Curtis, I encourage us to understand a risk model before we try to build a new version.
As a thought experiment to address to your idea, if you are 100% long and 100% short but the beta of your longs are 2 and the beta of your shorts are 1, what is your exposure to the market? What if, in a separate thought experiment, the universe of securities are equally distributed to have a beta of between 1 and 2 but your 100% long portfolio filters stocks to select ranks of beta = 50. How much do you expect your portfolio to rise or fall when the overall market rises 10% or falls 10%?
Now, consider something other overall market exposure and see what you come up with.
Consider what a P123 rank is. By itself it tells you nothing of your returns. P123 portfolios don’t rise and fall because of ranks - it does so because those ranks equate to price action. This platform excels on easy and fast quantitative fundamental research but lacks the tools to translate that into future returns and risk. We are left with simulation summaries, only.
Regardless, we are able to do what you want with the current tools, I think. Filter your portfolio holdings using Rating(“Your ranking model”) and filter the ranks you want to avoid. Could be a decent solution though I think Yuval has been saying for some time, your value formula, as one factor example, determines your excess return - and I’m suggesting the sensitively of those securities to market wide value factor is your value risk (i.e. factor loading).
I believe letting us determine that risk factor, for many reasons, is better at this stage than leasing one or having the same for all P123 customers.
@marco, it sounds like at least (edit) 2 of us would be willing to commit time to create an risk model MVP. Will P123 commit to a release?