Improving the profitability of Technical Analysis through intelligent algorithms

The profitability of Dual Moving Average Crossover ( DMAC) rule can be improved through suitable trading systems. However, Artificial Intelligence techniques can increase the profit performance of technical systems. In this paper, two intelligent trading systems are proposed. The first one makes use of fuzzy logic techniques to enhance the power of genetic procedures. The second system attempts to improve the performances of fuzzy system through Neural Networks. The target is to obtain good profits, avoiding drawdown situations, in applications to the DMAC rule for trading the euro-dollar in the foreign exchange market. The results show that the fuzzy system gives good profits over trading periods close to training period length. Viceversa, the neuro-fuzzy system achieves the best profits over trading period less or much greater than training period length. Both systems show an optimal robustness to draw-down.