Quantopian hosted a sold out QuantCon 2017
in New York City on April 28-30th.
The conference featured expert workshops and talks on
algorithmic trading, quantitative finance, and machine learning.
Did you miss it? Check out our QuantCon Replay.
The QuantCon Replay will give you exclusive access
to videos and presentations from QuantCon NYC
thru October 31st 2017.
Michael Kearns is a professor at the University of Pennsylvania for their Computer and Information Science Department. Hes also a founding director of Penn’s Warren Center for Network and Data Sciences.
His research interests include topics in machine learning, artificial intelligence, algorithmic game theory, computational social science, and quantitative trading.
He has consulted and worked widely within the technology and finance industries, and is Chief Scientist at MANA Partners, a quantitative hedge fund and trading technology firm based in New York City.
He is also a scientific advisor to Quantopian.
For more information, visit: www.cis.upenn.edu/~mkearns.
Marcos López de Prado is Senior Managing Director at Guggenheim Partners, where he manages several multibillion-dollar internal funds. Over the past 18 years, his work has combined advanced mathematics and supercomputing technologies to deliver billions of dollars in net profits for his investors and firms. A proponent of research by collaboration, Marcos has co-authored with over 30 leading academics, and serves on the editorial board of 5 academic journals, including the Journal of Portfolio Management (IIJ).
Since the year 2010, Marcos has also been a Research Fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy’s Office of Science). For the past 6 years he has lectured at Cornell University, where he currently teaches a graduate course in Financial Big Data and Machine Learning at the Operations Research Department.
For more information, visit www.QuantResearch.org
.
"Trading without Regret"
by Dr. Michael Kearns, Professor at the Computer and Information Science Department at the University of Pennsylvania
No-regret learning is a collection of tools designed to give provable performance
guarantees in the absence of any statistical or other assumptions on the data (!),
and thus stands in stark contrast to most classical modeling approaches.
With origins stretching back to the 1950s, the field has yielded a rich body of algorithms and analyses that covers problems ranging from forecasting
from expert advice to online convex optimization.
Dr. Kearns will survey the field, with special emphasis on applications to quantitative finance problems, including portfolio construction and inventory risk.
"Building Diversified Portfolios that Outperform Out-of-Sample"
by Dr. Marcos López de Prado, Senior Managing Director at Guggenheim Partners
Hierarchical Risk Parity (HRP) portfolios address three major concerns of quadratic optimizers in general and Markowitz’s CLA in particular: Instability, concentration and underperformance. HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix, an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out-of-sample variance than CLA, even though minimum-variance is CLA’s optimization objective. HRP also produces less risky portfolios out-of-sample compared to traditional risk parity methods.
Read the corresponding white paper here.
"Herding Robotic Cats: Constructing a Single Portfolio from Hundreds of Thousands of Autonomous Strategies" by Jonathan Larkin, Chief Investment Office at Quantopian
Many multi-strategy and multi-manager investment managers are faced with a common problem: how to implement a single portfolio subject to a single investment mandate when the collection of underlying strategies are autonomous, private, and independent. This talk demonstrates a framework to solve this problem.
"On the Bayesian Interpretation of Black–Litterman"
by Dr. Gordon Ritter, Senior Portfolio Manager at GSA Capital
We will present the most general model of the type considered by Black and Litterman (1991) after fully clarifying the duality between Black–Litterman optimization and Bayesian regression.
Our generalization is itself a special case of a Bayesian network or graphical model. As an example, we will work out in full detail the treatment of views on factor risk premia in the context of APT.
We will also consider a more speculative example in which the portfolio manager specifies a view on realized volatility by trading a variance swap.
In this paper, we showcase how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with our own NLP algorithms, we study a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. Our NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors.
"Trading Strategies That Are Designed Not Fitted"
by Robert Carver, Independent Systematic Futures Trader, Writer, and Research Consultant
Engineers design stuff. Why do Quants prefer to fit? In this talk, Robert will explain what designing a trading system actually involves, explore why designing might be better than fitting, and introduce some of the tools you could use. He will also take you through the design process for an example trading strategy.
Finally, he will discuss how we can have the best of both worlds: strategies that are well designed and also fitted to the data.
"From Trading Strategy to Becoming an Industry Professional – How to Break into the Investment Management Business"
by Andreas Clenow, Chief Investment Officer for ACIES Asset Management
You have created a great trading strategy, backtested, traded it and now you want to take it to the next level. You may find that developing the strategy was just the first of many difficult steps.
With the increased availability of low cost, high quality quant modelling platforms, the field is much more open than it once was. The interest for algorithmic trading his higher than ever and anyone has the potential develop a great trading model.
But having a great trading model is not enough. The work is not done yet.
This presentation will discuss turning your algorithmic trading strategy into a business or a great job, and becoming a professional trader. We’re going to talk about what it takes to move to the next level and where the common pitfalls lay. What kind of strategies are marketable are which are not. The pros and cons of trading your own money and how to go about finding external capital and gaining traction in the business.
Are you ready to take the step?
"The Changing Face of Market Microstructure"
by Kerr Hatrick, Executive Director at Morgan Stanley's Electronic Trading Strategist Group
Transaction costs can make or break the cleverest of quantitative trading strategies. These costs are determined by market microstructure. Microstructural change affects our expectations of best execution, should alter algorithm selection, and is central to the price formation process itself.
