Python toolkit for quantitative finance

The problem is that the models (closed sourced or open source) only get you part of the way. For example, (to name just a few items) a stock option pricing model is useless without

- holiday calendars

- ex dividend dates

- interest rate curves

- real-time stock prices

- corporate actions database

Are there open source and free sources of the above? For the first two, sort of, for the remainder, no. And I'm sure I'm forgetting a number of other inputs.

Does someone have LOC as a performance indicator?

The pointless act of "encapsulating" nothing? I see that a lot unfortunately =[

It’s so bizarre my first reaction was that surely it must be so an LLM can make some kind of sense of it, otherwise what on earth are they doing?

What about the @plot_function bit? Also, wrapping dependency calls for a more consistent interface isn’t necessarily a bad thing.

Is there a way out of this dilemma?

Are there any decent APIs for UK data, by the way?

Most data vendors will require a free API key because that's their GTM. They want you to create an account with them to get a free API key and then expect to be able to upsell you over time.

PS: I'm the main creator behind the OpenBB project on GitHub.

As you point out the GTM of others, can you tell us what your GTM is?

To make money, write software for people who will make money.

In readme, they should write tutorial on how to make money with this.

You don't make money with this.

Trading is a zero sum game, no one is ever going to give up their secret sauce

How could one study said design? By generating class diagrams or what?

I was really surprised there aren't any real examples here. You have a couple of videos linked, but am I really going to pause the video and hand copy each line of code?

Nah this looks pretty orthogonal to that. This just looks like a collection of pure python libraries for doing common quant work. The thing Cal Peterson is describing (which is pretty transparently JP Morgan's Athena) would be SecDB at Goldman and would be running on their proprietary scripting language called "Slang". None of that is open source.

Goldman was the first place to do a system like that, and when it was copied at other investment banks like JP Morgan and Bank of America, they opted to use Python instead of an in-house language and so "bank python" was born. Actually, the banks all poached engineers from one another, so many of the people that built the system at one place ended up building it again at another, hence why there are so many similarities between the equivalent systems at all these US investment banks. Some of those people eventually went on to build it again as a SaaS offering: https://www.beacon.io/

Still very much present, it powers "SecDB" (which is pretty much the nervous system of the entire markets business). While there's certainly been openness to Python/etc and tech to integrate Slang into the 21st century, it's the kind of thing that's hard to imagine ever being phased out.

"Copyright 2020 Goldman Sachs."

Probably added in panic mode around March 2020?

I have bad news for you. Most code you tend to come across is at that level.

That's what you find in a lot of domain specific libraries written by scientists, mathematicians, etc. Professional engineer-quality code written by people who aren't professional engineers is rare. Or it's some enormously popular library that has had a lot of attention from engineers over the years.