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last update : 07/06/2019

Algorithmic Trading Workshop



Session 1:

How Machine Learning Adds Value to the Investment Process

Presenter: Ernie Chan, QTS Capital, Management, LLC

♦ Pros and cons of applying ML to investing 
♦ Importance of features selection 
♦ Subtleties of applying ML to investing 
♦ Meta-labelling as the conservative choice 
♦ Where to start? 


Ernie Chan is the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor. Ernie has worked for various investment banks (Morgan Stanley, Credit Suisse, Maple) and hedge funds (Mapleridge, Millennium Partners, MANE) since 1997. He received his Ph.D. in physics from Cornell University and was a member of IBM’s Human Language Technologies group before joining the financial industry. He is the author of “Quantitative Trading: How to Build Your Own Algorithmic Trading Business”, “Algorithmic Trading: Winning Strategies and Their Rationale”, and “Machine Trading: Deploying Computer Algorithms to Conquer the Markets”.


Session 2:

Applying Machine Learning to Algorithmic Trading Strategies

Presenter: Douglas Castilho, University of São Paulo and OptiRisk Systems

The objective of this session is to show you how to create databases from your own strategies and adapt them for Machine Learning Methods. Besides presenting different generic algorithmic trading strategies, some machine learning methods are also explained with a discussion about different kinds of validation processes. This section comprises 2 parts; each 1.5 hours duration.


Douglas specialises in Computer Science and Computational Mathematics at the University of São Paulo (Brazil), is an associate of OptiRisk Systems and a visiting researcher in University of Porto (Portugal). He obtained his MSc degree in Computer Science in 2014 from the Federal University of Minas Gerais – Brazil. He is researcher and professor at Federal Institute of Education, Science and Technology of South of Minas Gerais. During his career, he was awarded with Outstanding Student prize in 2012, granted by the Brazilian Society of Computing. He has been working with machine learning and financial market since 2010. Recently, he participated as finalist in Data Science Game 2017, an international competition for students held in Paris, France. He researches in areas of Computational Intelligence, Online Social Networks, Deep Learning and Financial Market, with emphasis on High Frequency Trading and Algotrading Improvement Techniques.


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