Abstract
A genetic programming system for data mining trading
rules out of past foreign exchange data is described.
The system is tested on real data from the dollar/yen
and dollar/DM markets, and shown to produce
considerable excess returns in the dollar/yen market.
Design issues relating to potential rule complexity and
validation regimes are explored empirically. Keeping
potential rules as simple as possible is shown to be
the most important component of success. Validation
issues are more complicated. Inspection of fitness on a
validation set is used to cut-off search in hopes of
avoiding overfitting. Additional attempts to use the
validation set to improve performance are shown to be
ineffective in the standard framework. An examination
of correlations between performance on the validation
set and on the test set leads to an understanding of
how such measures can be marginally benificial;
unfortunately, this suggests that further attemps to
improve performance through validation will prove
difficult
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