Notebooks in (via knitr) and in Jupyter are often heavy in library calls. This is poor practice, not only for readers of the notebook but for the author as well. I have seen many Jupyter notebooks that do not include narrative text along with the Python code. For papers generated with knitr, the paper source supports reproducibility. There is a movement toward “reproducible research”, where the software and data for a paper are published along with the paper. As the Jupyter notebook associated with this page shows, this is not yet possible with Python (or at least I have not figured out how to do it). R includes a package named knitr, that supports the creation of publication-quality documents. This notebook contains some of the first steps in this journey. I also used R for my Masters degree project (see Value Factors Do Not Forecast Returns for S&P 500 Stocks)įor a variety of reasons, I am moving from R to Python. I have a Masters degree in Computational Finance and Risk Management from the University of Washington. The equations and images had to be copied into this page (Medium is not a great platform for technical publishing). The original notebook can be found here: This page is derived from a Python Jupyter notebook.
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