Strategic Financial Manager
Dr. Grigory Sergeenko is a Chief Executive Officer of Stronghold s.r.o. Company as well consists of the global team of quants and software developers.
Grigory has worked for nine years as a Senior Quantitative Analyst, Senior Risk manager and R&D director at various hedge funds and investment houses in Prague, Geneva, London, Russia and Denmark. He was responsible for mathematical modelling of trading strategies, in both research and development in Matlab. He participated in creating the system for testing the falling knifes approach in Matlab and hedge fund replication systems. He developed the risk management system in Matlab together with the realtime risk monitor with stochastic modelling of the market and calculating of the performance attribution ($ 120 mil under management). He as well created an original risk reporting system in Matlab (VaR, CVaR, correlation, volatility, omega ratios etc.) with automated reporting generation system. Grigory participated actively in road-shows for new hedge fund strategies. He led the developement of the original automated quantitative system with out-of-sample modelling for US equities; of the new robust valuation system for quantitative models; of the real-time risk and performance monitor with alert system and web-based systematic trading system.
Grigory has published one monograph and more than 40 papers in Russian journals covering different aspects of stochastic programming, artificial intelligence and practical aspects of the modern firm's management. In 2011 Grigory has received a patent on the "Original ERP system for the enterprise adaptive management". And in 2019 he has received "Excellence in Finance Award for the contribution to the Applied Finance Industry by FiNext" (Singapore, 2019).
Grigory is an expert in system development and process engineering of building different trading strategies with the data preprocessing, pure out of sample testing, risk management and robustness check as well as applied mathematical modelling and stochastic programming.