Workshop: Supercharge your Quant Models: Unlock Pythons Potential for Production

In this workshop, we will provide a practical solution to address the trade-off faced by quants: the ability to prototype in Python quickly and the need for high-performing models. We will demonstrate an easy-to-use framework combining Python flexibility with AAD (Automatic Adjoint Differentiation) and "bare metal" performance, speeding up models by a thousandfold. Our solution is define-by-run, meaning no usual @jit decorators, special C-like syntax, or heavy restrictions on function bodies. You also get to keep any existing object-oriented code, including QuantLib - we made it as plug-and-play as it gets! We will focus on several use cases, including model calibration and curve bootstrapping, and cover the XVA with a focus on live risk. Additionally, we will demonstrate how such workloads can be serialised for cloud execution, seamlessly achieving performance at scale.

MatLogica specializes in software solutions that allow to accelerate Monte-Carlo Simulations using highly parallel vectorized software and automatic adjoint differentiation. At the moment we are developing a breakthrough C++ tool for AAD. Our unique approach allowed us to obtain impressive benchmarks compared to other well known AAD tools. If you are interested in getting the best performance from your existing or new C++ library, we can offer quick proof-of-concept projects to gage the possible benefits our tool can bring to you.

MatLogica brings together a broad range of specialists: from quantitative analysts and computer science engineers to academic researchers. The company was organized around an invention which forms the kernel of the new Adjoint Differentiation Tool.

We specialize in parallel computations in a wide range of areas including XVA, MVA, Monte-Carlo Simulations, Derivative pricing and Risk, Large Portfolio simulations, Model calibrations such as Heston SV and LMM, among others.

matlogica.com