I am back testing a strategy in R and I have some questions about testing design. I have a universe of around 500 stocks to test filtered based on liquidity. To test the trading strategy I have implemented a simple in-sample/out-of-sample testing scheme. Before running a full test on the whole universe of stocks I have been running the tests over a random sample of the stocks while designing a generic testing framework.
I have been dividing the universe into two parts, I have been optimizing over a random sample of different stocks then using the optimized parameters from the training set to test over a random selection of different stocks from the test set. Are there any problems with doing this rather than testing over the total universe?
My other question is to do with using prices vs returns (vs log returns). I am a little confused as to the reasoning to use returns rather than price to find signals. Would you use returns series because you are more able to compare returns series more easily then price series?