While prediction based on data may be valuable in some cases, it isn't robust enough to scale up in any meaningful way. Context matters, like you state above, and most quantitative traders start by taking their contextual knowledge of the markets, and then collecting data on features, and THEN they fit a model to it.
Skipping these steps is only going to lead to a bunch of blowups. I doubt they have any meaningful sharpe that they could scale up or publicly defend with the approach they have taken so far. I'd guess they are paying people VC dollars, not actual market profits right now.
I do think its cool that they have been able to use homomorphic encryption to solve the problem of wanting anonymize data, but I'm not sure it actually helps in this case.
Im not optimistic...
Let's give many people some [encrypted] data to play with. May be someone will find a curious and unexpected pattern we didn't looked at before. May be this idea won't be workable straight away but we [hedge fund] are going to investigate this idea further and probably use this in our trading strategy.
In short, they are probably doing ideas mining using power of crowd.
- You need to know something about the domain in order to make sensible predictions. Is the data daily? Is it per second? Is it ticks? You can't build a sensible model if you don't know that, even if you have good predictions. Relative cost will vary a lot between timescales.
- It matters what the features are. Maybe there's some clever reason why it doesn't, but until I hear why I'm going to take the ordinary view that some features are different in nature to others. For instance maybe one feature is volatility, a thing we typically model with GARCH, while another is some fundamental like P/E, which we'd incorporate some other way.
- How are you executing the trades? It matters a lot whether you're click-trading through some broker API, automating via Excel, or running your own network of colo servers. Some things just aren't possible if you're too slow.
- If you make the data encrypted, you'd better know very well what it represents. For instance, you might take all the closing prices of the LSE stocks on a given day as inputs. You can make analyses that are valid with that, and ones that aren't, because the data you've collected do not represent a snapshot of the market at a specific time. It might sound like it does, but it doesn't on deeper inspection (market opens and closes are not simultaneous).
Does anyone know how it's going for them?