Posts

Showing posts from July, 2017

Value-at-Risk (VaR) Calculator Class in Python

Image
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.

Features:
Requires data frame of stock returns, factor returns, and stock weightsExpose a method to calculate and return a single VaR number for different variance typesExpose a method to calculate and return an array of VaR values by confidence levelExpose a method to calculate and plot an array of VaR values by confidence level Still to do:
Dynamic factor usage Note: Data to valida…

Calculating Value at Risk (VaR) with Python or R

Image
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 returns 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 The modules:
In R: Financial Risk - Calculating Value At Risk (VaR) with R In Python: Financial Risk - Calculating Value At Risk (VaR) with Python

Review: Make Your Own Neural Network

Image
As part of understanding neural networks I was reading Make Your Own Neural Network by Tariq Rashid. A review is below:

Make Your Own Neural Network by Tariq Rashid
My rating: 4 of 5 stars

The book itself can be painful to work through, as it is written for a novice, not just in algorithms and data analysis, but also in programming. For the neural network aspect, it jumped between overly simplistic and complicated, while providing neither in enough detail. That said, by the end I found it a worthwhile dive into neural networks, since once it got to the programming structure, it all made sense, but only because I stuck with it.

View all my reviews