I'd start off with a simpler experiment; just some constant value plus fluctuations. You can generate random numbers using the built-in function rand() in C or C++; something simliar must exist in Java. The random numbers range from 0 to 1, and then you map them onto a normal (gaussian) distribution. The larger you make the std deviation, the more the numbers will fluctuate around the mean (here the offset). I think there's an example in "Numerical Recipes" or I can post a scrap of code if you are interested.
I have been thinking about this, but why not just pick real stock prices instead? If the algorithm closely mimics real stocks, then we can just as well use real stocks. The reason for the artificial stocks is to be able to quantify just how well an algorithm performs on a stock with certain characteristics. This should give you a better "feeling" for the algorithm.
The other approach would be to take the whole universe of stocks and let the algorithm loose upon it. That would require quite some computing power, and you would still face the selection problem.
Anyway I will use CSV files as the stock-database so, anyone could create any stock data he likes and play with that. I suppose that most spreadsheets are able to generate CSV files.
Best,
Rien.
AI
