Quantopian's Algorithmic Trading and Quantitative Finance Conference returns to NYC for it's fourth year April 28th 2018. Livestream tickets available at www.quantcon.com.
Quantopian's Algorithmic Trading and Quantitative Finance Conference returns to NYC for it's fourth year April 28th 2018. Livestream tickets available at www.quantcon.com.
Quantopian's Algorithmic Trading and Quantitative Finance Conference returns to NYC for it's fourth year April 28th 2018. Livestream tickets available at www.quantcon.com.
Quantopian will host the second annual QuantCon Singapore
on September 28th-30th 2017.
Our conference will feature expert workshops, talks,
and a hackathon, with a clear focus on algorithmic trading,
portfolio optimization, and machine learning -
all with the goal to help you craft and improve on your trading strategies.
Exclusive access to videos and presentations from QuantCon NYC 2017 are included with every ticket purchase.
Our 3-day program will focus on rigorous methodologies and ideas, cutting-edge data and tools, and current industry trends, that can help you craft, improve on, and trade your own investment strategies.
The program includes:
An interactive workshop day to help you improve on your trading strategies.
A main conference day featuring:
talks, tutorials, and breakout sessions from experts across the quant finance universe.
A hackathon day where you can test your investment strategy skills.
Exclusive access to videos and presentations from QuantCon NYC 2017.
Yi Li
Portfolio Manager at GIC’s Systematic Investment Group
Morning Keynote
"An Ensemble Approach to Nowcasting Economic Conditions: A Practitioner’s View"
Macroeconomic data are mostly backward-looking and suffer from publication lags and revisions. In this talk, we will explore three approaches to estimate high-frequency “nowcasts” of these low-frequency economic releases. We will further examine if an ensemble of these methods could give us the best chance at extracting true signal from the noises. Lastly, we will share practical advices on how best to incorporate these high-frequency fundamental data into a framework of systematic macro investing.
Delaney Mackenzie
Director of Academia at Quantopian
Afternoon Keynote
"Arming the Quant Revolution"
The asset management industry is going through a quantitative revolution; simple processes are getting cheaper and more reliable, while on the flip side newer and highly sophisticated methods are emerging.
We believe education is the only solution to surviving in this new world. We will also discuss how Quantopian views new quants navigating in the industry, some types of strategies we believe can produce alpha, and preview some upcoming features at Quantopian.
"Applied Reinforcement Learning in Trading Algorithms"
by Pierre Maarek
Vice President, Linear Quantitative Research at J.P. Morgan
Execution algorithms are hierarchical decision frameworks designed to optimally plan and trade portfolios over a period of time. The planning process tracks a desired trade-off between transaction costs and uncertainty or risk, whereas the execution process implements the plan and focuses on efficiency.
In practice, the execution component of trading algorithms consists of complex heuristics or rules that encode the trading logic, which can become highly complex and difficult to optimize.
The topic of this presentation is to outline the application of reinforcement learning in trading algorithms to directly optimize down to the transaction level, over various problem configurations.
In this framework, the rules that govern the logic of a trading algorithm – historically written by humans, are instead encapsulated in the parameters of complex functions learned by machines.
This machine learned algorithm can outperform the hand-tuned algorithm in a variety of conditions to be discussed, and serves as a more integrated and efficient framework for the development of next generation of trading algorithms.
In this sharing session, Anthony will share his experience and challenges in applying machine-learning techniques to trading. He starts with some examples of machine learning application to well-known trading strategies such as pairs trading, long short factor investing, regime detection etc. and gradually move to cover more advanced artificial intelligence techniques. Most of these strategies have been implemented in Quantopian platform using Python.
This talk is ideally suited to those who are starting out in apply machine learning techniques to trading and those who are looking for fresh innovative ideas.
“Real-Time Machine Learning Architecture and Sentiment Analysis applied to Finance”
by Dr. Juan Cheng, Data Scientist
at InfoTrie
The vast proliferation of data related to the financial industry introduces both new opportunities and challenges to quantitative investors. These challenges are often due to the nature of big data and include: volume, variety, and velocity.
In this talk, Dr. Cheng will take the audience on a tour of the “big-data production line” in InfoTrie and show how the financial news collected from various and customizable sources are transformed into quantitative signals in a real-time manner. The talk will touch on various kind of topics like sentiment analysis, entity detection, topic classification, and big-data tools.
"Deep Reinforcement Learning for Optimal Order Placement in a Limit Order Book"
by Ilija Ilievski, Ph.D. Candidate, NUS
Financial trading is essentially a search problem. The buy-side agent needs to find a counterpart sell-side agent willing to trade the financial asset at the set quantity and price.
