The pandas-datareader Python library covers a number of APIs with global fundamental macro- and industry data sources including the following (for a full list see Pandas Datareader - data sources ):
- St. Louis FED (FRED): Federal Reserve data on the U.S. economy and financial markets
- Fama/French data library : market data on portfolios capturing returns on key risk factors like size, value, and momentum by industry
- Yahoo Finance : retrieve daily stock prices, historical corporate actions (dividends and stock splits) from Yahoo Finance
- World Bank: global database with economic/social indicators and demographics.
This post is part of a series on using Pandas datareader to retrieve financial data:
- Using Python and Pandas Datareader to retrieve financial data - part 1: Federal Reserve Data (FRED)
- Using Python and Pandas Datareader to retrieve financial data - part2: Fama & French library
- Using Python and Pandas Datareader to retrieve financial data - part 3: Yahoo Finance
In this post I will focus on retrieving data from FRED using pandas-datareader. Federal Reserve Economic Data (FRED) - https://fred.stlouisfed.org/ is a database maintained by the Federal Reserve Bank of St. Louis. It has more than 800.000 data time series covering categories such as Economic growth & employment, monetary & fiscal policy, demographics, industries, commodity prices at different frequencies (daily, monthly, annual). One of the interesting time series you can find here are 3-month Treasury Bill Secondary Market rate (TB3MS) or 1-year US Treasury bills which are used a proxy for the risk free rate in financial modeling.
There is however some missing data on the TB1YR - so I will be using the TB3MS (3 Month) in my next example. You will notice that all time series are identified by a short abbreviation that you can find by searching on the FRED website.
References:
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