with Multiple Time Frames in Genetic and Evolutionary Computation Conference 2011.”A number of researchers have attempted to take successful GP trading systems and make them even better through the use of ﬁlters. We investigate the use of a linear genetic programming (LGP) system that combines GP signals provided over multiple intraday time frames to produce one trading action. Four combinations of time frames stretching further into the past are examined. Two diﬀerent decision mechanisms for evaluating the overall signal given the GP signals over all time frames are also examined, one based on majority vote and another based on temporal proximity to the buying decision. Results indicated that majority vote outperformed emphasis on proximity of time frames to the current trading decision. Analyses also indicated that longer time frame combinations were more conservative and outperformed shorter combinations for both overall upward and downward price trends.”
Garnett Wilson is currently CEO of Afinin Labs Inc., St. John’s, Canada, where he develops evolutionary computation systems for equities trading. Garnett received his Ph.D. in Computer Science from Dalhousie University in 2007, and he is currently an Adjunct Assistant Professor in the Faculty of Computer Science at Dalhousie University. During his postdoctoral fellowship studies at Memorial University of Newfoundland and Verafin, Inc. he developed solutions for the detection of debit fraud and money laundering activity in consumer bank account data. Garnett is a member of the IEEE Computational Intelligence Society’s Computational Finance and Economics Technical Committee.