S&P Trading Model - Quite Technical - Meet the Competition

[crazy canuck]

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GA can be applied in many areas. While this particular model doesn't reflect GA, survival of the fittest algorithms were used when selecting the inputs.

To avoid overfitting is modeling 101. As to the amount of data, it is not the amount of time that is required, it is the quantity of records/facts for training/development that is relavant. If you create one record/fact per day you would indeed need many years of data. If you create 500/1000 records per trading session then you would need considerably less than 1 year.

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GA is useful, however in most cases it is far from an ideal method for input selection (as eastbay said). It is much better to do a principal component analysis to make sure the inputs are not correlated.

And it is terrible for input selection for neural networks. NNs tend to overfit even without optimizing for input. And cross-validation using "training/test/test" samples is not very effective. Might work, but again it is not ideal.

Also, I was trying to point out that I don't get the purpose of your post. It didn't give any useful info for somebody trying to implement a system.