That title deserves an explanation. This note will look at the Theil Sen estimator for robust regression. I’m going to use the UCI Machine Learning abalone data set to compare this technique with Ordinary Least Squares. This one is via a Colab notebook, all is explained here.
What’s a stock master? It’s database, that contains data on stocks. It is also the master or authoritative, at least for me, source of that data. What kind of data exactly? Prices and fundamentals (and maybe economic time series). This post is going to document the data sources and tools used in building this database. The repo for the project is here. Motivation Firstly, why do I need a database that containing this type of information?
This is a quick post about intra-portfolio correlation. Intra-portfolio correlation (“IPC”) is defined as a weighted average for all unique pairwise correlations within a portfolio. It has typically been used to measure a portfolio’s diversification. That’s not what I’m interested in however. I’m looking at IPC as a potential technical trading indicator. The idea being that an increase or decrease in the co-movement of a group of stocks (or the market as a whole for that matter) may say something about their future returns.
The next instalment of the Dataquest guided projects. This one covers the datetime module and like the last, heavily utilises loops and dictionaries. Next up its Numpy and pandas!!
The content below is a Jupyter Notebook converted to markdown following the instructions of this blog. This allows for publishing of Jupyter Notebook’s using blogdown. Everything appears to work except for the rendering of tables. That will be something to resolve. Table formatting notwithstanding, it doesn’t really look as nice as the standard Jupyter Notebook format. I’ve therefore linked the original here. Introduction This notebook will implement a rolling out of sample forecast on time series data.