• Yoshi (Josh) graduated from the University of Warsaw with a Master's degree in Quantitative Finance and received the Semkow Prize for his innovative research on High-Frequency Trading (HFT). He also studied Machine Learning and Reinforcement Learning at NYU Tandon School of Engineering. In the past years he has provided technical consulting services to several quantitative hedge funds and trading firms, where he did R&D on, inter alia, the application of Deep Reinforcement Learning (DRL) to financial markets and on Multi-Agent Simulation (MAS) for market risk analysis and trading strategy design. He also has expertise in blockchain and served as a technical advisor at early blockchain startups. Prior to entering the financial industry, he developed a 3D graphics engine from scratch in the gaming industry, where he gained expertise in high-performance computing in C/C++ with x86 code optimization.


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  • Gauzilla: 3D Gaussian Splatting in Rust for WebAssembly

    with Lock-Free Multithreading


  • OvercookedGPT: Long-Horizon Reasoning & Task Planning

    with LLMs in Multi-Agent Simulations


    -> Code

  • DeFinetti: Simulating a Dynamic Liquidity

    Provision Strategy for Uniswap v3


  • Gamma Squeeze & Short Squeeze Agent-Based

    Simulation (ABS) in a Limit Order Book


  • "Model-Free Reinforcement Learning for

    Financial Portfolios: A Brief Survey"


  • "A Flash Crash Simulator:

    Analyzing HFT's Impact on Market Quality"


  • Trend-Following Buyback Achievers


  • "A Multithreaded FX Algo Backtesting System"