erkandem / calcbsimpvol Goto Github PK

I was wondering if any performance optimization has been explored on the GPU for this. In particular, I was thinking that Python JAX would be very well suited to this optimization. I'm still familiarizing myself with the source/algorithm used here for the computation, but in addition to providing optional GPU-backed numpy for computing, JAX also provides:

jit(), for speeding up your code
grad(), for taking derivatives
vmap(), for automatic vectorization or batching.

in case any of these would be helpful here. A demo of computing the greeks via differentials on Black-Scholes from grad() is shown here, although I've found that the demo is further improved with jit() on the functions there.

You can simply change S in line 236 to S[cp==-1] and similarly where it occurs for [cp==1]

Is your feature request related to a problem? Please describe.
the CI runs tests for 3.8 max. we could add 3.9+ to the pipeline

Describe the solution you'd like
Add 3.9+ to the CI runs

Describe alternatives you've considered
Using chicken bones as part of a Voodoo ritual.

Additional context

Hi Erkan,

I have been using your calcbsimpvol and it's awesome! A real life saver.

Now, while trying to use it one maturity at a time (to apply it to options
on futures, where the underlying is a specific futures contract), and
passing a 1-dim vector as 'cp', I get get an error in the broadcasting
operation line 254 linked to the shape of tau. I looked further and
wanted your opinion on whether the code had an an error in the function
"calcbsimpvol" lines 187 and 189? Shouldn't size(q) and size(cp) be
size(q)==1 and size(cp)==1?

calcbsimpvol/calcbsimpvol/src/calcbsimpvol.py

Line 187 in b38639a

if size(q):

and

calcbsimpvol/calcbsimpvol/src/calcbsimpvol.py

Line 189 in b38639a

if size(cp):

Best,
Greg

On Tue, Mar 24, 2020 at 5:00 PM Erkan Demiralay [email protected]
wrote:

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Originally posted by @gregdesu in #1 (comment)

I would have committed some changes directly into master but let's use a this issue as a wrapper.