tidyfinance 0.1.1

Tidy Finance Helper Functions

tidyfinance

Helper functions for empirical research in financial economics, addressing a variety of topics covered in Scheuch, Frey, Voigt, and Weiss (2024). The package is designed to provide shortcuts for issues extensively discussed in the book, facilitating easier application of its concepts. For more information and resources related to the book, visit tidy-finance.org/python.

Installation

You can install the release version from PyPI:

pip install tidyfinance

You can install the development version from GitHub:

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

Download Open Source Data

The main functionality of the tidyfinance package centers around data download. You can download most of the data that we used in Tidy Finance with R using the download_data() function or its children.

import tidyfinance as tf

The function always requires a domain argument and depending on the domain typically also a dataset. For instance, to download monthly Fama-French factors, you have to provide the dataset name according to pdr.famafrench.get_available_datasets():

tf.download_data(
  domain="factors_ff",
  dataset="F-F_Research_Data_5_Factors_2x3_daily",
  start_date="2000-01-01", 
  end_date="2020-12-31"
)

For q factors, you provide the relevant file name:

tf.download_data(
  domain="factors_q",
  dataset="q5_factors_monthly",
  start_date="2000-01-01", 
  end_date="2020-12-31"
)

To download the Welch and Goyal (2008) macroeconomic predictors for monthly, quarterly, or annual frequency:

tf.download_data(
  domain="macro_predictors",
  dataset="monthly",
  start_date="2000-01-01", 
  end_date="2020-12-31"
)

To download data from Open Source Asset Pricing (OSAP):

tf.download_data(
  domain="osap",
  start_date="2020-01-01", 
  end_date="2020-12-31"
)

To download multiple series from the Federal Reserve Economic Data (FRED):

tf.download_data(
  domain="fred",
  series=["GDP", "CPIAUCNS"], 
  start_date="2020-01-01", 
  end_date="2020-12-31"
)

To download stock prices from Yahoo Finance:

tf.download_data(
  domain="stock_prices",
  symbols=["AAPL", "MSFT"], 
  start_date="2020-01-01", 
  end_date="2020-12-31"
)

To download index constituents from selected ETF holdings:

tf.download_data(
  domain="constituents",
  index="S&P 500"
)

Download WRDS Data

To access data from the Wharton Research Data Services (WRDS), you need to set your credentials first:

tf.set_wrds_credentials()

To download monthly CRSP data:

tf.download_data(
  domain="wrds",
  dataset="crsp_monthly", 
  start_date="2020-01-01", 
  end_date="2020-12-31"
)

To download annual (or quaterly) Compustat data:

tf.download_data(
  domain="wrds",
  dataset="compustat_annual", 
  start_date="2020-01-01", 
  end_date="2020-12-31"
)

To download the CRSP-Compustat linking table:

tf.download_data(
  domain="wrds",
  dataset="ccm_links"
)

To download bond characteristics from Mergent FISD:

tf.download_data(
  domain="wrds",
  dataset="fisd"
)

To download Enhanced TRACE data for selected bonds:

tf.download_data(
  domain="wrds",
  dataset="trace_enhanced",
  cusips=["00101JAH9"],
  start_date="2019-01-01", 
  end_date="2021-12-31"
)

Other Helpers

We include functions to check out content from tidy-finance.org:

tf.list_tidy_finance_chapters()
tf.open_tidy_finance_website("capital-asset-pricing-model")

We also include (experimental) functions that can be used for different applications, but note that they might heavily change in future package versions as we try to make them more general:

# Create summary statistics
help(tf.create_summary_statistics)

# Assign portfolios
help(tf.assign_portfolio)

# Estimate betas
help(tf.estimate_betas)

# Estimate Fama-MacBeth 
help(tf.estimate_fama_macbeth)

# Add lag columns
help(tf.add_lag_columns)

# Winsorize or trim 
help(tf.winsorize)
help(tf.trim)