I am not a neural network expert but have thought about using neural network in developing trading strategies for quite some time. in the end, I found it is almost an impossible task in terms of using times series data as input. the reasoning is quite simple. for example, suppose there are 100 ways to make a profit in the market, each of the 100 strategies corresponds to a specific context of the data points, e.g., double bottom, shoulder and header, breakout, pin bar, etc. if you input all the data points to the neural network, the NN system can not pick out any of the 100 strategies, since every strategy corresponds to one pattern, i.e., one specific set of parameters in NN, thus there are no parameter sets in NN which can distinguish those 100 patterns. if NN can not produce a set of parameters to categorize specifically each of those 100 patterns, they can not pick out any of the 100 strategies either. for the above problem, genetic algorithm would be much easier to apply. NN is great if you have specified the search domain. any opinions?