[eigenvalue]

I was able to explore new ideas much much faster using Python than the experienced k people could. But creativity is more important anyway. Ultimately, having good ideas/data/signals trumps fancy or fast data wrangling. Glad I’m doing other things now in any case.

[gitonthescene]

Any detail whatsoever would make this a more credible claim. I haven’t met many people, including those skeptical of the performance claims, who have called K _slow_. Maybe for particular domains but I’d doubt that includes the kind of quant work that gets done at Millennium.

[spopejoy]

I've heard plenty of complaints over the years, and only within quant, unsurprisingly since that is the only field you'll get paid to use K.

A brilliant programmer I met who came from DE Shaw said he reimplemented a K-based portfolio optimization pipeline because the performance hit a wall once the dataset got large enough. He was able to beat K with Java of all things.

Columnar and timeseries dbs have continued to evolve, K is the same tech it was in the 2000s. The only reason it gets used at a Millennium is that whatever trade is still printing money, not any tech advantage.

[gitonthescene]

I appreciate it's probably not possible to share too many details but I wouldn't be surprised if the choice of Java wasn't simply preference. It may have been a problem with the pipeline rather than with K. I.e. a fix might have been available using K but it can be easier (and harder) to just use some out-of-the box solution. I agree that columnar and time series dbs may have caught up with K over the years, but most of the complaints I've heard about K aren't technical.