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Yet reality contradicts you. > What does python offer you? Lists, dictionaries, strings (none of which is of any use to scientific computing) pandas/numpy/sci

Yet reality contradicts you. > What does python offer you? Lists, dictionaries, strings (none of which is of any use to scientific computing) pandas/numpy/scipy/etc, so basically you get a blas/la pack equivalent, with a R/matlab feel. > there's no hope that your algorithms [...] will run unchanged in a decade, not to say in 50 years. Very few people care about that.

deleted 3y ago
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bitcharmer 3y ago
I have no idea why you're getting down-voted. I've worked in finance/algo-trading for 15+ years. Went through shops like Goldman Sachs, Morgan Stanley, UBS, HSBC. Quants everywhere use CPP and Python, sometimes R. I've never seen Fortran used anywhere.
- medo-bear 3y ago
  If they use Numpy in Python they surely use Fortran under the hood. Did you work in HFT? I would be very surprised if they used Python for that, and almost equally surprised if they all dismissed Fortran as an option. Besides the things you named, quants also use Excell (of course) but probably less known is K, which is an APL type language
  - Galanwe 3y ago
    > If they use Numpy in Python they surely use Fortran under the hood. Even that is disappearing. It was common practice even 10 years ago to use MKL as backend. > Did you work in HFT? Yes, I've done (and still do) HFT and mid freq since around 15 years. > I would be very surprised if they used Python for that Surprise! Nobody would dare do any heavy computation in e.g. C++ or Fortran or Cobol. It's just too unpractical, and slow to develop. You calibrate your models in Python/pandas/... , offline on historical data, and just use the prebuilt model to make the real-time prediction in C++, which doesn't involve much more than simple linear algebra. I would estimate conservatively that 80% of quants use Python, in both HFT and mid freq. There should be some niche where they use matlab, R, kdb or stata, but that's definitely a small fraction.
    - medo-bear 3y ago
      > Even that is disappearing. It was common practice even 10 years ago to use MKL as backend. Sorry I just saw this. And what do you think MKL does?
  - bitcharmer 3y ago
    I still work in HFT yes. A sibling comment lays it out better than I could, and I'm surprised that so many people here insist on Fortran's popularity when it's clearly absent from whole industries where you guys would expect to see it. Python is very widely used for prototyping and there's nothing anyone can do about it. In production the algos would be implemented in Cpp though. I'm merely stating a fact, not here to argue about anything. Raw K is almost never used but there's a lot of Q, depending on the shop.
  - merolish 3y ago
    Live trading systems are in C++ or Java, but like others have said Python is used for day-to-day research, sometimes R if the particular quant likes it. I never saw any Fortran in my time at Bloomberg in the 2010s but allegedly there was still a lot of it running in the nether regions.
- BeetleB 3y ago
  Although financo/algo-trading is math heavy, it does not fall into the category of scientific computing. Just as you haven't experienced Fortran, there are several areas in science that have not experienced C++/Python, and rely on Fortran.
  - Galanwe 3y ago
    > Although financo/algo-trading is math heavy, it does not fall into the category of scientific computing. Care to elaborate on why this wouldn't be the case? As far as I can see, half of my desk is ex CNRS/CERN/CEA/... , the other half is the top brightest PhDs and engineers. When I look at weather forecast models, or biological DNA white papers, I don't see much difference in terms of scientific process or category of problem than what we see in quantitative finance. It may be sad, but the truth is finance phagocytates an immense chunk of the scientific community,abd it provides. There are interesting and hard problems to solve, virtually unlimited compute resources, etc. Most top quant HF have yearly double digit million dollar infrastructure bills, compute and storage clusters rivaling or surpassing academic ones, dedicated hardware manufacturers, etc.
    - BeetleB 3y ago
      I'm not disputing that there is seriously heavy math involved - many of my fellow PhDs in physics/engineering went to finance. It's just not what people consider as "science" :-) My point is that branch of HPC developed somewhat independently of most of other science disciplines. It's also relatively newer, so it doesn't rely heavily on legacy code from the 70's/80's. Generally, newer disciplines (computational biology, modern ML, etc) don't use Fortran. Older stuff (physics, electromagnetics, most engineering, etc) are full of Fortran, and continue to be.
fastneutron 3y ago
> Very few people care about that. While it may be true that the absolute number of people involved is small compared to the wider tech industry, long-term code reproducibility is critically important for engineering-heavy industries with long-lived, high consequence physical assets. Think aerospace, nuclear, petrochemical, electrical, and other civil infrastructure. If something in the environment changes and you need to re-analyze the performance of that asset to ensure safety & reliability under the new conditions, the first thing you do is re-run the benchmark cases to verify the answers didn't change from the last time you ran it. If they did, it throws your entire basis for design into question unless you can conclusively resolve the inconsistency.
kergonath 3y ago
> Very few people care about that. In the general population, probably. There is quite a large overlap with people doing HPC and scientific computing in general, however.

