Comment by eigenvalue

1 year ago

Was working at a quant pod at Millennium for a bit where they used it. I was ultimately able to use it but everything took me 20x longer than using Numpy/Pandas. The irony was that the Python code was shorter because there were so many more library functions and better abstractions and syntax. So it was slow and unintuitive for zero benefit whatsoever.

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eigenvalue

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eggy  1 year ago

Geometric mean in Numpy vs. J:

(Copied from some forum, since I don't use Python much)

  import numpy as np

  def geo_mean_overflow(iterable):
      return

  np.exp(np.log(iterable).mean())

Or,

  from statistics import.
  geometric_mean

  geometric_mean([1.0, 0.00001, 10000000000.]) # 46.415888336127786

In J, since I don't know K:

  gm=:#%:*/

Even shorter than Python whether it's a canned lib routine or created from composing simple functions.

And I don't need to format code on HN in J because it's so short anyway, besides I don't know how!

wood_spirit  1 year ago

But how did your perf compare to the best of the K kicking quants around you? Were they too being less productive than they would have been in python?

I’m not saying they were right or better. Horses of courses. Array languages do my head in and my choice is sql.

  • eigenvalue  1 year ago

    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  1 year ago

      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.

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