We investigate the generalization properties of a data-mining approach to single-position day trading which uses an evolutionary
algorithm to construct fuzzy predictive models of financial instruments. The models, expressed as fuzzy rule bases, take a
number of popular technical indicators on day
t as inputs and produce a trading signal for day
t + 1 based on a dataset of past observations of which actions would have been most profitable.
The approach has been applied to trading several financial instruments (large-cap stocks and indices), in order to study the
horizontal, i.e., cross-market, generalization capabilities of the models.
Keywords Data Mining - Modeling - Trading - Evolutionary Algorithms