deleted 3y ago

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bitcharmer 3y ago

I have no idea why you're getting down-voted. I've worked in finance/algo-trading for 15+ years. Went through shops like Goldman Sachs, Morgan Stanley, UBS, HSBC. Quants everywhere use CPP and Python, sometimes R. I've never seen Fortran used anywhere.

medo-bear 3y ago
If they use Numpy in Python they surely use Fortran under the hood. Did you work in HFT? I would be very surprised if they used Python for that, and almost equally surprised if they all dismissed Fortran as an option. Besides the things you named, quants also use Excell (of course) but probably less known is K, which is an APL type language
- Galanwe 3y ago
  > If they use Numpy in Python they surely use Fortran under the hood. Even that is disappearing. It was common practice even 10 years ago to use MKL as backend. > Did you work in HFT? Yes, I've done (and still do) HFT and mid freq since around 15 years. > I would be very surprised if they used Python for that Surprise! Nobody would dare do any heavy computation in e.g. C++ or Fortran or Cobol. It's just too unpractical, and slow to develop. You calibrate your models in Python/pandas/... , offline on historical data, and just use the prebuilt model to make the real-time prediction in C++, which doesn't involve much more than simple linear algebra. I would estimate conservatively that 80% of quants use Python, in both HFT and mid freq. There should be some niche where they use matlab, R, kdb or stata, but that's definitely a small fraction.
  - medo-bear 3y ago
    > Even that is disappearing. It was common practice even 10 years ago to use MKL as backend. Sorry I just saw this. And what do you think MKL does?
- bitcharmer 3y ago
  I still work in HFT yes. A sibling comment lays it out better than I could, and I'm surprised that so many people here insist on Fortran's popularity when it's clearly absent from whole industries where you guys would expect to see it. Python is very widely used for prototyping and there's nothing anyone can do about it. In production the algos would be implemented in Cpp though. I'm merely stating a fact, not here to argue about anything. Raw K is almost never used but there's a lot of Q, depending on the shop.
- merolish 3y ago
  Live trading systems are in C++ or Java, but like others have said Python is used for day-to-day research, sometimes R if the particular quant likes it. I never saw any Fortran in my time at Bloomberg in the 2010s but allegedly there was still a lot of it running in the nether regions.
BeetleB 3y ago
Although financo/algo-trading is math heavy, it does not fall into the category of scientific computing. Just as you haven't experienced Fortran, there are several areas in science that have not experienced C++/Python, and rely on Fortran.
- Galanwe 3y ago
  > Although financo/algo-trading is math heavy, it does not fall into the category of scientific computing. Care to elaborate on why this wouldn't be the case? As far as I can see, half of my desk is ex CNRS/CERN/CEA/... , the other half is the top brightest PhDs and engineers. When I look at weather forecast models, or biological DNA white papers, I don't see much difference in terms of scientific process or category of problem than what we see in quantitative finance. It may be sad, but the truth is finance phagocytates an immense chunk of the scientific community,abd it provides. There are interesting and hard problems to solve, virtually unlimited compute resources, etc. Most top quant HF have yearly double digit million dollar infrastructure bills, compute and storage clusters rivaling or surpassing academic ones, dedicated hardware manufacturers, etc.
  - BeetleB 3y ago
    I'm not disputing that there is seriously heavy math involved - many of my fellow PhDs in physics/engineering went to finance. It's just not what people consider as "science" :-) My point is that branch of HPC developed somewhat independently of most of other science disciplines. It's also relatively newer, so it doesn't rely heavily on legacy code from the 70's/80's. Generally, newer disciplines (computational biology, modern ML, etc) don't use Fortran. Older stuff (physics, electromagnetics, most engineering, etc) are full of Fortran, and continue to be.

fastneutron 3y ago

> Very few people care about that. While it may be true that the absolute number of people involved is small compared to the wider tech industry, long-term code reproducibility is critically important for engineering-heavy industries with long-lived, high consequence physical assets. Think aerospace, nuclear, petrochemical, electrical, and other civil infrastructure. If something in the environment changes and you need to re-analyze the performance of that asset to ensure safety & reliability under the new conditions, the first thing you do is re-run the benchmark cases to verify the answers didn't change from the last time you ran it. If they did, it throws your entire basis for design into question unless you can conclusively resolve the inconsistency.

kergonath 3y ago

> Very few people care about that. In the general population, probably. There is quite a large overlap with people doing HPC and scientific computing in general, however.