They did say it didn't work. The overwhelming majority of finance stuff in published work doesn't work, because they're either too simplistic, poorly backtested, or they get exploited too quickly, so beating those doesn't imply you can run a hedge fund.
The main part here is that it's one thing to predict price action, it's another thing to trade profitably - and in particular they were not able to beat fees, which is a common hurdle if you're new to HFT.
Basically this. We were heavy infra pros and my cofounder was an HFT veteran so it wasn’t classic implementation shortfall so much as we didn’t solve the “do we enter” threshold on what would be a friction-free windfall.
What they describe looks like a single predictor. You can't create a strategy with a single predictor, unless it's incredibly predictive. 99% of the time, a predictor cannot beat its transaction costs alone.
You need to combine hundreds of such predictors to be able to beat costs and have a net profitable strategy.
We have a saying in French that you need a lot of rivers to create a sea.
So the group involved veterans from like Knight and DRW and stuff: we understood the model of combining lots of small signals with a low-latency regression.
We were trying to learn those signals as opposed to sweat-shop them.
Wasn't the US housing crisis of the late 2000s caused by that 99% threshold?
Not in finance at all but I do use reverse Kalman filters, to which this seems similar in core concepts.
While reverse Kalman filters are incredibly helpful in reducing cloud spends by predicting when to auto scale, you still have to have metrics to quickly recover from mistakes.
Based only on tech interviews with HFT companies, I would assume someone could predict your moves using these methods based on historical data.
But perhaps I am just too risk adverse or am missing the core concept.