Ilija will present a deep reinforcement learning algorithm for optimizing the execution of limit-order actions to find an optimal order placement. The reinforcement learning agent utilizes historical limit-order data to learn an optimal compromise between fast order completion but with higher costs and slow, riskier order completion but with lower costs.
The talk will continue with the challenges of applying reinforcement learning to optimal trading and their potential solutions. Finally, Ilija will share the system architecture and discuss future work.
"Demonstration of Machine-Learning Based Strategy Parameter Selection in Python"
by Dr. Thomas Starke, Quantitative Trader at Vivienne Court
System Parameter Permutation (SPP) has been a hot topic in quantitative trading in the past few years. However, for most people this is still quite an abstract concept that is challenging to put into practise. This presentation will demonstrate how to apply SPP in practise and how machine learning can help to improve and automate this process. It also aims to demonstrate the importance of SPP as integral part of trading strategy development.
Lookahead bias and stale data when used in an algorithm are generally categorized as "incorrect data". In fact, the issue does not lie with the data itself, but instead is an issue of perspective. This talk will examine how data is typically viewed through the lens of time, and why, on the whole, that approach is wrong.
At Quantopian, we've tried several ways of handling data with regards to time, and we'll talk about lessons learned along the way. We'll also discuss what multidimensionality means for financial data specifically, and how we can apply this to get better results in backtesting.
Additionally, we'll touch on how to apply multidimensionality to more general data, and why it's important for anyone working with applied data to take this approach.
At JPMorgan's annual quantitative conference 93% of investors said alternative data will change the investment landscape.
In this presentation, Emmett will discuss the rapidly increasing adoption of alternative data, give a detailed overview of the 24 different types of alternative data, outline the applications of alternative data for quantitative funds, discuss interesting datasets that are available (including Asian datasets) and present case studies that evidence value in alternative datasets.
Since the publication of Markowitz’s seminar paper in 1952, there have been numerous research and papers that show that, though elegant and mathematically solid, his portfolio optimization theory is of little practical value.
Markowitz’s theory makes two unrealistic assumptions – the availability of both expected means and expected covariance for the future which are known to be very difficult to estimate or even guess.
In this presentation, we will survey the latest development in portfolio optimization technologies and how we can apply the new theories to generate positive profits in practice.
"Seeking Alpha Through Asset Rotation: An Alternative Way of Applying Modern Portfolio Theory"
by Danielle Jiang, Founder and CEO of Hedga Technology
Robot advisors are growing rapidly globally and the majority of them are based on modern portfolio theory. The traditional application of creating a balanced portfolio to achieve a optimal investment returns from the efficient frontier, and rebalancing the weights back to the optimal allocation of a diversified asset pool has been adopted and also criticized in many places. Some people embrace it as a smart beta strategy. Some people criticize for the model assumption, as the behavior of the assets are changing over the time.
In this talk, Danielle will walk through an alternative way of understanding and applying modern portfolio theory to achieve alpha, through asset rotation in stead of asset allocation.
"Portfolio Optimization When You Don’t Know the Future (Or The Past)"
by Rob Carver, Independent Systematic Futures Trader, Writer And Research Consultant
We generally assume the past is a good guide to the future, but well do we even know the past? What effect does this uncertainty when estimating inputs have on the notoriously unstable algorithms for portfolio optimization?
I explore this issue, look at some commonly used solutions, and also introduce some alternative methods.
"From Alpha Discovery to Portfolio Construction: Pitfalls and Solutions"
by Dr. Oleg Ruban
Executive Director And Head of Analytics Applied Research for Asia Pacific At MSCI
Implementation is the efficient translation of alpha research into portfolios. It includes portfolio construction and trading. It is a vital step in the quant equity workflow, as poor implementation can ruin even the best alpha ideas. Two crucial challenges must be solved: how to construct a portfolio that most efficiently captures a given alpha signal; and, in the presence of multiple signals, how to optimally combine them into a single composite alpha factor.
This talk addresses these challenges, examines common pitfalls in the implementation of quantitative strategies and good practices to avoid them. A common theme is striking the right balance between factor signal purity and investability. We look at how factor models and optimisation techniques help professional investors answer three key questions:
· What risks should your risk model be cognisant of?
· What objective function should you use?
· What effect do investability constraints have on your portfolio?
"Supply Chain Earnings Diffusion"
by Josh Holcroft, Head Of Quantitative Research, Asia At UBS Investment Bank
Supply chains and network effects are becoming increasingly important and increasingly transparent in the global economy. However, conventional techniques are poorly equipped to handle relational data, and new techniques are required to decode the meaning of supply chain effects. We explore a novel technique for modelling and forecasting the diffusion of earnings revisions, known as a diffusion graph kernel support vector machine.
