The first experiment I'm doing is more of a simple test and proof of concept and will serve as a reference point for future tests. I'm going to start with a small network that is quick to train. I will only read in the last 2 days of Hi/Lo data plus the current market close and make a prediction of next days direction.

Attached shows a picture of the net - just 5 input neurons. I've added 3 hidden layers for no other reason than to be sure there are enough connections to ensure the net is capable of sufficient complexity. There is no real science behind the design.

In the uploaded spreadsheet I have uploaded test runs from 3 nets - the first is the net without any training, the 2nd is after 2 training runs and the third is the fully trained net.

The column accum Percent is the sum of all of the percent gains/losses from each daily trade. It serves as a better gauge than using pips/points only because percentages are scaled to the level the market is at. It is this result that the nets are trained against. If the net was trained against pips only it would have a bias towards recent years when the index and pip gains were larger.

Initially the net seems to sell everything - but after 2 generations it has already decided that buy and hold (with a few exceptions) is the best policy. After 7 generations the network cannot be improved upon which you can see produces only marginally better results than from generation 2. Bear in mind the net can only see the last 2 days of market data.

This net will be retained for forward and cross index testing later.

Coming next is training a net with last five days of data to see if using this extra data it can improve the results.