Can you break down those four different pieces of data and, and why did you do that, that breakdown? What sorts of information are the signals?

If we dig down a level from, from what you described, so we, when we think about prices, we're, we're focused on, we're focused on, on trends over, over different types of horizons.

Those could be trends in the cross section, so comparing stocks to performance of other stocks in the universe over different horizons.

It could be trends in the short term, the medium term, and the long term at the individual stock level.

It could be some technical measures in, in how much, uh, maybe a stock's price is oscillating around a, a, a certain, uh, level.

When we move into the, uh, when we move into the, the fundamental world, we are still thinking about a lot of the same, uh, accounting metrics that you would see in a traditional approach, but rather than thinking about them what is their level at the moment, we're more focused on how they might be changing.

So what is the, what is the volatility in the, in the dividend yield over the last six months? What is the volatility in the return on assets over the last 12 months? So we're very much focused on the changes, uh, to fundamentals.

What's an example of something that you can understand by a human, people will be listening and say, "Oh, that makes sense," and you're using the quantitative technique to capture that? W- And what is an example of the 85% of this works, the data tells us this works, but we have no idea why?

In everyday language, can you tell us what the computer is trying to figure out when it studies all that data? And how do you try to differentiate your approach from other models that maybe try to do the same thing?

Let's start and think about a more traditional quant approach that is trying to exploit some style factors or risk premia, where we can group certain signals, signals that are telling us something about a company or the stock that have an economically sound reason why they should they should work.

So it might be a signal in the momentum grouping of signals, momentum being something that where you're picking up on a trend maybe in a stock's performance and a trend that is going to continue in the future.

That has a economically sound reason why it should work, generally justified by the fact that it's accepted that different parts of the market pick up on certain news driving that trend at different times.

Or maybe it's a signal from the quality grouping where there is something about the quality of a company's earnings or its management team or its business model that allows it to have a outsized result versus its peers.

Again, there's a rationale for using that.

In a traditional quantitative approach, a portfolio manager will combine these different signals together in a model that they build.

Why is the reality more of a steady evolution, as you alluded to earlier? And what makes that gradual approach potentially more effective than a sudden revolution?