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Randomized geometric tools for anomaly detection in stock markets
Integrate the code from https://zenodo.org/record/7198256 into volesti repository.
The code implements the methods in this paper [presented in AISTATS 2023].
For more details contact the mentors.
Difficulty: Medium
Medium (175 hours)
- Required: R, some familiarity with C++, background in mathematics
- Preferred: Experience with mathematical software is a plus
This is a very useful project for volesti, since it will enhance it with a finance tools that could be helpful to detect anomalies in stock markets.
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Bachelard Cyril <cyril.bachelard at quantarea.ch> He serves as the Head of Quant Engineering and is a founding partner at Quantarea, a quantitative Asset Manager in Switzerland. He has 12+ years of experience in quantitative portfolio management and systematic equity research. His areas of expertise include high-dimensional portfolio optimization, machine learning, and signal processing for dynamic asset allocation.
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Apostolos Chalkis <tolis.chal at gmail.com> is a Research Engineer at Quantagonia GmbH. He is an expert in statistical software, computational geometry, and optimization, and has previous GSoC student experience (2018 & 2019) and mentoring experience with GeomScale (from 2020 to 2023).
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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).