For the first experiments, networks will be kept/disgarded purely on the basis of the total number of points they achieve over the historical data. However I will weigh the points according to the index level. Gaining 1 point when the S&P was at 100 is a lot more significant than gaining one point when it was at 1200.

I'm going to train the network on daily data from the S&P from 3-Jan-1962 to 31-Dec-2006. The S&P is relatively friendly to indicators given it's large number of constituents. I am keeping data from 1-Jan-2007 aside for forward testing, I think it will be interesting to see how any evolved network will cope with the credit crunch. I'm hoping by training on such a large range of data from '62 - '07 that the net will not become too optimised, however I can't guarantee this and I'm deliberately leaving out a pretty volatile period in history to see if the network can cope with this unseen data.

Afterwards I will increase the complexity of the outputs - stating trade size, stop size and limits. I will also see if the evolved network is capable of trading other more tricky indices like the Dow Jones Industral and the Nikkei without ever having seen the data before.

If any of this is successful I will move onto shorter timeframes in forex to see if a similar network can be built.