Automated Trading is the activity of buying and selling financial instruments for the purpose of gaining a profit, through
the use of automated trading rules. This work presents an evolutionary approach for the design and optimization of artificial
neural networks to the discovery of profitable automated trading rules. Experimental results indicate that, despite its simplicity,
both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant
returns.