Building an R interface to use volesti for financial applications

Overview

The project will implement an R interface to represent financial data and regulatory constraints using geometric objects and notions. Then, it will construct those objects using R volesti tools. The interface will be also able to call volesti's routines (i.e. preprocessing, sampling) for the constructed geometric objects.

Details of your coding project

The interface will have to be based on standard R packages that handle financial data. Thus, it will follow a similar paradigm of package-user interaction (i.e. use similar data structures to read and store the data). Then, from those structures the contributor will have to construct the corresponding geometric objects (e.g. convex polytopes, ellipsoids, etc).

Skills

Required: R, Basic applied math and computational geometry background
Preferred: Experience with financial software is a plus

Expected impact

The projects will provide GeomScale with an R interface toward a new R package in computational and quantitative finance.

Mentors

Bachelard Cyril <Cyril.Bachelard at olz.ch> He is a Senior Quantitative Research Analyst in OLZ AG since 2011. He is an expert on Quantitative Research, risk forecasting, and optimization models. He is also a Ph.D. student in Algorithmic Sampling and Portfolio Optimization at the University of Lausanne. He holds a master's degree in economics and has further completed studies in mathematics, statistics, and computer science.
Apostolos Chalkis <tolis.chal at gmail.com> is an expert in statistical software, computational geometry, and optimization, and has previous GSoC student experience (2018 & 2019) and mentoring experience with GeomScale (2020 & 2021).
Vissarion Fisikopoulos <vissarion.fisikopoulos at gmail.com> is an international expert in mathematical software, computational geometry, and optimization, and has previous GSOC mentoring experience with Boost C++ libraries (2016-2017) and the R-project (2017).
Elias Tsigaridas <elias.tsigaridas at inria.fr> is an expert in computational nonlinear algebra and geometry with experience in mathematical software. He has contributed to the implementation, in C and C++, of several solving algorithms for various open-source computer algebra libraries and has previous GSOC mentoring experience with the R-project (2019) and GeomScale (2020 & 2021).

Students, please contact the mentors after completing at least one of the tests below.

Tests

Students, please do one or more of the following tests before contacting the mentors.

Easy: Compile and run volesti in R. Use the R extension to visualize sampling in a polytope.
Medium: Sample approximate uniformly points from a randomly generated polytope using the implemented in volesti random walks, for various walk lengths. For each sample compute the PSRF and report on the results.
Hard: Use R volesti to sample from the set of long-only portfolios with 5 regulatory constraints defined by you.