finsets 0.0.4

Download and process datasets commonly used in finance research

finsets

Download and process datasets commonly used in finance research

Each module handles a different data source. Almost all submodules (other than utility ones) have a get_raw_data function that downloads the raw data and a process_raw_data function that processes the data into a pandas.DataFrame having, as index, either:

• A pandas.Period date reflecting the frequency of the data (for time-series datasets), or
• A pandas.MultiIndex with a panel identifier in the first dimension and a pandas.Period date in the second dimension (for panel datasets).

The period date in the index will be named following the pattern Xdate where X is the string literal representing the frequency of the data (e.g. Mdate for monthly data, Qdate for quarterly data, Ydate for annual data).

Documentation site.

GitHub page.

Install

pip install finsets

How to use

import finsets as fds

or

from finsets import fred, wrds, papers

Below, we very briefly describe each submodule. For more details, please see the documentation of each submodule (they provide a lot more functionality than presented here).

WRDS

Downloads and processes datasets from Wharton Research Data Services WRDS.

Each WRDS module handles a different library in WRDS (e.g. compa module for the Compustat Annual CCM file, crspm for the CRSP Monthly Stock file, etc.).

Before you use any of the wrds modules, you need to create a pgpass with your WRDS credentials. To do that, run

from finsets.wrds import wrds_api

db = wrds_api.Connection()

This will prompt you for your WRDS username and password. After you enter your credentials, if you don’t have a pgpass file already set up, it will ask you if you want to do that. Hit y and it will be automatically created for you. After this, you will never have to input your WRDS password.

You will still have to supply your WRDS username to functions that retrieve data from WRDS (all of them have a wrds_username parameter). If you don’t want to be prompted for the username for every download, save it under a WRDS_USERNAME environment variable:

• On Windows, in a Command Prompt:
  • setx WRDS_USERNAME "your_wrds_username_here"
• On Linux, in a terminal:
  • echo 'export WRDS_USERNAME="your_wrds_username_here"' >> ~/.bashrc && source ~/.bashrc
• On macOS, since macOS Catalina:
  • echo 'export WRDS_USERNAME="your_wrds_username_here"' >> ~/.zshrc && source ~/.szhrc
• On macOS, prior to macOS Catalina:
  • echo 'export WRDS_USERNAME="your_wrds_username_here"' >> ~/.bash_profile && source ~/.bash_profile

The functions in the wrds_ modules will close database connections to WRDS automatically. However, if you open a connection manually, as above (with wrds.Connection()) make sure you remember to close that connection. In our example above:

db.close()

Check the wrds_utils module for an introduction to some of the main utilities that come with the wrds package.

FRED

Downloads and processes datasets from the St. Louis FRED.

To use the functions in the fred module, you’ll need an API key from the St. Louis FRED.

Get one here and store it in your environment variables under the name FRED_API_KEY

Alternatively, you can supply the API key directly as the api_key parameter in each function in the fred module.

gdp = fred.fred.get_raw_data(['GDP'])

gdp['info']

	id	realtime_start	realtime_end	title	observation_start	observation_end	frequency	frequency_short	units	units_short	seasonal_adjustment	seasonal_adjustment_short	last_updated	popularity	notes
0	GDP	2023-11-15	2023-11-15	Gross Domestic Product	1947-01-01	2023-07-01	Quarterly	Q	Billions of Dollars	Bil. of $	Seasonally Adjusted Annual Rate	SAAR	2023-10-26 07:55:01-05	92	BEA Account Code: A191RC Gross domestic produ...

gdp['Q']

	GDP
1947-01-01	243.164
1947-04-01	245.968
1947-07-01	249.585
1947-10-01	259.745
1948-01-01	265.742
...	...
2022-07-01	25994.639
2022-10-01	26408.405
2023-01-01	26813.601
2023-04-01	27063.012
2023-07-01	27623.543

307 rows × 1 columns

PAPERS

Downloads and processes datasets made available by the authors of academic papers.

Each papers module handles a different paper. The naming convention is that the module’s name is made up of the last names of the authors and the publication year, separated by underscores. If more than two authors, all but the first author’s name is replaced by ‘etal’. For example, the module for the paper “Firm-Level Political Risk: Measurement and Effects” (2019) by Tarek A. Hassan, Stephan Hollander, Laurence van Lent, Ahmed Tahoun is named hasan_etal_2019.

papers.hassan_etal_2019.list_all_vars().head()

	name
0	gvkey
1	date
2	PRisk
3	NPRisk
4	Risk