Tips for Staying Employed as an Older Developer
A response to an article Tips for Staying Employed as an Older Developer
A bit about myself, older and working as a developer, team lead and project manager, writing here to add to the options for staying relevant, and how to let the world know about it.
- GitHub - I have several libraries in C# and F#, so that others can directly use and evaluate my code
- NuGet - Packaged versions of code shared on GitHub
- Blogs - lately I have been learning data languages, and have several, focused on design patterns and algorithms, as well as one focused on data analysis using R, Python, and F#
- Websites - I have several sites, along with blogs, all accessible from a primary site, James Igoe. This site has links to other sites and blogs, one of which is an older site where I share code as downloads - this site predates GitHub - in my core languages, VBA, C# and SQL, tools for doing programming interviews, as well as cheat sheets.
- Reposts of career and tech-related articles on LinkedIn, GooglePlus (communities), Twitter, Facebook (page)
- Training - Yes, like others I am always learning, but I also share the material I work through and my opinion about it, meaning writing book reviews and sharing my opinion on courses from Pluralsight.
Value-at-Risk (VaR) Calculator Class in Python
As part of my self-development, I wanted to rework a script, which are typically one-offs, and turn it into a reusable component, although there are existing packages for VaR. As such, this is currently a work in progress. This code is a Python-based class for VaR calculations
, and for those unfamiliar with VaR, it is an acronym for value at risk, the worst case loss in a period for a particular probability. It is a reworking of prior work with scripted VaR calculations
, implementing various high-level good practices, e.g., hiding/encapsulation, do-not-repeat-yourself (DRY), dependency injection, etc.
- Requires data frame of stock returns, factor returns, and stock weights
- Calculate and return a single VaR number for different variance types
- Calculate and return an array of VaR values by confidence level
- Calculate and plot an array of VaR values by confidence level
Still to do:
Note: Data to validate this class is available from my Google Drive Public folder
Calculating Value at Risk (VaR) with Python or R
The following modules linked below are based on a Pluralsight course, Understanding and Applying Financial Risk Modeling Techniques
, and while the code itself is nearly verbatim, this is mostly for my own development, working through the peculiarities of Value at Risk (VaR) in both R and Python, and adding commentary as needed.
The general outline of this process is as follows:
Load and clean Data
Calculate historical variance
Calculate systemic, idiosyncratic, and total variance
Develop a range of stress variants, e.g. scenario-based possibilities
Calculate VaR as the worst case loss in a period for a particular probability