In this presentation we examine the interdependence of some key dimensions of market microstructure:
- The Volume Curve
- The Order book, over the trading day
- Intraday patterns in spreads for different liquidity classes
- Fragmentation, Speed, and the amount executed in auctions
- Quantifications of how systematic a market is
- Event-based analysis of intraday trading patterns
We present our results in a new series of big-data animations. These animations are enriched by a series of granger causality studies which help disentangle the relationships between the features we visualize. Our findings will examine closely some received wisdom about systematic trading, and, we hope, will provide a unique and relevant picture of microstructural regime change.
"Snake Oil, Swamp Land, and Factor-Based Investing"
by Gary Antonacci, author of Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk
BlackRock forecasts smart beta investing oriented toward size, value, quality, momentum, and low volatility to reach $1 trillion by 2020 and $2.4 trillion by 2025. Gary’s talk will show that this growth may not be justified due to these factors' lack of robustness, consistency, persistence, intuitiveness, and investability. Gary will also show that the success attributed to these factors would be better directed toward macro momentum and the short interest ratio.
Commonality in idiosyncratic volatility cannot be completely explained by time-varying volatility. After removing the effects of time-varying volatility, idiosyncratic volatility innovations are still positively correlated. This result suggests correlated volatility shocks contribute to the comovement in idiosyncratic volatility.
Motivated by this fact, we propose the Dynamic Factor Correlation (DFC) model, which fits the data well and captures the cross-sectional correlations in idiosyncratic volatility innovations. We decompose the common factor in idiosyncratic volatility (CIV) of Herskovic et al. (2016) into the volatility innovation factor (VIN) and time-varying volatility factor (TVV). Whereas VIN is associated with strong variation in average returns, TVV is only weakly priced in the cross section
A strategy that takes a long position in the portfolio with the lowest VIN and TVV betas, and a short position in the portfolio with the highest VIN and TVV betas earns average returns of 8.0% per year.
"Quantum Hierarchical Risk Parity - A Quantum-Inspired Approach to Portfolio Risk Minimization"
by Maxwell Rounds, Finance Specialist, 1QBit
Maxwell will present the methodologies and results behind the algorithm that has been developed by 1QBit, named Quantum Hierarchical Risk Parity, or QHRP.
This is an extension of the work done by Marcos Lopez de Prado on
Hierarchical Risk Parity in his paper "Building Diversified Portfolios that Outperform Out-of-Sample."
QHRP tackles the problem of minimizing the risk of a portfolio of assets using a quantum-inspired approach. Although the ideas surrounding this go back to Markowitz’s mean-variance portfolio optimization of 1952’s Portfolio Selection, we have applied recent quantum-ready machine learning tools to the problem to demonstrate strong performance in terms of a variety of risk measures and lower susceptibility to inaccuracies in the input data.
The quantum-ready approach to portfolio optimization is based on
an optimization problem that can be solved using a quantum annealer. The algorithm utilizes a hierarchical clustering tree that is based on the covariance matrix of the asset returns. The results of real market data used to benchmark this approach against other common portfolio optimization methods will be shared in this presentation.
View the White Paper: https://bit.ly/2k5xTxW.
""Bayesian Deep Learning: Dealing with Uncertainty and Non-Stationarity"
by Dr. Thomas Wiecki, Director of Data Science at Quantopian
Deep Learning continues to build out its dominance over other machine learning approaches on several challenging tasks including image, hand-writing, and speech recognition, image synthesis, as well as playing board and computer games exceeding human expert abilities.
This has generated a lot of interest in the quant finance community to try and mirror Deep Learning's success in the domain of algorithmic trading. Unfortunately, algorithmic trading poses a unique set of challenges. Specifically, the risk (i.e. uncertainty) of certain trading decisions as well as the fact that market behavior changes over time (i.e. non-stationarity) is not handled well by deep learning.
In this talk, I will show how we can embed Deep Learning in the Probabilistic Programming framework PyMC3 and elegantly solve these issues. Expressing neural networks as a Bayesian model naturally instills uncertainty in its predictions. This talk is focused on practitioners and will be introductory and hands-on with many code examples.
"Finding a DeLorean: Building an Investment Strategy that Explains Asset Returns in the Past and Makes Money in the Future"
by Christopher Covington, Portfolio Manager and Researcher at AJO Partners
Academics and practitioners alike often repeat several missteps in
factor and strategy research that render their work unprofitable out of sample.
This talk will discuss several of these missteps including the assessment of implementation costs and back test over-fitting. The hope is to provide the audience with experience and tools to avoid these pitfalls and produce work that not only explains the past, but also forecasts the future.
"Deep Q-Learning for Trading"
by Dr. Tucker Balch, Professor of Interactive Computing at Georgia Tech and Chief Scientist and Co-founder of Lucena Research
Reinforcement Learning (RL) has been around for a long time, but it has not attracted much attention over the last decade. Until, that is, a group of Google researchers showed how RL can be used to train a computer to play video games at far above human capabilities.
Besides video games, the RL problem is also well aligned to solve trading problems as well (e.g., work by Dr. Michael Kearns). In this talk, Tucker will provide a gentle introduction to Q-Learning, one of the leading RL methods.
He will also show how Q-Learning can be integrated with artificial neural network learners and how such a system can be used to learn and execute a trading strategy. This is joint work with David Byrd at Georgia Tech.
