This Jupyter notebook provides an analysis of ETF returns data stored in an SQL database.
Specifically, it provides:
- An analysis of PYPL (which is a single asset in the ETF), including a DataFrame created from an SQL query of ETF database data, as well as interactive plots of daily and cumulative returns.
- DataFrames of PYPL data created from more advanced SQL queries that optimize data access of the database.
- An analysis of all holdings in the ETF taken together, including a DataFrame created from an SQL query and interactive plots.
The analysis can also be deployed as a web application.
This analysis is a Jupyter lab notebook that makes use of the following Python libraries:
- Numpy
- Pandas
- PyViz - hvPlot library
- SQLAlchemy
To use this notebook:
- Install Jupyter lab Version 2.3.1 and Python 3.7.
- Numpy, Pandas, and SQLAlchemy should already be included in the dev environment distribution. If not, install them.
- Install PyViz visualization package.
- Install hvPlot version 0.7.0 or later.
- Install NodeJS version 12 or later.
Open the notebook in Jupyter lab and you can rerun the analysis.
Here are examples of the type of analysis and interactive plots in this notebook, taken from a Voila web application deployment of this notebook:
Here is an example of a DataFrame created from an SQL Inner Join statement, taken from a Voila web application deployment of this notebook:
Michael Danenberg
MIT