Tag Archives: library

Options Future Equation class library with Test Client

I resume my ‘Phase 2’ of my Algo Trading Course Series I started in March. After completing the “Infrastructure Building Blocks” and “Equity Arbitrage/Pair Trading”, it is time to focus on learning both futures and options trading. Get more details here:
http://quantlabs.net/blog/python-algo-course-series-for-your-indie-automated-trading-business/

This new Phase that will be taught is a 24-week section which covers many fundamental aspects of these asset classes. We will focus on examples as we mirror my favorite UC Davis course that focuses on fundamental analysis. I also provide both C++ and Python coding samples throughout this course. The main idea of these presentations is to learn further on these topics outside of the course material. Our topic for this day is:
Options Future Equation class library with Test Client A couple of notes: I will not be teaching content live anymore after this as I move into an analytics service in a few months.

You also need to be a Quant Elite member to attend this. Signup here:

http://quantlabs.net/mkt/quant-elite/

If you are a Quant Elite member, please preview the UC Davis video to become acquainted with the topic being presented. Login into the “Python C++ Algo Indie Trading Business Course Futures Options Algos” dashboard with in the membership.

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Source code for How to get great open source dlib C++ Bayesian Network library working on Visual Studio

Source code for How to get great open source dlib C++ Bayesian Network library working on Visual Studio

The source code is included for this demo:

See more at: http://quantlabs.net/blog/2014/04/how-to-get-great-open-source-dlib-c-bayesian-network-library-working-on-visual-studio/#sthash.IFV7M5FZ.dpuf

 

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How to set your R environment with RStudio and DotNet library for C#

How to set your R environment with RStudio and DotNet library for C#

IMPORTANT NOTE:  This train of thought has been abandoned with too many connecting issues with R.NET which makes this entire process useless. Sorry but I cannot proceed as it is not safe nor have the authors of all this software thought of backward incompatibility. This is why you stick with Matlab!!!


Download R and RStudio

Download R for Windows from your nearest location http://www.r-project.org/

Download RStudio IDE http://www.rstudio.com/

You want to add R_HOME to your Windows Environment Variables and add to the path of R.DLL. Mine is located under C:\Program Files\R\R-3.0.2\bin\x64

.NET library for C# (R.NET)

http://rdotnet.codeplex.com/

Download from:

http://rdotnet.codeplex.com/releases

C# Example of R.NET RDotNetExample

 

Research paper in R: Here is why GotoBLAS2 may be the fastest C multi threading library over ATLAS, MKL, GPU, FPGA, and others

Research paper in R: Here is why GotoBLAS2 may be the fastest C multi threading library over ATLAS, MKL, GPU, FPGA, and others.

 

I was just referred to this which shows GotoBLAS2 could be the fastest C multi threading library out there.

http://www.tacc.utexas.edu/tacc-projects/gotoblas2

 

These claims come from a vignette where there is an R package around these:

 

http://cran.r-project.org/web/packages/gcbd/index.html

 

I just extracted out of this from this research paper:

http://cran.r-project.org/web/packages/gcbd/vignettes/gcbd.pdf

 

.

Between the multithreaded

BLAS implementations, Goto is seen to have a slight advantage over MKL

and Atlas. GPU computing is showing promise but requires relatively large matrices to

outperform multi-threaded BLAS.

A second key aspect is the di_erence between static and shared linking. In static linking,

object code is taken from the underlying library and copied into the resulting executable.

This has several key implications. First, the executable becomes larger due to the copy of

the binary code. Second, it makes it marginally faster as the library code is present and no

additional look-up and subsequent redirection has to be performed…. Shared library builds, on the other

hand, result in smaller binaries that may run marginally slower|but which can make use of

di_erent libraries without a rebuild.

Shared library builds, on the other

hand, result in smaller binaries that may run marginally slower|but which can make use of

di_erent libraries without a rebuild.