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Every trading strategy has a capacity -- the maximum amount of capital that you can put behind it before P&L stops going up. Trading a signal "to capacity" means finding this maximum and exploiting the edge as much as you possibly can.
You can simulate it with a simple model. Generate a signal and some returns so that the signal predicts returns -- with most predictability on the same day, but some predictability 5-6 days into the future.
You can compute the cross-correlation function (a chart showing how well the signal predicts the returns for same day, t+1, t+2 etc) to see how the predictive power falls away over several days.pic.twitter.com/qKjKwi5rza
Now simulate trading this signal, where you include both temporary and permanent market impact in the simulation. The temporary impact scales like (trade size)^3/2 and the permanent impact scales linearly with trade size.
The temporary impact is what prevents you scaling the signal arbitrarily since it grows faster than the size of your trades. The permanent impact represents how you distort the market by trading the signal.
Experiment to find out where the point of maximum P&L is, and record the returns that come out of the simulation (they are different to the input returns because they include the permanent market impact that your trading generated)
The cross-correlation of the signal with the returns now looks like this - very different! The same-day correlation is is much higher, and there is no predictability for t+1 or beyond.pic.twitter.com/w9O1zP0yMz
To a casual researcher who doesn't look at fast enough timescales (or doesn't have the infrastructure to trade that quickly) it will look like this signal has been arbitraged away and they probably won't try to trade it!
That's what I think the quoted Renaissance exec means when they say that they trade signals "to capacity" so that other researchers won't discover them. I've no idea how important it is for their edge, but it surely doesn't hurt. Fin.
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