Implied Volatility with Python’s Pandas Library AND Python in Excel

Mar 31 2014 Published by under General

Authors: Brett Murphy and Aaron Waters

The March 6 New York Quantitative Python User’s Group (NY QPUG) Meetup included presentations by NAG (Numerical Algorithms Group), known for its high quality numerical computing software and high performance computing (HPC) services, and Enthought, a provider of scientific computing solutions powered by Python.

Brian Spector, a technical consultant at NAG, presented “Implied Volatility using Python’s Pandas Library.” He covered a technique and script for calculating implied volatility for option prices in the Black–Scholes formula using Pandas and nag4py. With this technique, you can determine for what volatility the Black–Scholes equation price equals the market price. This volatility is then denoted as the implied volatility observed in the market. Brian fitted varying degrees of polynomials to the volatility curves, then examined the volatility surface and its sensitivity with respect to the interest rate. See the full presentation in the video below:

Brian Spector of NAG demonstrates a technique and script for calculating Implied Volatility using Python’s Pandas Library at the March 2014 NYQPUG Meetup.

Implied Volatility Plot

An interactive Implied Volatility plot with Numpy, Pandas, Chaco, Matplotlib and nag4py

Then Aaron Watters, scientific software developer at Enthought, presented an overview of replacing VBA with Python in Excel using the PyXLL package. Instead of uncontrolled spreadsheet versions spreading across an organization, PyXLL allows you to load centrally-managed Python code and execute it in Excel, giving you the full breadth and power of the Python analytic computing ecosystem within the familiar user interface of Excel. Aaron showed a demo of a tool in Excel where he could browse his disk usage graphically.

Enthought: Chaco GUI in Excel

Chaco GUI running in Excel with data recalculating live in the spreadsheet

For those looking to get their latest Python models and algorithms out to Excel users, PyXLL greatly streamlines the process. See Aaron’s full demo of the functionality below:

Aaron Watters of Enthought presented an overview of replacing VBA with Python for Excel with PyXLL at the March 2014 NYQPUG Meetup.

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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 my_pyxll_module.py 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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