I'm the farthest thing from an expert but here are some factors to consider. You're going to train on the past data. Typically at least to begin with you pick s

I'm the farthest thing from an expert but here are some factors to consider. You're going to train on the past data. Typically at least to begin with you pick some time period and train on part of the data and keep back some to test on. And yes, there's definitely a risk of overfitting. So some of the real data will be in sample and some out of sample for testing. If you want to get more sophisticated there is one of a number of resampling techniques in which you systematically choose different subsets of the data to train on and then look at the variation of the predictions. That can give you some idea of the error you'll get. Not sure how that works on data with high dimensionality but if you're doing an autoregression/autocorrelation (i.e. predicting tomorrows price based on it's past behavior) you'll only have the single time series. The first thing you'll likely notice if you try and apply that sort of model to stocks is that stocks are at least as highly correlated and likely much more so to the market than they are to their own past behavior.