"Using Partial Correlations for Increasing Diversity of Mean-variance Portfolio"
by Dr. Alec Schmidt, Lead Research Scientist at Kensho
It is found that partial correlations between 12 major US equity sector ETFs conditioned on the state of economy (mimicked here by the S&P 500 index) are significantly lower than the Pearson’s correlations. The Markowitz mean-variance portfolio theory is modified in terms of partial covariance. The maximum Sharpe portfolios formed by 12 equity sector ETFs in 2007 – 2015 are examined. With the exclusion of the bear market of 2008, the partial correlation based portfolios (PaCP) are much more diversified than the Pearson’s correlation based portfolios (PeCP).
Out-of-sample performance of the maximum Sharpe PeCP and PaCP, and the equal-weight portfolio (EWP) are compared. The results are very sensitive to the model parameters (portfolio calibration window and frequency of portfolio rebalancing). While the PeCP weights change significantly from month to month,
the PaCP weights outside the bear market effects are almost constant. PaCP outperforms both EWP and PeCP when the 36-month calibration window and one-month rebalancing frequency are used. We conclude that partial covariance is a promising concept for constructing optimal portfolios.
In this presentation, Saeed will discuss his general approach to modelling trading strategies for FX cash and vol markets, and how it can differ from other asset classes. He will give some practical examples, in Python, of a basic FX trading trend following strategy and examples of analysis on FX spot and vol markets, using the open source library finmarketpy, to assess the impact of major economic events like FOMC.
Lastly, he will discuss some tips and tricks for speeding up
your Python code when backtesting.
Bayesian Global Optimization: Using Optimal Learning to Tune Trading Models"
by Scott Clark, Co-founder and CEO of SigOpt, Inc.
Many trading strategies require fine tuning of various configuration parameters to reach their full potential. We'll show how Bayesian Global Optimization can be used as an efficient way to optimize model parameters, especially when evaluating or backtesting different configurations is time consuming or expensive.
We'll also show several examples of how these techniques can help unlock the potential of these sophisticated models faster, better, and cheaper than standard techniques - including some joint work with the Quantopian team.
"Algorithmic Trading Opportunities in Asia -
Regulations, Technology, Competitive Landscape, Opportunities"
by Rajib Ranjan Borah, Co-founder at iRage
The high frequency and algorithmic trading landscape in America is hugely competitive with a high number of firms competing for similar profit opportunities. Furthermore, there is continuous talk of new regulations against HFT & algorithmic trading in Europe and in the US.
In light of the both the challenges mentioned above, it becomes prudent for firms that have built their expertise to look at nascent markets in Asia. Using similar effort and expertise in Asia might generate higher profits and also provide early mover advantage to some of the new markets.
However, the road is full of unique obstacles - and an inside insight to the challenges and possibilities is critical. This talk hopes to clear some of these confusions and throw more light to the algorithmic trading business expansion possibilities in Asia
One by-product of the global data explosion is the rise of alternative data – information that offers trading insight despite being non-financial in nature, such as Internet of Things data or email receipt data.
Alternative data sources are potentially game changing, making them a powerful tool for investors who have the ability to access and analyze them.
Raymond will present several case studies that illustrate what it takes to find new data sources, how to access them, and the many other challenges that investors must overcome before they can harness the full potential of this new alpha source.
"A Framework-Based Approach to Building Quantitative Trading Systems"
by Dr. Michael Halls-Moore, Founder of QuantStart.com
Contrary to popular wisdom the difference between a retail quant trader and a professional portfolio manager is not in "having better trade entry and exit rules". Rather it is the difference in how each approaches the concepts of portfolio optimisation and risk management.
Both of these topics are synonymous with heavy math, which can be off-putting for beginner retail systematic traders. Hence, it can be extremely daunting for those without institutional experience to know how to turn a set of trading rules into a robust portfolio and risk management system.
In this talk, Mike will discuss how to take a typical retail quant strategy and place it in a professional quantitative trading framework, with proper position sizing and risk assessment, without resorting to pages of formulas or the need to have a PhD in statistics!
Recent headlines about high frequency trading mention decreased profits, heightened regulatory scrutiny, and a surge in competition. To what extent are smaller firms affected by these developments?
In this talk, we discuss some current trends in automated and high frequency trading, from the latest infrastructure developments to the challenges that funds are facing amid stiff competition. By analyzing job applicant data over the years, we assess the changing skillset required to work in HFT.
The discussion examines unique challenges and insights from the perspective of an HFT hedge fund.
Aaron Fifield, from Chat with Traders, Interviews Dr. Xiao Qiao, Researcher at SummerHaven Investment Management
Aaron will dive into Xiao's processes, thoughts, and ideas as a research analyst. More specifically, he will lead with questions which hit upon Xiao's research project with trading heavyweight, Blair Hull, how they go about testing and implementing new ideas, problem solve, and their focus. They will also discuss considerations for how academics and practitioners can better work together.
"Explore Financial Data in Immersive Reality" by Bob Levy, CEO of Virtual Cove Inc. & John Horcher, Co-Founder of Virtual Cove Inc.
In this talk, we’ll explore exciting new possibilities rooted in evolutionary neuroscience for mastering complexity. Attendees will have the opportunity to see financial data hands-on with a virtual or mixed reality headset, opening your senses to new ways for rapidly understanding complex data. Drawing on collaboration with MIT faculty and leading financial institutions, we’ll share a framework quantifying why this approach accelerates time-to-insight by several orders of magnitude.
"The Futures Are in Your Hands: Building Better Strategies With Futures" by Jamie McCorriston, Analyst at Quantopian
Quantopian has expanded it's asset universe to include futures. The expansion includes new data and tools that make it easier to research and develop quantitative strategies using futures — with or without equities in the same algorithm.
