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.
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