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Webinar: Work Better, Smarter, and Faster in Python with Enthought Training on Demand

Jul 22 2014 Published by under Enthought Training on Demand, NumPy, Python, SciPy, Training, Webinars

 

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We’ll demonstrate how Enthought Training on Demand can help both new Python users and experienced Python developers be better, smarter, and faster at the scientific and analytic computing tasks that directly impact their daily productivity and drive results.

Enthought Training on Demand Webinar

Space is limited! Click a webinar session link below to reserve your spot today:

  • Tues, August 5, 2014, 1:00-1:45 PM CDT (REGISTER)
  • Wed, August 6, 2014, 8:00-8:45 AM CDT (REGISTER)
  • Wed, August 13, 2014 11:00-11:45 AM CDT (REGISTER)

 

What You’ll Learn

Whether you’re new to the language or looking to expand your existing capabilities, you’ll see examples of how this innovative training can help you:

    • Work Better by using the most effective approaches to problems, reducing time spent on trial and error; we’ll show you not just the “what” but the “how” and “why”
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    • Work Faster by accelerating your learning through courses tailored to the particular tasks and needs of your role; you’ll focus your learning time on the 20% of topics you’ll put to use 80% of the time

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PyQL and QuantLib: A Comprehensive Finance Framework

Authors: Kelsey Jordahl, Brett Murphy

Earlier this month at the first New York Finance Python User’s Group (NY FPUG) meetup, Kelsey Jordahl talked about how PyQL streamlines the development of Python-based finance applications using QuantLib. There were about 30 people attending the talk at the Cornell Club in New York City. We have a recording of the presentation below.

FPUG Meetup Presentation Screenshot

QuantLib is a free, open-source (BSD-licensed) quantitative finance package. It provides tools for financial instruments, yield curves, pricing engines, creating simulations, and date / time management. There is a lot more detail on the QuantLib website along with the latest downloads. Kelsey refers to a really useful blog / open-source book by one of the core QuantLib developers on implementing QuantLib. Quantlib also comes with different language bindings, including Python.

So why use PyQL if there are already Python bindings in QuantLib? Well, PyQL provides a much more Pythonic set of APIs, in short. Kelsey discusses some of the differences between the original QuantLib Python API and the PyQL API and how PyQL streamlines the resulting Python code. You get better integration with other packages like NumPy, better namespace usage and better documentation. PyQL is available up on GitHub in the PyQL repo. Kelsey uses the IPython Notebooks in the examples directory to explore PyQL and QuantLib and compares the use of PyQL versus the standard (SWIG) QuantLib Python APIs.

PyQL remains a work in progress, with goals to make its QuantLib coverage more complete, the API even more Pythonic, and getting a successful build on Windows (works on Mac OS and Linux now). It’s open source, so feel free to step up and contribute!

For the details, check out the video of Kelsey’s presentation (44 minutes).

And here are the slides online if you want to check the links in the presentation.

If you are interested in working on either QuantLib or PyQL, let the maintainers know!

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