Hi, I'm Vsevolod Nedora, a Quantitative Developer at Citadel FlexPower, dedicated to building algorithms for quantitative trading, focusing on intraday continuous markets, backtesting, execution, and optimization.

Additionally, I enjoy exploring topics outside of trading by building fully open-sourced, research-focused, non-commercial projects, using tools and methods I find exciting. Most recent projects include:

• Energy Price Forecasting: I maintain an active MLOps pipeline designed to forecast fundamentals related to European power grid. This pipeline features automated data collection through APIs (e.g., SMARD, openmeteo) and web scraping (epexspot), continuous integration/deployment via GitHub Actions, and planned scalability across European energy markets.

• Open Event Intel: I actively build a "live knowledge system" for the European energy sector Open Event Intel. The aim is to leverage advanced NLP and generative AI techniques to automate the collection and extraction of industry news, creating personalized, concise updates tailored to specific informational needs.

Before my move to the energy industry, I pursued an academic career in theoretical astrophysics at the Max Planck Institute for Gravitational Physics. My (mostly numerical) research specialized in multimessenger and high-energy astrophysics, focusing on software development, data analysis, and modeling. Notable contributions include:

• Numerical Simulations: I built and released the (C++ core + Python interface) gamma-ray burst afterglow modeling tool PyBlastAfterglow currently used by the high energy astrophsycis team at the University of Potsdam.
• Bid Data ETL I buit a post-processing pipeline bns-ppr-tools for outputs of general relativistic hydrodynamic simulations (30+ TB of data processed).
• Data Modeling: Statistical analysis and modeling of neutron star merger ejecta properties, widely adopted in the astrophysics community (model, dataset).
• Machine Learning: Designed a conditional variational autoencoder (CVAE) for efficient inference on synthetic astrophysical data.

More details about my academic work can be found on my arXiv and INSPIRE profiles.

I'm passionate about exploring new technologies and impactful solutions, and I'm always open to collaboration. If you're interested in innovative forecasting approaches, NLP-driven information management, or interdisciplinary projects in energy and data science, please reach out. I look forward to connecting and exchanging ideas!