fred

Retrieve and process data from the St. Louis FRED

This module is a wrapper on the fred_api module, which itself is a fork of fredapi.

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.

from typing import List, Dict
import time
import copy

import pandas as pd

import pandasmore as pdm
from finsets.fred.fred_api import Fred
PROVIDER = 'Federal Reserve Economic Data (FRED)'
URL = 'https://fred.stlouisfed.org/'

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get_series_info

 get_series_info (series:str, api_key:str=None)

Get metadata from FRED for given series from FRED

Type Default Details
series str FRED series name
api_key str None FRED api key. If None, will use os.getenv(“FRED_API_KEY”)
Returns Series 
get_series_info('TB3MS')
id                                                                       TB3MS
realtime_start                                                      2023-11-12
realtime_end                                                        2023-11-12
title                        3-Month Treasury Bill Secondary Market Rate, D...
observation_start                                                   1934-01-01
observation_end                                                     2023-10-01
frequency                                                              Monthly
frequency_short                                                              M
units                                                                  Percent
units_short                                                                  %
seasonal_adjustment                                    Not Seasonally Adjusted
seasonal_adjustment_short                                                  NSA
last_updated                                            2023-11-01 15:21:08-05
popularity                                                                  78
notes                                Averages of Business Days, Discount Basis
dtype: object

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default_raw_vars

 default_raw_vars ()

List of FRED series that will be used in get_raw_data() if none are specified

print(default_raw_vars())
['TB3MS', 'DTB3', 'GS10', 'DGS10', 'GS1', 'DGS1', 'AAA', 'BAA', 'DAAA', 'DBAA', 'FEDFUNDS', 'DFF', 'CPIAUCSL', 'CPIAUCNS', 'INDPRO', 'IPB50001SQ', 'UNRATE', 'GDP', 'GDPC1', 'GNP', 'GNPC96', 'GDPPOT', 'USREC', 'RECPROUSM156N', 'CFNAI', 'UMCSENT', 'MICH', 'USEPUINDXM', 'USEPUNEWSINDXM', 'USEPUINDXD', 'VIXCLS', 'VXOCLS']

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parse_varlist

 parse_varlist (vars:List[str]=None, api_key:str=None)

Splits vars by frequency and returns dict with one pd.DataFrame per frequency

Type Default Details
vars typing.List[str] None list of variables requested by user; if None, will use default_raw_vars()
api_key str None FRED api key. If None, will use os.getenv(“FRED_API_KEY”)
Returns typing.Dict[str, list] 
varlist = parse_varlist(['AAA','GDP', 'INDPRO'])
varlist.keys()
dict_keys(['info', 'M', 'Q'])
varlist['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 AAA 2023-11-12 2023-11-12 Moody's Seasoned Aaa Corporate Bond Yield 1919-01-01 2023-10-01 Monthly M Percent % Not Seasonally Adjusted NSA 2023-11-01 10:17:02-05 79 These instruments are based on bonds with matu...
1 GDP 2023-11-12 2023-11-12 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...
2 INDPRO 2023-11-12 2023-11-12 Industrial Production: Total Index 1919-01-01 2023-09-01 Monthly M Index 2017=100 Index 2017=100 Seasonally Adjusted SA 2023-10-17 08:29:02-05 78 The industrial production (IP) index measures ... 
varlist['M']
['AAA', 'INDPRO']
varlist['Q']
['GDP']

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get_raw_data

 get_raw_data (vars:str=None, api_key:str=None)

Retrieves vars from FRED, splits them by frequency and returns dict with one pd.DataFrame per frequency

Type Default Details
vars str None FRED series name
api_key str None FRED api key. If None, will use os.getenv(“FRED_API_KEY”)
Returns typing.Dict[str, pandas.core.frame.DataFrame] 
rdat = get_raw_data(['AAA','GDP', 'INDPRO'])
rdat.keys()
dict_keys(['info', 'M', 'Q'])
rdat['M']
AAA INDPRO
1919-01-01 5.35 4.8665
1919-02-01 5.35 4.6514
1919-03-01 5.39 4.5170
1919-04-01 5.44 4.5976
1919-05-01 5.39 4.6245
... ... ...
2023-06-01 4.65 102.2924
2023-07-01 4.66 103.2895
2023-08-01 4.95 103.3170
2023-09-01 5.13 103.6115
2023-10-01 5.61 NaN

1258 rows × 2 columns

rdat['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

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process_raw_data

 process_raw_data (data:dict=None)

Processes data from FRED: cleans the date and sets it as index using pdm.setup_tseries

Type Default Details
data dict None keys are frequency str literals; same format as the output of get_raw_data
Returns DataFrame 
cdat = process_raw_data(rdat)
cdat.keys()
dict_keys(['info', 'M', 'Q'])
cdat['M']
dtdate AAA INDPRO
Mdate
1919-01 1919-01-01 5.35 4.8665
1919-02 1919-02-01 5.35 4.6514
1919-03 1919-03-01 5.39 4.5170
1919-04 1919-04-01 5.44 4.5976
1919-05 1919-05-01 5.39 4.6245
... ... ... ...
2023-06 2023-06-01 4.65 102.2924
2023-07 2023-07-01 4.66 103.2895
2023-08 2023-08-01 4.95 103.3170
2023-09 2023-09-01 5.13 103.6115
2023-10 2023-10-01 5.61 NaN

1258 rows × 3 columns

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