An introduction to accessing financial data in EDGAR, using Python

Some sources of financial data can be expensive or difficult to find. For example, some is only available from exchanges or vendors who charge a hefty fee for access. However, the financial industry is also heavily regulated, and one of its main regulators provides free access to its data. The (U.S. Securities and Exchange Commission)[https://www.sec.gov] … Continue reading An introduction to accessing financial data in EDGAR, using Python

Analyzing stock data near events with pandas

Stock returns can be heavily impacted by certain events. Sometimes these events are unexpected or a surprise (natural disasters, global pandemics, terrorism) and other times they are scheduled (presidential elections, earnings announcements, financial data releases). We can use pandas to obtain financial data and see the impacts of events the returns of stocks. In my … Continue reading Analyzing stock data near events with pandas

Financial market data analysis with pandas

Pandas is a great tool for time series analysis of financial market data. Because pandas DataFrames and Series work well with a date/time based index, they can be used effectively to analyze historical data. By financial market data, I mean data like historical price information on a publicly traded financial instrument. However, any sort of … Continue reading Financial market data analysis with pandas

Parameterizing and automating Jupyter notebooks with papermill

Have you ever created a Jupyter notebook and wished you could generate the notebook with a different set of parameters? If so, you've probably done at least one of the following: Edited the variables in a cell and reran the notebook, saving off a copy as neededSaved a copy of the notebook and maybe hacked … Continue reading Parameterizing and automating Jupyter notebooks with papermill