It's still a good idea to engage the list as you think about this.  A good
exercise is to mock up a little example a simple use of your proposed
module.  I'm not suggesting building it, I'm suggesting showing inputs and
desired outputs.


On Tue, Mar 4, 2014 at 2:12 PM, Chase Relock <[email protected]> wrote:

> As I e-mailed Matt just now, I think this is a great idea. I will most
> likely start with the symbolic construction of Markov Chains and work from
> there.
> -Chase
>
>
> On Tuesday, March 4, 2014 9:02:55 AM UTC-8, F. B. wrote:
>>
>>
>>
>> On Tuesday, March 4, 2014 6:18:18 AM UTC+1, Chase Relock wrote:
>>>
>>> I was looking at SymPy's matrix code and was curious if that supports
>>> symbolic computation right now or is it only numeric?
>>>
>>
>> Of course you can put symbols inside sympy matrices (if that is what you
>> mean), for numeric-only ones, have a look at numpy (technically numpy could
>> be forced to use symbols, but it's not straightforward).
>>
>>
>>> As for some of the individual pieces, I think the following are
>>> important concepts needed in financial statistics that I am unsure SymPy
>>> currently has functionality for:
>>> kernel density estimators for PDFs
>>> Empirical distribution functions
>>> Statistical moments
>>> Stochastic Processes
>>> Stochastic/Ito Calculus
>>> Time series
>>> Matrix decomposition (spectral, singular value)
>>>
>>>
>> What about introducing stochastic processes, Martingales and Ito
>> integration (all in symbolic representation, of course)? I think that could
>> already be a good project.
>>
>> By the way, I have been using the sympy.stats module, and I get a lot of
>> *NotImplementedError*, and other kinds of errors. Finishing that parts
>> would be great, too.
>>
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