In this talk, Jamie will give a walk through the process of researching and backtesting a futures strategy, aided by "continuous futures", a new tool that saves us from having to think of futures on a transient contract-to-contract basis.
"A Workflow for Rapidly Testing Hypotheses" by Delaney Mackenzie, Director of Academia at Quantopian
Rapidly testing hypotheses is the core to any quantitative workflow. Forecasting returns accurately requires models, and evaluating those models requires testing the underlying hypotheses. The faster you can test hypotheses the more models you can evaluate and the faster you’ll build up sufficient models to trade. We’ll go through a generalized workflow for testing hypotheses, and show specific examples of the work being done on the Quantopian platform.
"From Insufficient Economic data to Economic Big Data – How Trade Data is redefining Reliability in Economic Indicators for Markets" by Tony Nash, Chief Economist and CEO at Complete Intelligence & Mayookh Lad, Head of Analytics at Complete Intelligence
Over the last 10 years, the world of economics has been playing a catching up game and many economists have been struggling to explain their theories. The world has adopted technology in nearly every aspect of life, from phones to cars; however, good, reliable and quality data in economics is still elusive.
There is over reliance on macroeconomic principles in comparison to the quality of data available. Macro-economic figures move markets, only to get revised one, two or three times in the following months. Some fields of economic study are exceptions, such as analysing trade data. Trade data, with the support of technology, has become readily available and can now be analysed in depth, providing actual numbers indicating the health and state of economies.
Trade data, which is export and import information of all the goods and services from one country to another, can be seen as an inseparable marker of real economic activity. It can be used to predict various market indicators exhibiting high correlations, from currencies to commodities to equities to macroeconomic data, with varying degree of certainties. Trade data, at an in-depth level, acts like a compilation of millions of real life mathematical functions.
This presentation explores this new economic area of trade data as a quantitative tool, its intense big data analysis and its applications in trading markets.
What should we worry about? Will we have superhuman AI within our lifetimes? What are the most recent advances in machine learning? What should you follow to keep up to date? In this talk we will discuss the current trends in AI - legislation, societal impact, finance, and research - without hyperbole and with negative spin.
Recordings and slide deck presentations from the majority of talks from QuantCon are available now for $199.
Purchase of this ticket will guarantee you exclusive access
to the recordings and presentations until October 31st, 2017.
After this time period, we will release some of the presentations piecemeal throughout the end of 2017 and thru 2018.
All in-person, live stream, and replay attendees will receive videos and presentations from all shareable presentations on April 29th soon after the event. *All of the recordings will be provided except for those taking place in Gotham and Duffy/Columbia rooms.
Times, talk topics, and recordings are subject to change.
Welcome to QuantCon by John "Fawce" Fawcett, CEO and Founder of Quantopian
Morning Keynote: "Trading without Regret" by Dr. Michael Kearns, Professor at the Computer and Information Science Department at the University of Pennsylvania
"Text Mining Unstructured Corporate Filing Data" by Yin Luo, Vice Chairman at Wolfe Research, LLC
"Identifying Credibility of News" by Dr. Sameena Shah, Director of Research for Thomson Reuters
"Correlated Volatility Shocks" by Dr. Xiao Qiao, Researcher at SummerHaven Investment Management
"Recent Developments in High Frequency Trading" by Christina Qi, Partner at Domeyard LP
"Finding a DeLorean: Building an Investment Strategy that Explains Asset Returns in the Past and Makes Money in the Future"
by Christopher Covington, Portfolio Manager and Researcher at AJO Partners
"On the Bayesian Interpretation of Black–Litterman" by Dr. Gordon Ritter, Senior Portfolio Manager at GSA Capital
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independent Systematic Futures Trader, Writer, and Research Consultant
"Bayesian Deep Learning: Dealing with Uncertainty and Non-Stationarity" by Dr. Thomas Wiecki, Director of Data Science at Quantopian
"A Workflow for Rapidly Testing Hypotheses" by Delaney Mackenzie, Director of Academia at Quantopian
"From Trading Strategy to Becoming an Industry Professional – How to Break into the Investment Management Business"
by Andreas Clenow, Chief Investment Officer for ACIES Asset Management
"Enhancing Statistical Significance of Backtests" by Dr. Ernest Chan, Managing Member of QTS Capital Management, LLC.
