Processing Financial Data with FRED API: Difference between revisions
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=Internal= | =Internal= | ||
* [[ | * [[FRED API]] | ||
* [[Pandas#Code_Examples|Pandas Code Examples]] | * [[Pandas#Code_Examples|Pandas Code Examples]] | ||
=Overview= | =Overview= | ||
This article describes the sequence of steps required to process financial data obtained from FRED API. | This article describes the sequence of steps required to process financial data obtained from FRED API. | ||
=Procedure= | |||
Import the package and establish a connection to the FRED backend, providing the API Key obtained as described [[FRED_API#Get_the_API_Key|here]]. | |||
<syntaxhighlight lang='py'> | |||
from fredapi import Fred | |||
fred = Fred(api_key='...') | |||
</syntaxhighlight> | |||
Search for a dataset using full text search. The result is a [[Pandas_DataFrame|DataFrame]]. | |||
<syntaxhighlight lang='py'> | |||
df = fred.search("S&P", limit=1000, order_by=None, sort_order=None, filter=None) | |||
</syntaxhighlight> | |||
The exploration yields a series with the ID "SP500". | |||
Retrieve the [[Pandas_Series|Series]]: | |||
<syntaxhighlight lang='py'> | |||
s = fred.get_series(series_id="SP500") | |||
</syntaxhighlight> | |||
The series is already time-indexed: | |||
<font size=-2> | |||
2013-10-07 1676.12 | |||
2013-10-08 1655.45 | |||
2013-10-09 1656.40 | |||
2013-10-10 1692.56 | |||
2013-10-11 1703.20 | |||
... | |||
2023-10-02 4288.39 | |||
2023-10-03 4229.45 | |||
2023-10-04 4263.75 | |||
2023-10-05 4258.19 | |||
2023-10-06 4308.50 | |||
Length: 2610, dtype: float64 | |||
</font> | |||
<syntaxhighlight lang='py'> | |||
s.index | |||
</syntaxhighlight> | |||
<font size=-2> | |||
DatetimeIndex(['2023-10-01', '2023-10-02', '2023-10-03', '2023-10-04', | |||
'2023-10-05', '2023-10-06', '2023-10-07', '2023-10-08', | |||
'2023-10-09'], | |||
dtype='datetime64[ns]', name='date', freq=None) | |||
</font> |
Latest revision as of 19:41, 8 October 2023
Internal
Overview
This article describes the sequence of steps required to process financial data obtained from FRED API.
Procedure
Import the package and establish a connection to the FRED backend, providing the API Key obtained as described here.
from fredapi import Fred
fred = Fred(api_key='...')
Search for a dataset using full text search. The result is a DataFrame.
df = fred.search("S&P", limit=1000, order_by=None, sort_order=None, filter=None)
The exploration yields a series with the ID "SP500".
Retrieve the Series:
s = fred.get_series(series_id="SP500")
The series is already time-indexed:
2013-10-07 1676.12 2013-10-08 1655.45 2013-10-09 1656.40 2013-10-10 1692.56 2013-10-11 1703.20 ... 2023-10-02 4288.39 2023-10-03 4229.45 2023-10-04 4263.75 2023-10-05 4258.19 2023-10-06 4308.50 Length: 2610, dtype: float64
s.index
DatetimeIndex(['2023-10-01', '2023-10-02', '2023-10-03', '2023-10-04', '2023-10-05', '2023-10-06', '2023-10-07', '2023-10-08', '2023-10-09'], dtype='datetime64[ns]', name='date', freq=None)