"Behavioral Factors and Their Performance in Emerging Markets: An Illustration Using China A-Shares Data"
by Dr. Jason Hsu, Founder and CIO of Rayliant Global Advisors
This talk looks at anomalies in China A-shares. The empirical study presented gives the audience insight into the behavioral mechanisms behind various investment factors. This talk is more than identifying anomalies in China or testing traditional factor methods using A-shares data; it gives the audience a deeper understanding into how behavioral factors arise in markets with naïve investors who make mistakes.
Fundamental and quantitative stock selection research has long focused on creating accurate forecasts of company fundamentals such as earnings and revenues. In this talk we examine why fundamental forecasts are powerful and survey some classic methods for generating these forecasts. Next we explore some newer methodologies which can be effective in generating more accurate fundamental forecasts, including new uses of traditional data as well as novel crowdsourced and online behavior databases. Finally, we present new research examining the temporal variation in efficacy of these forecasts with an eye towards understanding the market conditions in which an accurate fundamental forecast can be more or less profitable.
"How Much Do You Pay for the Price Impact of Your Trade?"
by Dr Christopher Ting, Associate Professor of Quantitative Finance Practice at SMU
Whether big or small, any liquidity-taking trade (using market order or marketable limit
order) will create a price impact. A few heuristic models have been proposed but one that is derived from first principles is yet to appear. This talk provides a model derived
from the rich literature of market microstructure, with a focus on empirical analysis. In particular, we examine five different futures contracts on the same underlying Nikkei 225 index: onshore big and mini Nikkei futures, the offshore Nikkei futures of SGX and CME, as well as CME's quanto futures denominated in dollars. This model is useful for analyzing agency execution costs of trading in pre- and post-trade analyses. The model will also be useful in accounting for the price impact in back-testing of trading or investment strategies.
Factor modeling and style premia are historically well documented and extensively researched in generating abnormal returns. Despite the large amount of research around factors, there is less clarity around effectively capturing and extracting this alpha from a given universe. In this presentation, Cheng will demonstrate different techniques for combining multiple factors, and the rationale behind maximizing alpha while maintaining scalability.
“Market Insights Through the Lens of a Risk Model”
by Olivier d'Assier, Head of Applied Research, APAC for Axioma
In this presentation, Olivier d’Assier, Managing Director of APAC Applied Research, will discuss the major drivers of the change in risk year-to-date and how the risk environment is affecting investor’s portfolios. This talk will look at global markets with a focus on the Asian region and how it compares to others with regards to its risk footprint.
"Order & Randomness in Asian Market Microstructure"
by Kerr Hatrick, Executive Director of the Electronic Trading Strategist Group at Morgan Stanley
An understanding of the statistics of intraday market dynamics is as important to the design of trading algorithms, as the study of aerodynamics in the construction of planes. The statistics of microstructure is central to the optimal timing and sizing of trades; it is also central to estimating whether a trading strategy might work in more than in one country. Between markets, microstructural regimes vary widely, and in Asia this divergence is particularly prominent. Assumptions regarding microstructure can make the difference between success and failure - in resizing portfolios, or porting alpha, or in the expansion of asset universes.
Despite this, it is still hard to get accurate, relevant and timely advice on market microstructure. Opaque, oversimplified and out-of-date figures abound in algorithmic trading brochures. The scale of the data involved, it's irregularity, the difficulty of denoising – all present real challenges. The knowledge of many practitioners, about the market microstructure of the places they trade, is consequently filled with gaps. In this presentation, using scientific visualization, we attempt to fill some of these gaps in.
"Quant Trading for a Living – Lessons from a Life in the Trenches"
by Andreas F. Clenow, Chief Investment Officer For ACIES Asset Management
It takes hard work, skill and time to develop robust trading models, but that is just the beginning of the journey. The question then is what you can do with it, and how to go about building a career in quant finance.
If your plan is to move beyond hobby trading and build a career in in the professional quant trading field, the work is not over once you have a great model.
This presentation will discuss how to leverage your trading models into building a successful career in quant trading. We will look at the various options available, and their respective merits and faults. Whether you want to trade your own money for a living, find a job in the industry or build your own business, your model design will have to be adapted to your aim. We will discuss what type of models and results there is a market for, how to go about finding investors for your trading, and how the real economics of the business look.
"How to Run a Quantitative Trading Business in China with Python" by Xiaoyou Chen, Head of Option Trading at Shanghai Junzhi Asset Management Ltd.
