Beta weighting is a tool that allows us to approximate our positions in terms of the same benchmark.
Today we learn how to beta weight your portfolio in python. We will use three equivalent methods to estimate the beta coefficients of each security and then progress onto how you can beta weight your portfolio delta’s (the change in value given a unit change of the underlying) to get an approximation for how your portfolio will change with respect to a movement in your benchmark (whether it be a market index or specific security).
We will calculate beta coefficients directly using the capital asset pricing model (CAPM) definition, and through some linear algebra tools using linear regression on individual stocks and then aggregated using a closed form solution for linear regression with a specific minimisation function.
00:00 Intro
01:04 Get stock data
04:26 Beta coefficient CAPM calculation
09:02 Beta coefficient linear regression (scipy stats module)
11:25 Beta coefficient linear algebra (closed form solution)
17:30 Beta weighted stock portfolio
24:45 What about options?
30:40 Beta weighting the Delta’s
35:23 Why it’s useful
As a high-level programming language, Python is a great tool for financial data analysis, with quick implementation and well documented API data sources, statistical modules and other frameworks related to the financial industry. We will be using Jupyter Lab as an interactive web browser editor for this series due to ease of use and presenting code in a live notebook is ideal for this tutorial series.
This is the fifth video of many on the topic of Python for Finance. The series will include general techniques used for financial analysis and act as an introduction for more in-depth tutorials that we may explore later (such as time series modelling, building financial dashboards, machine learning ect.).
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