Budapest, Budapest, Hungary
Quantitative Portfolio Manager with fifteen years of experience building systematic, factor-based investment strategies. I specialize in the full research lifecycle: developing non-linear alpha signals through machine learning, integrating these into robust factor models, and optimizing multi-factor combinations and tactical timing. Having operated on both the sell and buy sides since 2010, I bridge the gap between quantitative research and production-hardened trading infrastructure. Beyond direct portfolio management, I have substantial experience as a CEO leading research teams. I oversee the entire lifecycle of trading systems, from high-performance software engineering to large-scale data processing, using high-performance engineering in Rust and Python. I am focused on building predictive models that generate persistent, risk-adjusted alpha within disciplined institutional frameworks.
Portfolio Management: Direct a systematic, mid-frequency equity strategy; maintain an annualized Sharpe ratio of 1.5 through disciplined risk-adjusted execution and rigorous drawdown management within institutional mandates. Alpha Research: Lead the development of equity strategies, focusing on the research and implementation of factor timing enhanced by non-linear machine learning models. Infrastructure & Platform: Oversee the firm’s trading platform in Python; ensure the robustness of the quantitative research environment and the seamless deployment of systematic models to production. Business Leadership: Direct counterparty and prime brokerage relationships, capital allocation, and internal compliance; mentor quantitative research talent to drive systematic portfolio construction and strategy growth.
Alpha Research: Developed systematic equity trading strategies; researched and optimized factor modeling, factor combination, and factor timing frameworks, integrating non-linear machine learning models to capture predictive alpha. Model Implementation: Designed and deployed systematic models using Python; managed the end-to-end pipeline from clean tick data to systematic PnL attribution for mid-frequency execution. Data Analysis: Engineered high-volume data pipelines for equity time-series; extracted and validated predictive features for systematic signal generation and model training. Risk Management: Architected automated risk-monitoring and portfolio-level hedging tools; enforced disciplined backtesting and out-of-sample validation protocols to prevent over-fitting and validate signal performance.
Developed and delivered a comprehensive graduate-level curriculum on Machine Learning for Finance. Translated complex theoretical concepts into practical applications for algorithmic trading and predictive model construction.
Designed and instructed advanced coursework in "Machine Learning Concepts" and "Advanced Methods in Machine Learning". Led seminar discussions on the practical deployment of predictive models, covering model validation, feature engineering, complexity control etc.
Quantitative Research: Conducted quantitative research for Fixed Income Electronic Trading, focusing on market-making algorithms and execution strategies. Model Development: Contributed to the development of pricing and hedging models; improved liquidity provision and reduced adverse selection for the trading desk. Infrastructure: Developed and optimized KDB+ infrastructure for real-time execution; improved data availability and model performance for critical trading desks.