform a buy and hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Becker, Lee A. and Seshadri, Mukund}, biburl = {https://www.bibsonomy.org/bibtex/29af1472fc2172df23b5da6599003a1c8/brazovayeye}, email = {mukund@cs.wpi.edu}, institution = {Worcester Polytechnic Institute}, interhash = {d6ede74e36de91e1bf1b23f0b9299c93}, intrahash = {9af1472fc2172df23b5da6599003a1c8}, keywords = {, Occam's S&P500, algorithms, analysis, complexity comprehensibility genetic market overfitting, penalising, programming, razor, technical timing}, month = May, timestamp = {2008-06-19T17:36:22.000+0200}, title = {Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules}, url = {http://citeseer.ist.psu.edu/574013.html}, year = 2003 }