I agree that this is generally true, but Finance is sort of a special case. In a world where firms pay the premium for colocated servers and direct data feeds in order to shave off a few milliseconds of latency there is also a strong push to use the vary fastest language possible + have the very best possible architecture, all to shave off as many extra milliseconds as possible.
That's only true for a very niche sort of trading (only high-frequency/latency arbitrage type stuff) which makes up a tiny portion of the financial industry[1]—and that tiny portion is certainly not using Julia for production trading systems.
Everyone else has more reasonable performance constraints.
[1]: I can't find my source right now, but I remember seeing a credible estimate that the HFT industry in the US takes in $10 billion in revenue. That's a drop in the bucket of an industry that's measured in trillions just in the US.
Intuitively this makes a lot of sense because HFT strategies make a tiny amount of money per trade and have to do a ton of trades across a ton of different securities—which means the capacity of HFT strategies is relatively small.
Good point. Although there are some other areas of trading where speed matters that aren't HFT. An example is the algorithms used to split up large orders into smaller trades in order to hide the position and have a minimum impact on the market price. These are often optimized for very low latency as well. Especially the more sophisticated versions that do things like halt execution in the event that there is news on the stock, etc.
Volume isn't a particularly relevant metric here. Think of it as the resolution HFT firms trade at rather than a measure of the size of their trading activity. It's not a perfect analogy, but the point is that trade volume is not the best metric for understanding the scope and impact of HFT. It's definitely not an indicator that HFT makes up 30-70% of the finance industry in any meaningful capacity.
Here's a thought experiment: how much would things change if HFT firms traded half as much, with double the margin on each trade? Some securities would be a bit less liquid but otherwise almost nothing would change. It certainly wouldn't be a 2x difference from the status quo!
HFT having a lot of volume is irrelevant, what matters for Julia to be prevalent, is to have a lot of analysts use the toolset. You should measure the amount of analysts working in HFT vs those not working in HFT.
Furthermore - HFT is perhaps the worst case for Julia, since the implementation will always be in low level languages. Julia's speedup is only useful for research in HFT.
I doubt anyone's currently using Julia for the fast path of low latency trading. Usually it goes from FPGAs for sub-milli work, C++ on GPUs then either C++ or Java for multi-milli work. In my experience.
I'd expect most of the Julia usage in this space would be to replace any R or Matlab in the back testing engines if at all.
I didn't get the impression from the article that they were really talking about low latency anyway.
That's not to suggest that Julia doesn't have a place in Finance. I think it's a great language and there are a bunch of places I can see it being very valuable. Just doubt that low latency fast path is one of them in the near term.
