The tidyfinance package

tidyfinance is a package that contains a set of helper functions for empirical research in financial economics, addressing a variety of topics covered in this book. We designed the package to provide easy shortcuts for the applications that we discuss in the book. If you want to inspect the details of the package or propose new features, feel free to visit the package repository on GitHub.

R

The R package lives in the r-tidyfinance repository.

Python

The Python package lives in the py-tidyfinance repository.

Installation

R

You can install the released version of tidyfinance from CRAN via:

install.packages("tidyfinance")

You can install the development version of tidyfinance from GitHub (which might not be fully tested) via:

# install.packages("pak")
pak::pak("tidy-finance/r-tidyfinance")

Python

You can install the released version of tidyfinance from PyPI via:

pip install tidyfinance

You can install the development version of tidyfinance from GitHub (which might not be fully tested) via:

pip install "git+https://github.com/tidy-finance/py-tidyfinance"

Usage

Throughout the book, we refer to the corresponding features of the tidyfinance package. At a high level, the package groups its functionality into a few families:

• Data access via a single download_data() entry point, which dispatches on a domain (e.g., "WRDS", "Fama-French", "Goyal-Welch", "FRED", "Stock Prices", "Index Constituents", "Tidy Finance") and a dataset argument. The package also ships a domain = "Pseudo Data" for generating pseudo data with the same schema as the WRDS datasets, which is handy for examples and testing without WRDS access. You can list everything that is available via list_supported_datasets().
• Portfolio sorts through assign_portfolio(), compute_breakpoints(), compute_portfolio_returns(), compute_long_short_returns(), and the convenience wrapper implement_portfolio_sort(), configured via helpers such as breakpoint_options(), data_options(), and filter_options().
• Estimation with estimate_model(), estimate_betas(), and estimate_fama_macbeth(), plus utilities like create_summary_statistics(), winsorize(), trim(), and add_lagged_columns().

R

For the precise signatures and the latest changes, consult the package website.

Python

For the precise signatures and the latest changes, consult the library website.

Feature requests

We are curious to learn in which direction we should extend the package, so please consider opening an issue in the package repository. For instance, we could support more data sources, add more parameters to the family of functions for data downloads, or we could put more emphasis on the generality of portfolio assignment or other modeling functions. Moreover, if you discover a bug, we are very grateful if you report the issue in our repository.