"Snake Oil, Swamp Land, and Factor-Based Investing" by Gary Antonacci, author of Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk
"Quantum Hierarchical Risk Parity - A Quantum-Inspired Approach to Portfolio Risk Minimization" by Maxwell Rounds, Finance Specialist, 1QBit
"The Futures Are in Your Hands: Building Better Strategies With Futures" by Jamie McCorriston, Analyst at Quantopian
"A Framework-Based Approach to Building Quantitative Trading Systems" by Dr. Michael Halls-Moore, Founder of QuantStart.com
"Smart Sigma: Using Trading Flows as a Risk Factor" by Dr. Michael Steliaros, Global Head of Scientific Implementation & Head of International Portfolio Products Distribution, at Bank of America Merrill Lynch
"Momentum Investing: Simple, But Not Easy" by Dr. Wes Gray, Founder of Alpha Architect
"Deep Q-Learning for Trading" by Dr. Tucker Balch, Professor of Interactive Computing at Georgia Tech and Chief Scientist and Co-founder of Lucena Research
"Making the Grade: A Look Inside the Algorithm Evaluation Process" by Dr. Jess Stauth, Vice President of Quant Strategy at Quantopian
"Don't Lose Your Shirt Trading Mean-Reversion" by Edith Mandel, Principal at Greenwich Street Advisors, LLC & Adjunct Professor at NYU Tandon School of Engineering
"Herding Robotic Cats: Constructing a Single Portfolio from Hundreds of Thousands of Autonomous Strategies" by Jonathan Larkin, Chief Investment Officer at Quantopian
"Quantitative Momentum: Building a Better Momentum-Based Stock-Selection Algorithm" by Dr. Jack Vogel, Co-CIO, CFO, and a Managing Member of Alpha Architect
"Machine Learning Approaches to Regime-aware Portfolio Management" by Michael M. Beal, Managing Member & CEO of Data Capital Management
"The Power and Perils of Alternative Data" by Raymond McTaggart, Quantitative Data Curator at Quandl
"From Insufficient Economic data to Economic Big Data – How Trade Data is redefining Reliability in Economic Indicators for Markets" by Tony Nash, Chief Economist and CEO at Complete Intelligence & Mayookh Lad, Head of Analytics at Complete Intelligence
Afternoon Keynote: "Building Diversified Portfolios that Outperform Out-of-Sample" by Dr. Marcos López de Prado, Senior Managing Director at Guggenheim Partners
Closing Remarks by John "Fawce" Fawcett, CEO and Founder of Quantopian
Yin Luo joined Wolfe Research, LLC in September 2016, as a Vice Chairman to lead the coverage of quantitative research, economics, and portfolio strategy (QES). Prior to Wolfe Research, Yin was a Managing Director and
Global Head of Quantitative Strategy
at Deutsche Bank. Yin started at Deutsche Bank in New York in
October 2009 and in seven years,
he built a world class quantitative and macro research franchise.
Yin has been ranked #1 in Institutional Investor magazine’s II-All America
equity research survey in quantitative research for the past six years (2011-2016), and top ranked in the
Accounting & Tax Policy and Portfolio Strategy sectors.
Yin holds a Bachelor of Economics degree from Renmin University of China, a MBA in Finance from University of Windsor, and a Master of Management and Professional Accounting from University of Toronto.
Jonathan directs Quantopian's investment strategy, leading the effort to identify, select, and allocate capital to investment algorithms created by Quantopian's community of more than 120,000 algorithm writers. Writers of selected algorithms share in the profits generated by their algorithms.
Larkin's prior experience spans senior roles at some of the largest multi-manager and quantitative investment firms in the world.
He was most recently a Portfolio Manager at Hudson Bay Capital Management LP. Previously, he held the roles of Portfolio Manager and Global Co-Head of Equities at BlueCrest Capital Management LP, Managing Director at Nomura Securities, and Senior Managing Director and Global Head of Equities at Millennium Management LLC.
Dr. Michael Steliaros
Global Head of Scientific Implementation and
Head of International Portfolio Products Distribution for
Bank of America Merrill Lynch
Michael Steliaros is a managing director and Global Head of the Scientific Implementation Group, as well as Head of International Portfolio Products Distribution at Bank of America Merrill Lynch.
Michael is responsible for the development and implementation of quantitative processes for portfolio products, algorithmic trading, cash equities, and Delta1 globally and the management of portfolio agency trading and ETFs internationally.
Prior to joining Bank of America Merrill Lynch, Michael spent more than a decade on the buy-side (most notably BGI and Winton) building quant stock-selection models and managing global market neutral equity portfolios. Before that, he was a Financial Econometrics lecturer at City University (CASS) Business School, where he received his PhD in Finance. He has published – amongst others– in the Financial Analysts Journal and the Journal of Asset Management on a wide range of finance topics.
He is the author of “Systematic Trading:
A unique new method for designing trading and investing systems"
(Harriman House, 2015).
Until 2013 Robert worked for AHL, a
large systematic hedge fund, and part
of the Man Group. He was responsible
for the creation of AHL's fundamental global macro strategy, and then managed the funds multi billion dollar fixed income portfolio. Prior to that
Robert worked as a research manager
for CEPR, an economics think tank, and traded exotic derivatives for Barclays investment bank.
Robert has a Bachelors degree in Economics from the University of Manchester, and a Masters degree,
also in Economics, from Birkbeck College, University of
London.
Edith Mandel
Principal at Greenwich Street Advisors,
Adjunct Professor at NYU Tandon School of Engineering
As an expert in quantitative fixed
income trading, Edith advises on
trading infrastructure build-out, electronic trading, rates modeling,
alpha research and algorithmic execution. Prior to starting her own advisory firm in 2015, Edith Mandel was the head of Fixed Income Mid-Frequency Trading at KCG (formerly GETCO). While there, she spearheaded a development of a new quantitative and systematic business within the Fixed Income group.
Edith started her career at Goldman Sachs in 1996, where she held a
number of positions in the Fixed Income division. As a Managing Director,
Edith ran a team of quantitative strategists responsible for US Rates trading. Prior to joining KCG in 2012,
Edith Mandel worked at Citadel as a Managing Director, Head of Fixed
Income Quantitative Research. There
she was instrumental to a significant revamp and expansion of the Fixed Income Asset Management business.
Dr. Ernest Chan is the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor.
He began his career as a machine learning researcher at IBM’s Human Language Technologies Group, and later joined Morgan Stanley’s Data Mining Group. He was also a quantitative researcher and proprietary trader for Credit Suisse.
Ernie is the author of “Machine Trading”, Algorithmic Trading“, and “Quantitative Trading”, all published by Wiley, and a popular financial blogger at epchan.blogspot.com.