Running a quantitative trading business in China used to be very difficult and require strong IT skills, however it's getting much easier nowadays, when traders with no professional IT training can also do all the tasks in quantitative trading using Python.
In this sharing session, Xiaoyou will share his experience in using Python for data collection, strategy development and automated trading. He will also introduce some related open source projects including TuShare, quantOS, vn.py and so on.
As Director of Academia at Quantopian, he oversees the firm’s global educational and academic initiatives. While working with professors at schools including Princeton, MIT, and Harvard, Delaney developed the free online Quantopian Lecture Series.
Pierre Maarek and the LQR team are responsible for the research underpinning J.P. Morgan’s algorithmic trading suite in Asia Pacific. In particular, they are in charge of developing the quantitative models driving decisions made by the trading algorithms. Pierre spent ten years in quantitative trading roles, including SocGen and Barclays.
Jason is Founder and CIO of Rayliant Global Advisors, an asset manager specializing in Chinese equities. Jason also co-founded Research Affiliates, a $169B investment manager specializing in Smart Beta indices and asset allocation. Jason is also Co-Founder And Vice Chairman of Research Affiliates, and a Professor In Finance at UCLA Anderson School.
Eagle Alpha was incorporated in September 2012 with the sole purpose of enabling asset managers to obtain alpha from alternative data.
Prior to founding Eagle Alpha Emmett was an investment banker with Morgan Stanley and Credit Suisse. For the majority of his career he focused on equity capital market transactions in the TMT sectors.
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.
Andreas F. Clenow is the Chief Investment Officer for ACIES Asset Management, a Zurich based asset management firm with a nine figure asset base. 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.
Ilija is a machine learning researcher building holistic models of unstructured data from multiple modalities. Currently, Ilija is working on developing a unified model of financial data coming from multiple sources applied to portfolio optimization. Ilija is pursuing a Ph.D. degree in "Complex Data Analysis with Deep Learning" from the National University of Singapore. Previously, he obtained an M.Tech. in Software Engineering for Machine Learning.
Dr. Juan Cheng is leading the Data Science team at InfoTrie, a news analytics company. She has a keen interest in turning complicated “big financial data” into machine-readable and tradable signals. She holds a PhD from National University of Singapore in physics. Before joining InfoTrie, she was a research fellow studying nano-machines.
Anthony Ng has been teaching investment and portfolio management related modulesat 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 Quantopian events.
Dr. Tom Starke has a PhD in Physics and works as an algorithmic trader at Vivienne Court. He has a keen interest mathematical modelling 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.
Olivier d'Assier is Head of Applied Research, APAC for Axioma, responsible for generating unique regional insights into risk trends by leveraging and analyzing Axioma's vast data on market and portfolio risk. His research helps clients and prospects to better understand and adapt to the evolving risk environment in Asia Pacific. The author of periodic special reports, d'Assier produces regional and global research on market and portfolio risk.
Cheng is currently a Software Engineer at Betterment, the largest independent online financial advisor. Betterment manages more than $10 billion in assets for 280,000 customers. Prior to Betterment, Cheng worked across multiple industries (AIG, Blackberry, Textnow, Keyobi), as an Analyst, Software Engineer and Startup Founder. His passion for finance and technology drives his independent research as the founding member of Quantamental Investments, LLC.
As Head of Analytics Applied Research for Asia Pacific, Oleg
Ruban focuses on portfolio management and risk related
research for asset owners and investment managers in the
Asia Pacific region.
Prior to joining MSCI in 2008, Oleg worked as an emerging
market economist and a quantitative strategist at Dresdner
Kleinwort.
Dr. Christopher Ting
Associate Professor of Quantitative Finance Practice at the Lee
Kong Chian School of Business, SMU
Dr Christopher Ting is an Associate Professor of Quantitative Finance Practice at the Lee Kong Chian School of Business, Singapore Management University. He has served as the founding director of the Master of Science in Quantitative Finance programme, as well as the area coordinator of Quantitative Finance Group.
Vinesh founded ExtractAlpha in 2013 in Hong Kong with the mission of bringing analytical rigor to the analysis and marketing of new data sets for the capital markets. From 1999 to 2005, Vinesh was the Director of Quantitative Research at StarMine in San Francisco, where he developed industry leading metrics of sell side analyst performance as well as successful commercial alpha signals and products based on analyst, fundamental, and other data sources.
Max's background is in applied mathematics, statistics, and quantitative finance. He runs the online lecture series at Quantopian and is responsible for workshop curriculums and educational content. Max has published work in theoretical mathematics. He works with top universities including Columbia, U Chicago, and Cornell and holds a MS in Mathematical Finance from Boston University.
