Economic data from the Federal Reserve

Importing and Managing Financial Data in Python

Stefan Jansen

Instructor

Economic data from FRED

FRED logo

Federal Reserve Economic Data
500,000 series covering a range of categories:
- Economic growth & employment
- Monetary & fiscal policy
- Demographics, industries, commodity prices
- Daily, monthly, annual frequencies

Get data from FRED

fred lookup.png

¹ https://fred.stlouisfed.org/

Get data from FRED

fred lookup 2.png

¹ https://fred.stlouisfed.org/

Get data from FRED

fred lookup 3.png

¹ https://fred.stlouisfed.org/

Interest rates

from pandas_datareader.data import DataReader
from datetime import date

series_code = 'DGS10' # 10-year Treasury Rate

data_source = 'fred' # FED Economic Data Service

start = date(1962, 1, 1)

data = DataReader(series_code, data_source, start)

data.info()

DatetimeIndex: 15754 entries, 1962-01-02 to 2022-05-20
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
 --  ------  --------------  -----  
 0   DGS10   15083 non-null  float64
dtypes: float64(1)

Stock prices: Visualization

.rename(columns={old_name: new_name})

series_name = '10-year Treasury'
data = data.rename(columns={series_code: series_name})

data.plot(title=series_name); plt.show()

New visualization

Combine stock and economic data

start = date(2000, 1, 1)
series = 'DCOILWTICO' # West Texas Intermediate Oil Price

oil = DataReader(series, 'fred', start)

ticker = 'XOM' # Exxon Mobile Corporation
stock = DataReader(ticker, 'yanoo', start)

data = pd.concat([stock[['Close']], oil], axis=1)

data.info()

DatetimeIndex: 5841 entries, 2000-01-03 to 2022-05-23
Data columns (total 2 columns):
 #   Column      Non-Null Count  Dtype  
 --  ------      --------------  -----  
 0   Close       5634 non-null   float64
 1   DCOILWTICO  5615 non-null   float64

Combine stock and economic data

data.columns = ['Exxon', 'Oil Price']
data.plot()
plt.show()

Let's practice!

Importing and Managing Financial Data in Python