He also teaches at the Master of Science in Predictive Analytics program at Northwestern University. He received his Ph.D. in theoretical physics from Cornell University.
Dr. Sameena Shah is a Director of Research for Thomson Reuters and heads the R&D NY team. Sameena and her team build Machine Learning,
Natural Language Processing, Artificial Intelligence based capabilities for Thomson Reuters businesses. Their
most recent success story is Reuters News Tracer, which is an autonomous machine capable of sifting through millions of tweets every day to detect and verify newsworthy events.
Sameena worked at a StatArb hedge fund in NYC and a startup in India. She
is also on the board of Cocoa Compassion, a social enterprise seeking to alleviate the social injustices in the cocoa supply chain. Sameena has a
PhD in Machine Learning from IIT Delhi that was judged as the top one for the year across India.
She holds a Master in Computer Applications and Bachelors in Electronics Engineering. Sameena has published more than 30 papers, holds multiple patents, won several awards, and is on the review panel of major CS/EE journals and conferences.
Andreas F. Clenow is the Chief Investment Officer for ACIES Asset Management, a Zurich based asset management firm with a nine figure
asset base.
Starting out as a successful IT entrepreneur in the 90s, he enjoyed a stellar career as global head of equity and commodity quant modeling at Reuters before leaving for the hedge fund world.
Having founded and managed multiple hedge funds, Mr. Clenow is now overseeing asset management and trading across all asset classes.
He is the author of best-selling and critically acclaimed book Following the Trend as well as the recently released Stocks on the Move. You can reach him via his popular website: FollowingTheTrend.com.
Gordon Ritter completed his PhD in mathematical physics at Harvard University in 2007, where his published work ranged across the fields of quantum computation, quantum field theory, differential geometry, and abstract algebra.
Gordon is currently a senior portfolio manager at GSA Capital, and leader of
a team trading a range of high-Sharpe absolute return strategies across geographies and asset classes.
Prior to joining GSA, Gordon was a Vice President of Highbridge Capital and a core member of the firm's statistical arbitrage group, which although less
than 20 people, was one of the most successful quantitative trading groups
in history, responsible for billions in profit
and trillions of dollars of trades across equities, futures and options.
Concurrently with his positions in industry, Gordon teaches courses
ranging from portfolio management to econometrics at Rutgers University, and also at Baruch College (CUNY) and New York University. He has also published several articles on modern portfolio theory in top practitioner journals.
Xiao is a researcher at SummerHaven Investment Management. He sits on the editorial board of the Journal of Portfolio Management.
His research has been featured in Forbes and Institutional Investor Journals, and won a best paper award at R/Finance 2016.
Prior to SummerHaven, Xiao built predictive models forecasting the stock market at Hull Investments, and has worked in Morgan Stanley’s wealth management division.
Xiao received a B.S. in economics from the Wharton School and a B.S. in engineering from the School of Engineering and Applied Sciences at the University of Pennsylvania, graduating summa cum laude in both. He received a Finance PhD from the University of Chicago, where he was Eugene Fama’s teaching assistant.
Gary Kazantsev is the head of the Machine Learning group at Bloomberg, leading projects at the intersection of computational linguistics and machine learning such as sentiment analysis, market impact indicators, statistical text classification, social media analytics, question answering, recommendation systems, and predictive modeling of financial markets.
He holds degrees in physics, mathematics and computer science
from Boston University.
Fawce is the founder and CEO of Quantopian, a free platform where a vibrant online community of over 120,000 members from 180 countries can create institutional-quality investment algorithms.
Previously, Fawce was a founder and CTO for Tamale Software, Inc. which was sold to Advent Software, Inc. in 2008.
He graduated Cum Laude from Harvard College with a degree in Engineering Sciences - Mechanics & Materials.
Dr. Jessica Stauth is Quantopian's Vice President of Quant Strategy. Quantopian,
a crowd-sourced quantitative
investment firm, inspires talented
people from around the world to write investment algorithms.
Jess and her team are in charge of selecting the algorithms from the Quantopian community, for our
portfolio. Quantopian offers license agreements for algorithms that fit our investment strategy, and the licensing authors are paid based on their
strategy's individual performance.
Previously she has worked as an equity quant analyst at the StarMine
Corporation and as a Director of Quant Product Strategy for Thomson Reuters prior to joining Quantopian in August of 2013.
Jess holds a PhD from UC Berkeley in Biophysics.
Dr. Kerr Hatrick joined Morgan Stanley in 2013 and runs the Morgan Stanley Electronic Trading Strategist group in Asia. His research spans both high- and low-frequency delta-one equity products; he has constructed and managed significant equity portfolios, and his software has won a number of external awards.
He is familiar with the full spectrum of equity products, having started work in risk management and the pricing of derivatives, and having worked as quantitative strategist for both Goldman Sachs and Deutsche Bank.
He received his Ph.D. from University College London.
Tucker Balch
Professor of Interactive Computing
at Georgia Tech and Chief Scientist and Co-founder of Lucena Research
Tucker Balch is a professor of interactive computing at Georgia Tech where he teaches courses in Machine Learning and Finance. In addition to his teaching on campus, more than 170,000 students have take his courses online via Coursera and Udacity.
He is Chief Scientist and co-founder of Lucena Research, an investment software firm that focuses on Machine Learning and Big Data solutions to investment problems.
Balch has published over 120 research publications related to Robotics and Machine Learning. His work has been covered by CNN, New Scientist, Institutional Investor, and the New York Times. His graduated students work at NASA/JPL, Boston Dynamics, Goldman Sachs, Morgan Stanley, Citadel, AQR,
and Yahoo! Finance.
