I had started the QuantLib.jl package, but the goal was basically a rewrite 
of the C++ package in Julia.  I haven't given it much love lately, but I 
hope to pick it back up sometime soon.  Anyone who wants to join in is 
definitely welcome!


On Saturday, September 17, 2016 at 11:28:36 AM UTC-4, Chris Rackauckas 
> Thanks Femto Trader for bumping this. I took a quick look at Quantlib (and 
> Ito) and I have to say, their numerical methods are very rudimentary (in 
> fact, one of their methods for stochastic processes, EndPointEuler, doesn't 
> have finite moments for its error due to KPS 1994...). For anything that 
> isn't a Jump Process you can currently use DifferentialEquations.jl which 
> has higher Strong order methods for solving the SDEs (with efficient 
> adaptivity coming whenever my paper gets past peer review... short summary: 
> mathematicians don't like computer science tools to show up in their math 
> papers even if it makes it faster...). That's the thing though, you have to 
> know the stochastic differential equation for the process.
> That said, it would pretty trivial to use dispatch so that way you define 
> a "GeneralizedBlackScholes" equation, when then uses dispatch to construct 
> an SDE and apply an optimized SDE method to it. Since you can already do 
> this manually, it would just take setting up an object and a dispatch for 
> each process. Would this kind of ease-of-use layer for quants be something 
> anyone is interested in?
> The other thing is the Forward Kolmogorov PDEs associated to the SDEs. 
> Currently I have FEM methods for Poisson and Semilinear Heat Equations 
> which, as with the SDEs, can define any of the processes. This has a few 
> more fast methods than Quantlib, but it doesn't have TRBDF2 (but that would 
> be pretty trivial to implement. If you want it let me know, it should take 
> less than hour to modify what I have for the trapezoid rule since it's just 
> about defining the implicit function, NLsolve handles the solving).
> However, for most PDEs in finance you likely don't need the general 
> boundaries that FEM provides and so FDM (finite difference methods) can 
> probably be used. I haven't coded it up yet because I was looking for the 
> right implementation. I am honing in on it: ImageFiltering.jl gives a good 
> n-dimensional LaPlacian operator (and if I can convince Tim Holy it's 
> worthwhile, parallel/multithreaded), and I will be building up Grids.jl 
> <https://github.com/JuliaMath/Grids.jl/issues/3> memory-efficient 
> iterators for storing the space. This should lead to blazing fast FDM 
> implementations where the only actual array are the independent variable 
> (the option price) itself, so it should also be pretty memory efficient. 
> I'll be pairing this with the standard methods but also some very recent 
> Implicit Integrating Factor Methods (IIF) which should give a pretty large 
> speedup over anything in Quantlib for stiff equations. Would anyone be 
> interested in a quant ease-of-use interface over this as well? (If you'd 
> like to help speed this up, the way to do that is to help get Grids.jl 
> implemented. The ideas are coming together, but someone needs to throw 
> together some prototype (which shouldn't be too difficult))
> Note that Jump Processes can easily be done by using callback functions 
> (independent jumps can be computed in advance and then use an appropriate 
> tspan, adding the jump between the intervals. Dependent jumps just need to 
> use a callback within to add a jump in the appropriate intervals and maybe 
> interpolate back a bit, likely better with adaptive timestepping), and I'll 
> probably make an API to make this easier.
> Let me know what you guys would like to see on the differential equation / 
> stochastic processes side and I'll make it happen. I'm doing most of this 
> stuff for SPDEs in stochastic systems biology, but the equations are 
> literally the same (general SDEs and semilinear Heat equations) so I'm 
> plowing through whatever I can.
> On Thursday, October 1, 2015 at 7:34:32 PM UTC-7, Christopher Alexander 
> wrote:
>> I think the Ito package is a great start, and I've forked it to work on 
>> adding to it other features of Quantlib (as best as I can!).  I'm glad 
>> someone mentioned the InterestRates package too as I hadn't seen that.  I 
>> work at major bank in risk, and my goal is to at some point sell them on 
>> the power of Julia (we are currently a Python/C++ shop).
>> - Chris
>> On Friday, September 11, 2015 at 2:05:39 AM UTC-4, Ferenc Szalma wrote:
>>> Are there any quant finance packages for Julia? I see some rudimentary 
>>> calendar and day-counting in Ito.js for example but not much for even a 
>>> simple yield2price or price2yield or any bond objects in Julia packages on 
>>> GitHub. What is the best approach, using C++ function/object from Quantlib, 
>>> to finance in Julia?

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