Archive for the 'Finance' category

PyXLL: Deploy Python to Excel Easily

Feb 06 2014 Published by under Canopy, Enthought Canopy, Finance, News, Python, PyXLL

PyXLL Solution Home | Download PyXLL | Press Release

Today Enthought announced that it is now the worldwide distributor for PyXLL, and we’re excited to offer this key product for deploying Python models, algorithms and code to Excel. Technical teams can use the full power of Enthought Canopy, or another Python distro, and end-users can access the results in their familiar Excel environment. And it’s pretty straightforward to set up and use.

PyXLL is free for non-commercial and evaluation purposes, and in Canopy you can simply grab it from the Enthought repo via the Package Manager as shown in the screenshots below (note that at this time PyXLL is only available for Windows users). The rest of the configuration instructions are in the Quick Start portion of the documentation. PyXLL itself is a plug-in to Excel. When you start Excel, PyXLL loads into Excel and reads in Python modules that you have created for PyXLL. This makes PyXLL especially useful for organizations that want to manage their code centrally and deploy to multiple Excel users.

Enthought Canopy Package Manager   Install PyXLL from Enthought Canopy's Package Manager

To create a PyXLL Python Excel function, you use the @xl_func decorator to tell PyXLL the following function should be registered with Excel, what its argument types are, and optionally what its return type is. PyXLL also reads the function’s docstring and provides that in the Excel function description. As an example, I created a module and registered it with PyXLL via the PyXLL config file. In that module I put a simple function pyfib(): a naive Fibonacci implementation.

When I start Excel, I can access the Excel function wizard and find my pyfib() function and use it. The function documentation in Excel comes from my docstring. PyXLL parses the “n: integer input” portion as the variable documentation.

If I go back and make a change to the function, I can reload PyXLL without restarting Excel and update the cells. If I add another function to my module, it too will get loaded and be available to use in my worksheet.

So if you are developing Python models or functions for a large number of distributed Excel users, you can manage the code centrally. PyXLL will load new versions and new functions from the central repository whenever a user starts Excel. Deployment is very straightforward, and central management of all the code reduces the risk of Excel macros and functions proliferating uncontrolled.

I can also create menu functions using the decorator @xl_menu. PyXLL ships with several examples that you can start with. The one below adds a menu item to the Excel Add-in menu, and pops up a message box when selected.


As I said earlier, PyXLL is free to download for non-commercial and evaluation purposes. In Canopy it’s available in the Package Manager (as long as you upgrade to Canopy v1.3 first), and for other Python distros it’s available from our PyXLL store page. You can also find more details and documentation on the PyXLL product pages.

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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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