Before his career in academia, Balch was a USAF F-15 pilot.
Max heads up the finance industry applications team at 1QBit, where he is responsible for developing methods for utilizing quantum technology in the financial services industry.
Headquartered in Vancouver, Canada, 1QBit creates developer tools and provides end-to-end expertise in quantum software to build custom applications for Fortune 500 companies across the finance, energy, and health sciences sectors.
Prior to his current role, Max served as
a Vice President in the Corporate and Institutional Banking division of BNP Paribas on the New York equity structuring desk. Before his time at
BNP Paribas, Max was at Morgan
Stanley on the New York equity structuring desk, where he designed equity exotic derivatives, derivative-based quantitative investment
strategies, and hedges for variable annuities.
Max holds a Master of Science degree
in Financial Mathematics as well as a Bachelor of Science in Mathematics
and Economics, both from Stanford University.
Gary Antonacci
Author of Dual Momentum Investing:
An Innovative Approach for Higher Returns with Lower Risk
Gary Antonacci introduced the investment world to dual momentum which combines relative strength price momentum with trend following
absolute momentum. He is recognized
as a foremost authority on the practical applications of momentum investing
and is author of the award-winning
book, "Dual Momentum Investing: An
Innovative Approach for Higher Returns with Lower Risk."
In 2012, Gary was winner of the prestigious Wagner Awards for
Advances in Active Investment Management given annually by the National Association of Active
Investment Managers (NAAIM).
He received his MBA degree from the Harvard Business School. His website is: http://optimalmomentum.com.
Michael M. Beal is the Managing
Member and CEO of Data Capital Management, LLC. A leading machine learning hedge fund based on novel
“Big Data” technologies and data feeds. Michael graduated from Harvard
College with Honors in Economics and began his investing Career with Morgan Stanley and TPG Capital.
After graduation from Harvard Business
School with distinction, Mr. Beal Co-founded a new Data Analytics Line of Business for J.P. Morgan Chase & Co
and was promoted to the new group’s Management Committee.
After leading the build of the industry’s first “Big Data” approach to Collateral and Counter-party Risk Optimization, Beal along with an experienced team of PhD data scientists and quantitative researchers, teamed with a large Geneva-based Family Office to launch Data Capital Management in 2014.
Christina Qi serves as Partner at Domeyard LP, a hedge fund focused
on high frequency trading. Christina brings experience in investment management, sales and trading, derivatives operations, and technology across Goldman Sachs, UBS Securities, Zions Bank, and MIT Lincoln Labs.
Christina is a guest lecturer for Nobel Laureate Robert Merton’s “Retirement Finance” class at MIT and was a guest lecturer for the core class “Investment Strategies” at Harvard Business School. Christina also serves on the 100
Women in Hedge Funds U.S.
Non-Profit Boards Committee.
Christina holds a Bachelor of Science degree from MIT.
Dr. Thomas Wiecki is Director of Data Science at Quantopian Inc, where he uses Probabilistic Programming and Machine Learning to solve problems in quantitative finance.
He has developed various open source projects, such as Pyfolio -- a portfolio and risk analysis library, and PyMC3 — a probabilistic programming framework written in Python.
Prior to joining Quantopian, Thomas did his PhD at Brown University where he developed Bayesian methods and Neural Networks to understand brain disorders.
A recognized international speaker, he has given talks at conferences across the US, Europe, and Asia.
Mike received his PhD from Imperial College London where he developed fluid dynamics codes for rocket propulsion engines.
Subsequent to his PhD he worked as the lead systems developer for Oxalyst Systems LLP, a long-short equity fund based in London.
He is now the founder of QuantStart.com, which discusses quantitative trading methods using Python and R.
After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors.
Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute.
Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania. Wes currently resides in the suburbs of Philadelphia with his wife and 3 kids.
Saeed Amen is the founder of Cuemacro. Over the past decade, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura.
Independently, he is also a systematic
FX trader, running a proprietary trading book trading liquid G10 FX, since 2013.
He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan). Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading.
His clients have included major quant funds and data companies such as RavenPack and TIM Group. He is also a co-founder of the Thalesians.
Alec is Lead Research Scientist at Kensho. He holds a PhD in Physics.
Alec also teaches at Financial Engineering programs of NYU School
of Engineering and Stevens Institute of Engineering using his book "Financial Markets and Trading: An Introduction to Market Microstructure and Trading Strategies" (Wiley, 2011).
Jack Vogel, Ph.D., is co-CIO, CFO and a managing member of Alpha Architect, a quantitative asset management and consulting firm to high net worth family offices. Dr. Vogel conducts research in empirical asset pricing and behavioral finance.
Dr. Vogel is a co-author of DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth and QUANTITATIVE MOMENTUM: A Practitioner’s Guide to Building a Momentum-Based Stock Selection System.
His academic background includes experience as an instructor and research assistant at Drexel University in both the Finance and Mathematics departments, as well as a Finance instructor at Villanova University He has a Ph.D. in Finance and a MS in Mathematics from Drexel University. Dr. Vogel graduated summa cum laude with a BS in Mathematics and Education from The University of Scranton.
Scott has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems.
Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE.
Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University.
Scott was chosen as one of Forbes' 30 under 30 in 2016.
Tony Nash is Chief Economist and CEO
at Complete Intelligence. Tony also serves as Senior Advisor for International Economic Policy at the American
Council for Capital Formation, based in Washington, DC, and is an international advisory board member for Texas A&M University.
