- 24 June 2019, London
- 24 June 2019, London
- 27 June 2019, London
24 June 2019, London
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.
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”.
Workshop ticket price
- Super Early Bird until 15 April 2019 – £300+VAT
- Early Bird until 10 May 2019 – £400+VAT
- Standard Price – £550+VAT
- Buy Ticket
24 June 2019, London
Behavioral Finance is widely discussed. Nobel prizes are awarded. But there is more talk, than walk. The knowing-doing gap persists. Bridging that gap is at the core of developing a comparative advantage. Innovation-based specialization requires critical and creative thinking along the value chain of a professional investment process. It gets unavoidably personal. Our mental immune system, combined with organisational persistence, lead to a slow adoption of behavioral insights of how to benefit from this alphasource. Participants will learn where to find and how to exploit behavioral alpha on themselves and their teams.
Innovation-based specialization requires critical and creative thinking along the value chain of a professional investment process. It gets unavoidably personal. Our mental immune system, combined with organizational persistence, lead to a slow adoption of behavioral insights of how to benefit from this alpha source.
# Gaining a comparative advantage as a professional investor by making most evidence-based investment decisions
♦ Understanding the relevance of Behavioral Alpha
♦ Where to find it
♦ How to exploit it
Morning Session I: Introduction to Behavioral Alpha
♦ Mental Immune System
♦ Organizational Persistence
♦ Behavior Gap Penalty
♦ Knowing-Doing Gap in Behavioral Finance
♦ Behavioral Alpha & Comparative Advantage
Morning Session II: How to Source Behavioral Alpha
♦ Decisions Under Uncertainty
♦ Ambiguity & Complexity Tolerance
♦ Creative and Critical Thinking
♦ Simple Rules
♦ Best Practices of “Sourcing Behavioral Alpha”
Afternoon Session I: Case Study
Participants will study their own investment teams/committees and explore how to optimize it by applying previously introduced techniques and lessons learned.
Afternoon Session II: Review Your Findings
Moderated by the lecturer, participant share their findings and reflect together on eventual further improvements of their own or other findings.
Markus Schuller is the founder and managing partner of Panthera Solutions. As Investment Decision Architects ™, Panthera optimizes the choice architecture of professional investors through applied behavioral finance methods. Empowering the decision makers towards comparative advantages in capital markets remains the ultimate goal. As adjunct professor, Markus teaches courses like “Adaptive Risk Management”, “Investment Banking” and “Asset Allocation for Practitioners” at renowned Master in Finance programs of the EDHEC Business School and the International University of Monaco. Markus publishes in academic top journals (i.e. Journal of Portfolio Management, 2018), writes articles for professional journals (i.e. CFA Institute, OECD Insights, etc.) and holds keynotes at international investment conferences. As an investment banker, adjunct professor and author, Markus looks back at 18 rewarding years of trading, structuring, and managing standard and alternative investment products. Prior to founding Panthera Solutions, he worked in executive roles for a long/short equity hedge fund for which he developed the trading algorithm. Markus started his career working as equity trader, derivatives trader and macro analyst for different banks.
27 June 2019, London
Financial companies have widely adopted big data tools to uncover hidden patterns or unknown correlations that can help them make better predictions and thus more-informed business decisions. However, if not supported by rigorous methodological analyses, data mining can be more than misleading. The testing of systematic trading rules is usually done through backtesting and prone to spurious outperformance as a result of the data-mining bias. Multiple rules tested concurrently over the same history and optional stopping rules, along with some others, are commonly known as p-hacking.
Artificial Intelligence turned into a buzzword, an empty phrase. Overhyped, while underdelivered. Despite failed expectation management, there is value to find in AI for investment management. Experience a state-of-the-art workshop that provides a coherent framework and a useful set of tools to apply artificial intelligence with two widely-used programming languages: R and Python.
♦ how to critically evaluate systematic trading strategies with the help of Monte Carlo simulations and artificial trading rules
♦ how to use some of the most well-known methods in machine learning to detect potential anomalies in empirical data
♦ how to benefit from a mixed cognitive architecture
Programming Languages Used: Python and R
♦ Evaluate systematic trading strategies with Monte Carlo simulations and artificial trading rules in order to minimize the number of false positive trading strategies.
♦ Study some of the most well-known methods in machine learning (i.e. random forest and neural networks) and learn how to use these tools to detect potential anomalies in empirical data.
♦ Develop your own automated trading strategies and augment them with artificial intelligence, notably genetic algorithms, to build smarter strategies.
♦ Introduce the concept of mixed cognitive architecture, which provides a framework on how human and artificial systems can work together, in order to show how one can yield intelligent behavior in a diversity of complex environments.
Dr. Gregory Gadzinski is Senior Consultant at Panthera Solutions and also a full-time professor of Finance and Economics at the International University of Monaco, teaching a wide range of courses in the DBA, MBA and MFIN programs. He was previously an Assistant Professor of Economics at the Chair for International Economics in Cologne, Germany. Dr. Gadzinski was also a full-time researcher at the Hedge Fund Research Institute in Monaco. His consultancy experience includes mandates at ALPSTAR Management, a multi-strategy hedge fund and at the European Central bank, DG Research, Frankfurt, Germany. Dr. Gadzinski has a PhD from the Université de la Méditerranée, France, a postgraduate degree in Mathematical Economics and Econometrics and a “MagistèreIngénieurEconomiste” from the University Aix-Marseille II. He has published several scientific articles in prestigious journals such as the Journal of Asset Management, the Journal of Hedge Funds and Derivatives, and the Journal of Investing.