Open Source in Quantitative Finance
– The Revolution.

Frankfurt/Eschborn, 05. June 2015, 9-17h

The Cube, Deutsche Boerse Group

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Learn about the benefits of Open Source languages like Python, Julia, R or Scala.


Get an overview of what tools the Open Source world provides for Quant Finance.

Big Data

Learn about Open Source backend and big data technologies useful for Quant Finance.

  • Front End Revolution

    Tools like browser-based, interactive notebooks revolutionize how Quant Finance is conducted on a daily basis. Interactivity, collaboration, reproducability are nowadays easily accomplished with such tools.
    Powerful libraries allow, for example, to visualize in interactive D3.js fashion complex datasets with a single method call.

  • Language Revolution

    Modern, high level Open Source progamming languages like Python, R and Julia allow the implementation of even the most complex financial algorithms at much faster speeds (compared to e.g. C++) without suffering a significant performance burden. Dynamic compiling approaches, e.g. using the LLVM infrastructure, yield highly performing implementations that are nevertheless concise and easy to maintain.

  • Backend Revolution

    Cloud providers offer small and huge compute instances billed by the hour, clusters with 100 instances are instantiated in 10 minutes, Spark revolutionizes Big Data with its superior architecture compared to traditional Hadoop implementations.

Conference Schedule

Real world expert know-how, many new insights and amazing looks behind the scenes.

At Quantopian we love open source. Quantopian provides a browser-based IDE to develop trading algorithms in Python. These algorithms can be backtested and deployed live with a brokerage account. More recently, Quantopian launched an IPython Notebook based research platform. All these technologies rely heavily on open source software. But we also give back to the community, our full backtester software called Zipline has been open source since inception and received dozens of user contributions. When we have a good change to an open source project, we provide a patch.

In this talk I will discuss common concerns about open sourcing one's own code, highlight the benefits — of which there are many — as well as share our own experiences in running an successful open-source project.


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. and (cf.


Saeed illustrates in his talk the use of (unstructured & structured) Big Data in financial trading by the means of two case studies.

The first case study is about trading bond futures & FX using RavenPack news data. The seconds illustrates the use of FX tick data to understand stylized properties of intraday FX behaviour — it also includes Python code examples.


A combination of C/C++, Java, R and Python is currently the preferred option when tackling real world projects in the financial sector.

In this talk, Malcolm discusses the strides being made in the Julia community and poses the question: "Is Julia ready for the enterprise?", indicating how, when coupled with asynchronous operations, significant improvements in performance can be achieved by the use of this dynamically compiled language (using LLVM).


In this talk we will see the power of Python in building an energy trading business from scratch. From data capture and visualisation, to portfolio valuation and optimisation, to strategy testing and automated reporting, all the way to the deployment as a Web application or in Excel — the Python ecosystem provides a suite of powerful tools to get the job done cleanly and quickly.

In situations where there is a high premium on performance, close-to-the-metal languages (like C/C++) can be easily integrated.


Although there are often new and more sophisticated tools emerging in the world of quantitative finance, Excel is usually still somewhere involved. xlwings is an Open Source Python package that connects Excel with Python on both Windows and Mac. It allows you to interact with Excel from IPython Notebooks or any other Python environment and also enables you to replace VBA macros with Python code.

This talk will give an overview of the current Excel/Python landscape and will demo xlwings' ease of use. The demo will also include examples from finance where Excel merely acts as a user front end.


We show how a combination of Python, C++ and D3.js allows us to visualize and analyze the order books for futures at Eurex.

The orderbook viewer tool allows to drill down to single orders as well as to give a visual impression of the order book dynamics and market micro structure.


Risk Management is driven by distinct but interdependent processes (e.g. VaR calculation, stress testing). Although posing different non-functional requirements — such as performance, frequency and data volume — these processes must be functionally consistent. Such diverse requirements are usually contradictory, inevitably leading to complexity.

We evaluate, with concrete coded examples, existing open source tools from the Risk Analytics and complexity handling point of view. The examples use mainly the PyData (e.g. Pandas, Bokeh) stack and the Apache Spark framework.


Chris will demonstrate — mainly using IPython Notebooks — the plotting of interactive 2d and 3d financial graphs using both Python and R. In addition, the Cufflinks library will be shown which binds together Plotly and the data analytics library Pandas for carrying out the complete data analysis workflow in Python.


In this talk, Jorge will demonstrate what the future holds for Quants using and retrieving financial data from Thomson Reuters via an upcoming, unified Python-API.

9:00 Dr. Dragos Crintera
Deutsche Boerse Group
Keynote "Building Tomorrow
9:30 Dr. Yves Hilpisch
The Python Quants
Open Source in Quant Finance
9:45 Thomas Wiecki, PhD
Open Source Software at Quantopian
Abstract | Slides
10:15 Saeed Amen
Using Big Data for Trading Bond Futures & FX
Abstract | Slides
11:15 Dr. Teodora Baeva
Python for Quant Finance — Success Stories from the Trading Floor
Abstract | Slides | Github Repo
11:45 Jorge Santos
Thomson Reuters
New Python-API of Thomson Reuters
12:15 Sebastian Neusüß
& Pavel Schön

Deutsche Boerse Group
Orderbook Visualization and Analysis
Abstract | Slides
14:00 Chris Parmer
Interactive 2d & 3d Financial Plotting with
14:30 Dr. Malcolm Sherrington
Amis Consulting
Julia as a HPC Alternative for Quant Finance
Abstract | Slides
15:00 Dr. Miguel Vaz
Market Risk Analytics and Aggregation with Python & Spark
Abstract | Github Repo
16:00 Felix Zumstein, CFA
Zoomer Analytics
xlwings — Scalable Financial Analytics with Python & Excel
Abstract | Slides | Github Repo
16:30 Dr. Yves Hilpisch
The Python Quants
Open Source Deployment via the Browser
Abstract | Slides

We are proud of our sponsors.

Eurex Eurex is one of the world's leading derivatives exchanges offering a broad range of international benchmark products, operating the most liquid fixed income markets in the world and featuring open and low-cost electronic access.
d-fine With over 500 highly accomplished consultants based in Frankfurt, Munich, Vienna, Zurich and London, d-fine is one of the leading providers of demanding quantitative and technical consulting projects in Europe.
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.
Thomson Reuters
Thomson Reuters is the world’s leading source of intelligent information for businesses and professionals. We combine industry expertise with innovative technology to deliver critical information to leading decision makers in the financial and risk, legal, tax and accounting, intellectual property and science and media markets, powered by the world's most trusted news organization.

Applicable Know-how

Delegates get a 40% discount code for this O'Reilly book (50% on the ebook).

Delegates get a 30% discount code for this upcoming Wiley Finance book.

The Python Quants Group

The Python Quants Group focuses on the use of Python and Open Source software for Quantitative Finance and Data Science.

The group provides browser-based, scalable solutions for financial analytics ( and data science ( It also develops and maintains the Python-based Open Source library DX Analytics ( In addition, the group offers consulting, development and training services.

Dr. Yves J. Hilpisch (, the group's founder, is author of "Python for Finance" (O'Reilly) and "Derivatives Analytics with Python" (Wiley Finance). He organizes Meetup groups and conferences in Frankfurt, Berlin, London and New York. He is also lecturer for Computational Finance at the CQF program (

The Cube, Deutsche Boerse Group

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D-65760 Eschborn

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