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.
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.
We were trying to learn those signals as opposed to sweat-shop them.
But the broader point holds: signal isn’t alpha.
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.