stock trading using linear genetic programming with multiple time frames
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.
linear genetic programming computational finance algorithmic trading