FOR PYTHON QUANTS CONFERENCE

An exclusive conference brought to you by CQF Institute and The Python Quants

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NEW YORK CITY ON FRIDAY 01. MAY 2015

The Conference for those working in Finance and using Python.

UNIQUE COMBINATION

This conference is the only in the world to focus exclusively on Python for Quantitative Finance. Do not miss it if you work in finance and use Python.

GAIN INSIGHTS

Keep up with the latest developments in Python for Finance, see the experts in action, meet people active in your field, experience practical case studies.

BUILD YOUR NETWORK

In addition to the main conference program, there is lots of room for networking, chatting, making connections and building your network in the industry.


Stay informed about the latest of Python for Quant Finance.

THE CONFERENCE IN NUMBERS

Some numbers about the FOR PYTHON QUANTS conference.

For those quants believing more in numbers than just words.

390

NYC + LONDON

At the first conference in New York City 225 people attended. At the second, in London, total attendance was 165.

3

NEW YORK CITY

This year, we bring you not only 1 conference but also 2 intensive Python for Finance Bootcamps.

10+

TALKS

Expect a fast paced talk schedule with more than 10 talks over the day. All from experts in their fields.

2

STRONG PARTNERS

The CQF Program & Institute are amongst the world's most renowned education bodies for Quantitative Finance. The Python Quants Group focuses on
Python for Quant Finance.

CONFERENCE TICKETS

Attractive ticket prices for live and online attendance.

PROFESSIONAL
(LIVE)

USD 495
  • For professionals working
  • in Banking, Finance or
  • Consulting and attending live.

PROFESSIONAL
(ONLINE)

USD 295
  • For professionals working
  • in Banking, Finance or
  • Consulting and watching online.

Group Bookings

Package Bookings

For group bookings, email us at events@cqfinstitute.org and receive a 10% discount when registering 3+ delegates.

Receive a 15% discount when booking both bootcamps (buy tickets) or conference and both bootcamps (buy tickets).

IMPRESSIONS FROM LONDON 2014

Get a feel for the energy and flow at the For Python Quants conference.





PYTHON BOOTCAMPS

Intensive Python Bootcamps about Python for Finance for professionals to make their next step.

The technical bootcamp shows you the basics and advanced approaches of typical Python paradigms and libraries, like IPython, NumPy or pandas. The bootcamp covers those basic tools that you need every day.

Topics covered in the financial bootcamp include advanced time series management, analysis and visualization, performance Python through vectorization, real-time data streaming, automated trading strategies, backtesting approaches and automated trading. This bootcamp applies the basic Python tools and libraries presented in the technical bootcamp to real world Quant Finance examples.

The bootcamps take place at Fitch Learning, 55 Broad Street, Manhattan, New York City.

TECHNICAL BOOTCAMP

USD 495 (live)
USD 295 (online)

  • Tuesday, 28. April 2015,
  • one-day, intensive bootcamp,
  • covering technical Python for Finance,
  • with Dr. Yves J. Hilpisch.
  • Read the Outline.
This bootcamp covers basic concepts, topics and ideoms of importance for financial analytics and/or application development projects. The following is an outline of what you can expect:
  • Introducing the Quant Platform (http://pqp.io)
  • Fundamentals of data types and structures in Python
  • Selected Python ideoms for numerical algorithms
  • Fundamentals of NumPy arrays for numerical computations
  • Advanced concepts and approaches with NumPy and SciPy
  • Time series management with pandas and basic operations
  • Advanced operations on pandas DataFrame objects
  • Performant IO operations with Numpy and pandas
  • Selected performance issues and approaches for financial analytics
  • Static and interactive data visualization of numerical and time series data


Close

FINANCIAL BOOTCAMP

USD 495 (live)
USD 295 (online)

  • Wednesday, 29. April 2015,
  • one-day, intensive bootcamp,
  • covering applied Python for Finance,
  • with Dr. Yves J. Hilpisch.
  • Read the Outline.
This bootcamp covers some topics of importance for nearly every financial analytics and/or application project. The following is an outline of what you can expect:
  • Introducing the Quant Platform (http://pqp.io)
  • Fundamentals of pandas and plotly for financial analytics
  • Retrieving, processing and storing financial data
  • Implementing basic backtests for automated trading strategies
  • Optimizing trading strategies, doing in- vs out-of-sample testing
  • Capturing live financial data streams and plotting them in real-time
  • Implementing a simple Web app to visualize financial data (using Flask & plotly)
  • Implementing automated trading strategies with real-time data streaming and buy/sell orders


Close

TALKS

Conference Schedule (coming soon)

Expert know how you can immediately apply.



This talk illustrates the benefit of deploying and using Python via the browser.

kdbpy is a library for writing queries against the kdb+ database from Python. It leverages blaze to generate expressions, gives pandas DataFrames as the output format and uses qpython for communication with q over a tcp socket. It enables users to write a large subset of qsql from the comfort of their own Python interpreter and is one of many backends to come out of the blaze+pydata ecosystem.

I'll discuss a few quirks of the q language (for people unfamiliar with the language), what motivated kdbpy's creation and some details of how kdbpy leverages blaze to generate expressions.


Close

It is complex, costly and risky to deploy heterogeneous open source components across an organization. Web-based technologies allow for a central, unified deployment with end users only needing a (current) browser. Such a strategy facilitates introduction and maintenance of Open Source components for Quant Finance.

Yves illustrates such a systematic deployment strategy by some use cases based on the Quant Platform (cf. http://quant-platform.com) and datapark.io (cf. http://datapark.io).


