Document Type
Other
Publication Date
4-2003
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.
Suggested Citation
Becker, Lee A.
, Seshadri, Mukund
(2003). Comprehensibility & Overfitting Avoidance in Genetic Programming for Technical Trading Rules. .
Retrieved from:
https://digitalcommons.wpi.edu/computerscience-pubs/154
DOI
WPI-CS-TR-03-09
