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EVENT DETAILS |
Quantopian's algorithmic trading & quantitative finance conference.
Overcome the Barriers to Algorithmic Trading
Quantopian is hosting QuantCon 2017 in New York City on April 28-30th.
The conference features expert workshops & talks on algorithmic trading, quantitative finance, & machine learning.
There will also be ample networking opportunities with leaders from the quantitative finance world.
The QuantCon Program
An interactive workshop day to help you improve on your trading strategies.
A main conference day featuring:
talks, tutorials, & breakout sessions from experts across the quant finance universe.
A hackathon day where you can test your investment strategy skills.
QuantCon NYC 2017 - April 29th
Our quantitative finance & algorithmic trading conference will feature expert talks & tutorials on algorithmic trading, quantitative finance, machine learning, & Python programming. You will discuss trading strategies, explore alternative data sets, & learn how to apply machine learning to your investment strategies.
At the end of the day? Network with financial luminaries, your peers, & other expert members of the community.
Live stream tickets are available for the main conference. All permissioned-to-be-shared talks will be streamed.
The majority of videos & slide decks from the main conference will be made available shortly after the event to all attendees.
"Trading without Regret"
by Dr. Michael Kearns, Professor at the Computer & 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 & 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 quantiative finance problems, including portfolio construction & inventory risk.
"Building Diversified Portfolios that Outperform Out-of-Sample"
by Dr. Marcos Lpez de Prado, Senior Managing Director at Guggenheim Partners
Hierarchical Risk Parity (HRP) portfolios address three major concerns of quadratic optimizers in general & Markowitz's CLA in particular: Instability, concentration & underperformance. HRP applies modern mathematics (graph theory & 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.
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