Fitness Function Evaluation for MA Trading Strategies based on Genetic Algorithms
Pinto, J.
;
Neves, R.
;
Horta, N.
Fitness Function Evaluation for MA Trading Strategies based on Genetic Algorithms , Proc Genetic and Evolutionary Computation Conf. - GECCO, Dublin, Ireland, Vol. NA, pp. 819 - 820, July, 2011.
Digital Object Identifier:
Download Full text PDF ( 419 KBs)
Abstract
This paper presents a new approach to optimize an investment strategy based on moving averages (MA). The proposed approach optimizes the entry and exit points, for both long and short positions, using a genetic algorithm (GA) kernel. This approach outperforms B&H strategy and explores alternative functions to the classical absolute return fitness function. The approach is demonstrated for major market indexes, such as, S&P 500, FTSE100, DAX30, NIKKEI225.