Close

As Python gains acceptance, many financial companies have started building models in Python for risk management. In this talk, we will discuss the key aspects of model verification and validation and introduce a novel approach to do stress and scenario tests leveraging parallel and distributed computing technologies and the cloud.

We have developed a platform that leverages cloud based technologies to run stress tests on a massive scale without having to invest in fixed in-house architectures. Through a Python based case study, we will illustrate best practices for stress and scenario testing for model verification and validation. These best practices meant to provide practical tips for companies embarking on a formal model risk management program or enhancing their model risk methodologies to address the new realities specifically when building models in Python.


Close

More and more financial modelling and analysis is moving away from monolithic "big-BI" tools towards custom programmatic backends like Python and Pandas. As powerful as this workflow is, it lacks a rich presentation and sharing layer like R's Shiny Apps or Tableau's dashboards. It's time for that to change.

Chris Parmer, Plotly's Co-founder and Chief Product Officer, will demonstrate how to build rich, interactive financial analysis web-apps with Flask, Pandas, and Plotly's d3.js based graphs with just a couple hundred lines of code.


Close

This talk will review techniques for sourcing and combining uncorrelated returns streams to achieve consistent market outperformance. We consider backtested and simulated return streams from systematic (but opaque) algorithmic trading strategies sourced from "the crowd" as a novel asset class.

The discussion will focus on the construction of uncorrelated portfolios from trading algorithms entered into the "Quantopian Open" online trading competition.


Close

Quant teams can be incredibly productive, but to do their best work they need a platform: tools to write & test code, the ability to share code & data with others in the team, relevant data in a form they can easily access and write to, standard financial models & analytics to build on top of, a way to run automated daily jobs, access to large-scale grid compute, the ability to expose their work through applications, and a controlled production environment that protects their organization while allowing for iterative new development.

We discuss what we think is the best architectural model for such a platform, with examples from our implementation, Washington Square Technologies's Beacon platform.


Close

"Roads? Where we're going, we don't need ... roads."

No five year old ever says they want to grow up to "build a hedge fund". But maybe if they knew how easy it was and that if they did really well, they could get way more toys — then maybe they would. Thanks to nearly free and infinitely scalable ubiquitous computing, vast open source libraries and datasets, and the amazing abilities of modern languages, especially Python, the barrier to entry to actually building what is needed to run a hedge fund has never been lower. Python has afforded the ability to go from idea to inception to managing risk quicker than ever before.


Close

The Python financial ecosystem is an incredibly useful collection of related tooling built around a common set of ideas and practices. At Elsen, we are looking to augment the IPython notebook as the next generation accelerated research platform supporting the ability to scale research to arbitrary cloud resources and explore enormous amounts of market data all from the comfort of the existing ecosystem.


Close
8:00 Registration and Morning Coffee
WELCOME & OPENING REMARKS
9:00 Dr. Randeep Gug
CQF Institute
Welcome and Opening Remarks
9:10 Dr. Yves Hilpisch
The Python Quants
Open Source in Quant Finance — The Revolution
DATA & VISUALIZATION
9:30 Phillip Cloud
Continuum Analytics
kdbpy: Readable q via Blaze (Abstract)
10:10 Chris Parmer
Plotly
Building Web-based Financial Visualization Dashboards with Pandas, Flask, and Plotly (Abstract)
10:50 MORNING BREAK
BROWSER & CLOUD
11:20 Stephen Diehl
Elsen
Using High Performance Computing with IPython in Finance (Abstract)
12:00 Sri Krishnamurthy
Quant University
Scaling Python Models for Model Verification and Stress Testing on the Cloud (Abstract)
12:40 LUNCH BREAK
PLATFORMS & TRADING
13:30 Dr. Mark Higgins
& Kirat Singh
Washington Square Technologies
A Batteries-Included Quant Platform:
Scaling Python-Based Analytics and Data
(Abstract)
14:10 Jessica Stauth, PhD
Quantopian
Crowd-sourced Alpha: The Search for the Holy Grail of Investing (Abstract)
14:50 AFTERNOON BREAK
DEPLOYMENT & APPLICATIONS
15:15 Dr. Yves Hilpisch
The Python Quants
Open Source Deployment via the Browser (Abstract)
15:55 Adam Sherman
ARC Investments
Building a Hedge Fund with Python (Abstract)
PANEL DISCUSSION, RAFFLE, CLOSING REMARKS
16:35 Expert Group What Role will Open Source Play in the Future of Quant Finance?
17:10 Book Raffle sponsored by O'Reilly & Wiley and Closing Remarks
17:30 Conference Closing, Get Together

Venue

Fitch Learning 55 Broad Street (3rd Floor) in Manhattan.

Fitch Venue

The venue is next to
the financial center of the world
– Wall Street.

See it on Google Maps.

MEET THE TEAM

Organizers

Your organizer team.

DON'T MISS THIS UNIQUE OPPORTUNITY.

EXPECT 100%
PYTHON
& FINANCE

GET IN TOUCH TODAY!

CONFERENCE SPONSORS

These are the conference sponsors.

Visit their Web sites for more information.

Fitch Fitch Learning is a global leader in financial education with over 25 years of experience in delivering specialized, technical training to the finance community.

Plotly
Plotly is a platform for analyzing data and collaboratively making interactive 2D, 3D, and live-streaming graphs. Plotly is written in Python, and plots are rendered with D3.js, a JavaScript visualization library.
Wiley Wiley is a global provider of content and content-enabled workflow solutions in areas of scientific, technical, medical, and scholarly research; professional development; and education.