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Comment by chollida1 | Hacker News Reader

Wow, I'm surprised that this is the first funding they've ever got.

It wouldn't be a big stretch to say that 90% of quantitative hedge funds use Numpy in some fashion, whether its directly, or via a library that sits on top of it like pandas or tensorflow.

I can't think of a more ubiquitous library in the financial space, maybe QuicFix (http://www.quickfixengine.org/)...

Maybe numpy's problem is visibility?

Possibly it does its job so well that people don't know they are using it when they use library libraries like scikit learn and Pandas?

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I didn't know it needed funding until I read this.

It's just an assumed resource in quant finance, like air or water. You do realise you're using it, though. When you're using scikit or pandas it's very normal to do "import numpy as np". And you get the odd np.nan reminding you.

Do people still use QuickFix? I dropped it years ago, it was noticeably slower than alternatives when I tested it.

Tons of people still use QuickFIX. As I think you know, people very concerned with speed try to avoid using any kind of FIX for trading but still use QuickFIX for things outside the hot path such as drop copy (duplicate stream of trades for reliability).

QuickFIX is open source too, so you can make it somewhat faster without abandoning it. I did.

Don't be surprised, and don't feel bad. There is innumerable open source software we all depend on everyday without thinking about it. Consider zlib, libpng, libjpeg(-turbo), bash, bsd or gnu core utilities, random stuff ... and then the stuff you might sometimes think about, like openssl ...

That's maybe the biggest practical benefit of open source software. You don't have to keep track of who you owe what. A lot of these projects have had a few different critical creators and maintainers over the past decade or so. And we don't have to keep track of any of that. That's a huge efficiency boost.

(You should not re-distribute open-source software in a way that violates the license, but that's a separate issue from using it, and it scales a lot easier - everyone receives/uses many more different software works than they distribute.)

There's been quite a bit of private funding, through Enthought and other companies.

I don't think this is very true. There hasn't been much private funding for NumPy. People working at private companies have worked sometimes on NumPy but very rarely as part of their full-time job.

Oh, hey, Travis! (This is Joe Cooper, I worked at Enthought as a contractor for a few years before you joined the company full-time; we met a few times in Austin.)

You would obviously know better than I. I guess I considered employing people who were working on NumPy as "funding", but possibly not on the scale or with the focus of a specific grant. So many of the folks who do scientific computing with Python have gone through Enthought, it seems kinda like everyone has drawn a salary or contract work from there at some point. But, I guess a lot of the work at Enthought was focused on making the tools palatable to industry rather than the actual science side of things, and much of the math they're packaging came from the academic world.

Numpy is so 'normal' I think many people believe it is part of Python.

Visibility, REALLY? Was openssl problem also visibility?

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