Something I have not seen in the responses, which uses forks, is:
A =: 0&{
B =: 1&{
C =: 2&{
D =: 3&{
leading to:
ExpectedVolatility =: (A+B)*C+D NB. usage: ExpectedVolatility data
being the J-esque solution.
Joe Bohart wrote:
> Hi All,
>
> Been learning J for a bout a year now and very impressed - although it's a
> mind bend. I feel my understanding of complex problem is clearer however i
> have yet to actual code something interesting in J.
>
> Here is an interesting problem that I'd like some input on......
>
> I'm trying to implement an Expected Volatility equation 4.20 from the book
> (by the Olson group) High Frequency Finance
> in J and I'm curious if this is the correct J-way of solving a simplified
> subset of the problem:
>
> ExpectedVolatility = (A + B) * C + D
>
> so super simple problem reduction:
> data =: 1 2 3 4
> goal (non j notation): (1+2)*3 + 4
> 1st-cut: (0{data + 1{data) * 2{data + 3{data
> 2nd-cut: +/ 0 1 & { data * 2{data + 3{data
> 3rd-cut: I'm sure there is a more elegent way to do this, maybe using forks
> ????
>
> I'm curious if the following is the correct way to go about solving problems
> in a J-way:
> The actuality data matrix would be something like:
> (datetime,px,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o)
>
> I need a verb to transform this noun
> (datetime,px,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o)
> into this (easy to digest noun)
> (A B C D)
> where i have not defined the relation,
> then I'd need another verb to transform the easy to digest noun into the
> Expected Volatility noun where i have defined the relation above (but
> probably not in an elegant j-way).
>
> Am i on the right path for thinking in J, thoughts, comments...greatly
> appreciated.
>
> Thanks,
> Joe
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>
>
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