He is a contributor to leading global media which include regular sessions
on CNBC, Bloomberg, BBC, Nikkei and
Al Jazeera. He is a key facilitator for regional economic and industry forums and closed-door corporate executive
and government dialogues.
Tony has a Masters degree in International Relations from the Fletcher School of Law & Diplomacy at Tufts University and a BA in Business Management from Texas A&M University.
Mayookh Lad is heading the Analytics business at Complete Intelligence, a economics forecasting and financial analytics firm. Mayookh comes from a varied background, with a degree in Electronics & Telecommunication engineering, an MBA and Diploma in Law from the UK.
He has been involved with Delta Economics and was responsible for setting up and running the Analytics business which involved analysing and interpreting Trade Data as well as data production and quality. Analysis from this database covered Benchmarking, Trading and pure Analytics applications - analysing country/regional/goods profiling, a one of a kind database in the market. The application of this business stream involved clients across the banking sector and asset management firms.
Rajib is the co-founder at iRage - a High Frequency Trading firm based in India. At iRage he manages the trading business - contributing a significant portion of the exchange volumes in derivative segments in Asia.
Prior to iRage, Rajib worked with leading HFT firm Optiver – trading strategies that contributed a significant portion of equity & equity derivative volumes in most major US & European exchanges. Previously, as a strategy consultant, Rajib assisted a consortium start a national commodity derivatives exchange. He had also interned with Bloomberg R&D in New York and with Solutia’s EMEA strategy HQ in Belgium.
Rajib has a MBA degree from IIM Calcutta and a bachelor's degree in computer engineering from National Institute of Technology Surathkal. A National Olympiad finalist, Rajib has twice represented India at the World Puzzle Championships.
Raymond McTaggart is Quandl's lead Quantitative Data Curator. He is responsible for selecting, qualifying, vetting, and onboarding new alternative and commerical databases for the Quandl platform.
Ray authored the Quandl R integration and oversees the design of analyst tools developed in house.
He has a degree in Applied Mathematics and Statistics from the University of Toronto.
Aaron Fifield is the host of Chat With Traders podcast, which began in 2015 and is now past 100-episodes.
Each week Aaron has conversations with traders from across the full gamut of market participants—including the likes of: Edward Thorp, Blair Hull, Anthony Saliba and Manoj Narang.
Aaron’s also a developing quant trader and self-taught programmer.
Bob Levy is CEO of Virtual Cove Inc. & inventor of the company’s solution for rapidly making sense of financial data. He brings over two decades’ tech industry experience with firms including IBM & MathWorks.
Mr. Levy was founding president of the Boston Product Management Association in 2001, a 6,000+ person non-profit.
John Horcher is Co-Founder of Virtual Cove Inc. He brings over 15 years of financial markets experience in trading, investment banking & analyst roles.
Mr. Horcher has also held senior level roles with firms including SunGard, Business Intelligence Advisors, TIM Group, EDS & Intergraph. John also served as Managing Director of Halpern Capital, where he drove the investor base for research sales & investment banking opportunities including raising over $300 million in equity/debt.
Jamie is an analyst on the futures and data teams at Quantopian. The futures team is responsible for adding futures trading to Quantopian. The data team works to add dozens of datasets to the platform and builds interfaces for the community to efficiently interact with very large amounts of data.
Previously, Jamie worked at the Network Dynamics Lab where he focused on measuring and modeling large-scale human behavior on large online platforms.
He graduated from McGill University with a BSc in Computer Science & Biology.
The Workshop has been developed by Delaney Mackenzie, Director of Academia at Quantopian,
whose focus is on the intersection of computer science, statistics, and finance.
His background includes seven years of bioinformatics research and delivering lectures at schools including:
Harvard and MIT.
Have a question or comment about our workshops?
Reach out to Delaney at delaney@quantopian.com or
you can also visit us at: www.quantopian.com/workshops.
Rob is an Adjunct Professor at NYU's Courant Institute where he co-teaches a course on Times Series Analysis and Statistical Arbitrage. He is also currently a Senior Advisor to Quantopian.
He has been a Portfolio Manager for over 15 years at Millennium Partners, JPMorgan, and Visium Asset Management.
Rob received his Ph.D. in Finance from Wharton.
Max works at Quantopian as a data scientist and manages the lecture series for the academic team, coordinating content development and helping to run the company's quantitative finance workshops.
Max holds a MS in Mathematical Finance from Boston University and has a strong background in statistics and computer science.
He has implemented trading systems based on machine learning in the past and has published research on theoretical mathematics.
QuantCon NYC 2017 welcomes everyone who wants to learn about algorithmic trading, quantitative finance, machine learning, and Python.
Quants, analysts, data scientists, programmers, researchers, C-level executives, portfolio managers, hedge fund professionals, traders, and students are all among past attendees.
Past QuantCon blog posts, videos, slide decks, and corresponding research notebooks are available now. Visit our QuantCon Blog today!
Since 2014, over 700 people have participated at QuantCons in New York City and Singapore.
Past QuantCon blog posts, videos, slide decks,
are available now.
Visit our QuantCon Blog today!
Quantopian inspires talented people from around the world to write investment algorithms. Quantopian provides capital, data, and infrastructure to algorithm authors. We offer license agreements for algorithms that fit our investment strategy, and the licensing authors are paid based on their strategy’s individual performance.
We provide everything a person needs to create a strategy and profit from it. For more information about Quantopian, please visit: https://www.quantopian.com/.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.