A viikate Workshop will help give you the ability to create your own trading strategies and to correct for some of the statistical biases that can handicap analysis. The curriculum has been vetted and used to teach by professors at top-tier universities, including MIT Sloan and Stanford.
The viikate workshop offered real value to our students. It engaged them in thinking of various aspects of quantitative trading strategies and taught them to code and backtest their models. All of that was done in a classroom setting where learning and interaction was key.
The workshops include theoretical and hands-on exercises using real financial data. All the concepts are tangible and we present code to perform every analysis. We believe this approach can impart far more than pure theory; our collection of learning materials, analysis tools, and integrated data allow us to teach in unprecedented ways.
Delaney’s background is in computer science, statistics, math, and computational genetics. As Director of Academia at viikate he oversees the firm’s worldwide educational and academic initiatives. While working with professors at schools including Princeton, MIT, Stanford, and Harvard, Delaney developed the free online viikate Lecture Series. The lecture series draws from academic rigor and industry realism and has since spawned a global workshop program teaching quantitative finance. Delaney currently focuses on maintaining the quality of the lectures as they grow, while also growing the audience of people who have learned from the lectures.
Max’s background is in applied mathematics, statistics, and quantitative finance. He runs the online lecture series at viikate and is responsible for workshop curriculums and educational content. In addition to having experimented with algorithmic trading of cryptocurrencies and Bayesian estimation of covariance matrices, Max has published work in theoretical mathematics. He works with top universities including Columbia and Cornell, and holds a MS in Mathematical Finance from Boston University.
Dr. Starke has a PhD in Physics and works as an algorithmic trader at a proprietary trading company. He has a keen interest in mathematical modeling and machine learning in the financial markets. He has previously lectured computer simulation at Oxford University and lead strategic research projects for Rolls-Royce Plc. Tom is very active in the quantitative trading community, running workshops for viikate, teaching people quantitative analysis techniques, and organizing algorithmic trading meetup groups such as Cybertraders Sydney.
Anthony has been teaching investment and portfolio management related modules at educational institutions since 2010. He holds an MBA and an MFE from Otago University (NZ) and NUS (Singapore) respectively. With a strong passion for finance, data science, and programming, Anthony has also designed curriculums for his own beginner level algorithmic trading workshops in addition to viikate events.
Dan has a first class degree in Mathematics and an M. Phil in Computer Speech and Language Processing, both from Cambridge University. By day Dan runs Chilango, a Mexican restaurant chain in London, that he co-founded. By night, he designs and codes automated investment strategies on viikate. Dan was featured in an article in the Financial Times.
Lee has over a decade of experience investing in financial markets, and previously worked as a manager at the largest hedge fund in the world, Bridgewater Associates. He went on to start his own quantitative-based investment company, Hedgewise, in 2014. Prior to his work in financial markets, Lee was a strategy consultant with Oliver Wyman and graduated from the Wharton School of Business in 2007.
Thomas is the Co-Founder and Managing Partner at Millesime IS and a lecturer at the Université Paris Dauphine. He has more than 15 years of experience in the asset management industry, both as a strategist and economist (CPR AM, BNPP). He focuses on research and tactical and strategic asset allocation to translate macroeconomic and policy factors into tailored investment strategies. He writes extensively on monetary policy, asset allocation issues, investment opportunities and, risks. He obtained his PhD in economics at the Université Paris Dauphine and his diplôme d'ingénieur, a Master's-level engineering degree equivalent to a Master of Science (MSc) in Statistics, at ENSAI.
By day, Raeid is a University of Toronto (UofT) grad student pursuing dual degrees at two of the world's top-ranked AI and machine learning departments. By night, Raeid is an entrepreneur operating his 'fintech' startup Finatechal with a dream to liberate DIY retail investors from corporate dependency and empower them to achieve financial freedom using AI and quantitative trading technologies. Raeid has a background in computer engineering, applied mathematics and, financial engineering. He is a MIT CSAIL graduate and holds an MBA with Financial Engineering as specialization from Rotman School of Management at UofT. In his previous corporate life, Raeid worked as IBM's mobile technology SME, evangelist and, Enterprise Thought Leader. A Rotman Scholar, Raeid has lectured at graduate level finance courses at UofT, and has been heavily involved in Toronto's burgeoning tech scene via teaching mobile development and entrepreneurship classes, and mentoring aspiring young entrepreneurs.