On Tuesday, 23 December 2014 at 03:07:10 UTC, Laeeth Isharc wrote:
It would certainly be nice to have matrices, but I also don't
think it would be right to say D is dead in water here because
it is so far behind. It also seems like the cost of writing
such a library is v small vs possible benefit.
I have a longer horizon than the HFT guys, but I still have quite
a demand for high performance computing when backtesting a
quantitative strategy. A backtest will typically involve
1) Put some data in a database
2) Apply statistical models to appropriate data
3) Create forecast distribution
4) Optimize portfolio given forecast
5) Repeat 2:4 in each period and calculate performance of strategy
The biggest limiting factor to implementing it in D is a mature
math/stats library (I understand SciD is a thing, but I have not
tried it). Most optimization packages are written in C and could
probably be called in D (haven't tried, but I imagine). There's a
mysql module for D, though I think python has much better options
here (I have been pretty impressed with blaze). Python's Pandas
is also pretty helpful, but as it is built upon numpy, something
equivalent would need to built upon a matrix library in D.
I think it would also be helpful for bindings to Julia and C++
(so I can use MC Stan or Quantlib). I think the pyd project is
pretty important. Might be good to write up an example using it
for a finance application.