The workshop will give you a basic understanding of how futures markets are structured, how futures are traded algorithmically, plus reinforce core statistical principles that help you avoid being wrong.
Prerequisites to Attend:
- Laptop (You must bring it to the workshop)
- College-level understanding of mathematics
- Comfortable with the following Quantopian lectures:
Our workshop series is vetted and used by professors at top universities worldwide including: Harvard IACS and Cornell ORIE. We work with academics and industry alike to ensure that our curriculum reflects both academic rigor and practical applications.
8:30am: Register & Light Breakfast
9:00am: Introduction and Overview of Workshop
9:15am: Introduction to Futures
9:45am: Futures Trading on Quantopian
10:15am: Using the Futures API
12:15pm: Lunch & Networking
11:00am: Liquidity
1:30pm: Pairs Trading on Futures Contracts
11:45am: Integration, Cointegration, and Stationarity
2:00pm: Break
1:00pm: Quantitative Research
4:15pm: Questions & Wrap-up
2:45pm: Futures Template Algorithm
3:15pm: Choice of Directed Exercises
2:15pm Exploring Mean Reversion on Futures
We will discuss mathematical factor models for both returns forecasting and risk management, and frame everything in terms of workflows used by professional quants to run large capital bases.
Prerequisites to Attend:
- Laptop (You must bring it to the workshop)
- A strong working knowledge of the
Quantopian IDE and research environment
- Understanding of the following lectures from the Quantopian Lecture Series: Multiple Linear Regression, Hypothesis Testing, Spearman Rank Correlation, Beta Hedging, and the Dangers of Overfitting
- College-level math and statistics.
8:30am: Register & Light Breakfast
9:00am: Introduction and Overview of the Workshop
9:15am: Pipeline API Tutorial
11:15am: The Quant Equity Workflow
12:00pm: Lunch & Networking
1:15pm: Factor Analysis
2:45pm: Factor Combination
2:15pm: Break
1:45pm: Analyzing Factors
3:00pm: Template Algorithm
12:45pm: Long Short Equity
3:30pm: Filling in the Algorithm Template
4:30pm: Backtesting your Algorithm
4:00pm: Performance Analysis
Where can I access the materials covered in the workshops?
The Quantopian Lecture Series and Tutorials contain the materials presented at the workshops.
What value do the workshops bring if the materials are available online?
These workshops provide an enhanced learning experience through the use of hands-on exercises, as well as ample one-on-one time with the lecturer.
What should I bring?
The workshop exercises are entirely online. You must bring your own laptop and laptop charger.
What are the prerequisites for each workshop?
Please see the prerequisite section under the workshop you would like to take, in the above workshop section.
What should I do next after completing a workshop?
Continue to work your way through the lectures, cloneable algorithms and notebooks, found in The Quantopian Lecture Series.
We have set up several ways for you to attend QuantCon and are offering Corporate, Individual, and Student/Academic Tickets.
Exclusive access to videos and presentations from QuantCon NYC 2017 are included with every ticket purchase.
For discounted group pricing, email us at events@quantopian.com.
This ticket allows entrance to one Workshop and the
main conference, a light breakfast and lunch on both days, and one networking cocktail session being held the evening of September 29th.
QuantCon NYC Videos
Exclusive access to videos
and presentations from
When ordering your ticket, please specify which workshop you would like to attend.
This ticket allows entrance to one Workshop and the
main conference, a light breakfast and lunch on both days, and one networking cocktail session being held the evening of September 29th.
QuantCon NYC Videos
Exclusive access to videos
and presentations from
When ordering your ticket, please specify which workshop you would like to attend.
This ticket allows entrance to one Workshop and the
main conference, a light breakfast and lunch on both days, and one networking cocktail session being held the evening of September 29th.
QuantCon NYC Videos
Exclusive access to videos
and presentations from
When ordering your ticket, please specify which workshop you would like to attend.
We welcome everyone who wants to learn about algorithmic trading, quant finance, data science, and machine learning.
Quants, analysts, data scientists, programmers, executives, hedge fund pros, traders, data scientists, and students are all among past attendees.
Past QuantCon blog posts, videos, slide decks, and corresponding research notebooks are available now. Click here to visit our QuantCon Blog!
We have set up discounted rooms for attendees at the venue, the Grand Copthorne Waterfront Hotel. Click here to take advantage of our special QuantCon room rates.
For all inquiries, please reach out to our marketing and events team at events@quantopian.com.
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/.
Disclaimer: 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.