I remember my first few months unlearning normal Python and figuring out how to build the 'pixie graph', a lazily-evaluated Python DAG suited for calculating financial instruments. It took a while to get your head around this, but when you did it was a very powerful and productive way to building trading and risk management applications.
To get some sense in how this worked, here are two public projects on github with good introductions:
It was ubiquitous in GS by the mid-2000s, and was rather instrumental to GS navigating the crisis in 2008.
Being able to accurately and quickly compute risks across the entire firm's books, rather than manually merging across separate systems was key advantage, which encouraged JP, Citi and BofA to build a SecDB clone themselves.
https://www.goldmansachs.com/our-firm/history/moments/1993-s...
I've not heard the term "twiddle scopes" before; in Athena it is "tweaking".
For finance applications (risk) the whole concept works quite well, doesn’t it - combining the natural advantages of code and Excel.
[Disclosure: I was part of the Quartz Core team in 2011/2012.]
I suspect the Common Lisp influence was beneficial.
The weirdest thing, language-wise, that I noticed was scoping. It's neither dynamic nor static scoping, but weird scoping. (But there are work-around to get something like static scoping.)
Outside of the language, the whole CVS-based version control and review process was weird. But understandable as a product of the late 1990s, when review-before-going-into-permanent-history must have been way ahead of its time.
Not the spaces in variable names?
But I was used to different and exotic conventions from the obscure languages I played with over the years.
JPM's actual pixie code is proprietary, extremely performant after 12 years of pushing into bigger and bigger scale problems, and is definitely not on GitHub!
That system you're thinking of was Kapital.
http://www.cincomsmalltalk.com/main/successes/financial-serv...
It's the only Smalltalk usage at JPM I believe, they could have doubled-down on it but instead went to Python and as you say, tried reinventing the wheel in it.
That's not reinventing the wheel, that's building a new wheel you hope performs better/different with different materials and maybe some different techniques, and has a long and storied history of both successes and failures.
Reinventing the wheel would be if they published this system and some other bank recreated it without using what they published, thus "inventing" something that already existed and was available.
If it was like the predecessor system I worked with, it could handle either. But in a list comprehension, it could figure out the individual dependencies of the individual list items.
So, suppose you had a a function that took in a list, and returned a list with some computations performed on each element. If done with list comprehension, and one input element changed, only the corresponding output element would need to be recomputed, and only that would be recomputed.
If done with a loop, and one input element changed, the entire output array would be recomputed (even if all but one element remained unchanged - it would be hard to guarantee that property by code inspection at the time the dependency graph is built).
This would have had me running screaming from the building
AAAAaaaaaa
Imagine the benefit to the business had they just stuck with Smalltalk the whole time! It is remarkable how many businesses could have been quietly making money for years using a high-quality dynamic language such as Smalltalk or Lisp rather than going through the whole C/C++/Java/Python/Ruby/JavaScript/Go treadmill.
But I guess if they had done that then their programmers might not have gotten to type as many {}! Nor gotten to reinvent the wheel so often.
And Kapital was an interesting experiment but rightfully died. Smalltalk just wasn't the right environment for something that complex and scale. I'd expect most lisps to suffer in the same way. And TBH it was more the image based model rather than the dynamic language per se though that was an issue.
There's a point where dynamism becomes a liability and Kapital went way beyond that point. Python fares a bit better but still, a huge effort is needed to control its failings at scale. This isn't helped at all by sticking a DAG in the middle of it as was done with Athena and its ilk. This all can be made to work but in the "cos its Turing complete" sense rather than the language helping in any meaningful way.
Source: I suffered through it.
I had to LOL. You have very nicely put into words an experience I bet many have had.
Why? Eg Scheme or even the uglier Common Lisp are quite disciplined. And Clojure ain't too bad either.
As far as I can tell, Lispers don't usually go around monkey-patching things.
That was the official story anyway. A lot of things were claimed when I worked there, a lot of it BS.
The culture in banking was definitely not for me: generally fairly smart people, but very perverse culture. This was the only environment I ever worked where people who actually lie to my face to make sure I made mistake when coding and fail. Everything was custom, and sometimes that meant athena, sometimes that meant terrible systems such as a "distributed database" that was a single threaded wrapper on top of an C++ STL hashmap running on NFS. The guy behind it was reworking its own protocol based on UDP instead of TCP because said system was too slow...
There was 0 abstraction, so if you wanted something such as EUR/USD pair, you had to ask the one guy who knew which id it was, so that you could get the data from in code. To this day I am convinced this was done on purpose for job security.
(I realize Smalltalk is great, but my point is it has its issues as well otherwise I recon sooner or later the advantages would be so clear that Amazon or Google or someone would be all over it since it would give them a competitive edge ...)
I understand the value of it, but as an experienced scientific developer and python dev, the culture shock was huge. I don't have fond memory working on it. It is very different from traditional programming environment, closer to a kind of reimplementation of smalltalk env w/ python. I believe an influence was actually an old system that used smalltalk in the 90ies at JPM.
One of the fancy thing was integrating reactive programming, which worked through ugly hacks, at least at that time, by parsing code to detect dependencies. IIRC, it could manage list comprehension but not loops. They also had their own python binary w/ both python2 and python3 in one process.