Let's say I'm trying to build a strategy for multiple symbols/assets based on momentum. I'm sure some of you have done this. My question is: Do you guys have any clever ways of normalizing the inputs for a momentum strategy? Let me give you an example. Take a look at BAC versus JPM. Say you build a simple model for BAC, and you decide that "if x number of shares trades at the offer over y seconds" then you want to buy BAC. Obviously, x and y will vary for BAC (very thick stock), versus JPM. Or do I just have to fit all the parameters individually, keep a record of all the inputs for each symbol? I guess my question is whether there's any short-hand or elegant techniques I can use to transform the inputs into one convenient form. Here are some real world analogous examples: - In math, where you go from cartesian coordinates to polar coordinates because it's easier to work with polar coordinates instead - In signal processing, where people change signal descriptions from the time domain into the frequency domain, because dealing with the frequency domain is easier. I can normalize everything into descriptive statistics I guess, but the messy part is organizing all the data and calculating all the statistics for each and every symbol I want to look at. Is there no easy way around this problem?