Yes, there are.
For pure technical indicator libraries I would first check out:
Its open source and they provide APIs for both C# and Java among others.
Let me know if you look for commercial ones but this one is definitely the most comprehensive in terms of open source code.
I work with time series intensively, and I am experienced in Java and scripting languages such as MATLAB and R. I strongly suggest that you should cook up your own implementations in Java, and stop hunting for and relying on any off-the-shelf implementations. They are not reliable. One should be able to write std, corr, cov, ma, etc., easily by hand. Coding ...
I'm not aware of an industry-standard Quant Finance library in the F# space, but there are plenty of high-quality commercial and open source alternatives. See the F# Software Foundation's Math Stacks page.
F# is used in a wide range of finance scenarios.You can read some experience reports from the F# Software Foundation home page.
In my opinion F# will never ever take off in ways C++ or C# has become popular. There are way too many competitive functional languages out there and if you program functionally why would you ever want to lock yourself into a MS product, at least that is an argument I have heard multiple times.
There is no comprehensive F# library out there right now that ...
There are two open source libraries that you should take a look at.
Both feature a wide list of products and models.
QuantLib. Written in C++ but usable in other languages such as Python. The library is developed for several years now. A feature that might come very handy is that there are toolboxes to implement derivative pricing libraries in Excel. Take a ...
F# is a relatively recent programming language: it was only included with Visual Studio since the 2010 version. Therefore, there is little chance that programmers had the time to agree on a common library, especially given the fact that Quantitative Finance is a broad field and hence different libraries might be better in specific areas.
I still think the ...
You might have a look into the CRAN's "Empirical Finance" task view. It lists a whole bunch of R packages for time-series analysis and construction of automatic trading rules.
I currently use a combination of matplotlib and Oanda's FX API. Their API is REST based, and would essentially allow for any type of library to handle calculations. A python wrapper for the Oanda API is on github
Not that I know, and I'm in that business. Allegro is a ETRM provider, equivalent to Murex in the banking area, but, as far as I know, they don't provide any library outside their main system (which is not able to deal with any kind of exotic), and definitely nothing open source. Let me know if you find anything, because I would be glad to use it.
There is no one definitive programming language to be used for this.
As Attack68 stated, a library written mostly in Python while taking advantage of Python's fast libraries written in C would be a good choice for the following reasons:
Again, as the comment stated there is a large community of Python programmers, plus the community supporting the ...
Let me give you the perfect solution.
The charting, graphing and analysis can be done using the PyLab environment.
You can integrate the code into R using the package called rPython.
You can integrate it to C and many other languages.
Python also comes with infinite more features. So instead of looking for a particular library, use Python.
for Java you may try:
Hope this helps!
I suppose it will be difficult to provide a precise response as it is a fairly vague question and the reality is quite diverse.
From my personal experience, the Quant I used to work with are using techno as R, Matlab combined with Visual Basic. Regarding more sophisticated tool coded in Java or C#, they are most of the cases inhouse frameworks.
So the only ...
Strata seems like a fairly well designed library, which is an open source library designed by OpenGamma. From their docs
Strata allows financial systems developers to build or enhance
existing applications with standardized, off-the-shelf market risk
components. It provides all the core concepts and market risk
functionality at the heart of the ...
Here is library for time series modelling. There are exponential smoothing models (simple, double, triple) with maximum likelihood estimation and another time series utility classes: