Processing Financial Data with FRED API: Difference between revisions

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=Procedure=
=Procedure=


Import the package and establish a connection to the FRED backend, providing the API Key obtained as described here.
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]].


=Code Examples=
==Initialization==
<syntaxhighlight lang='py'>
<syntaxhighlight lang='py'>
from fredapi import Fred
from fredapi import Fred
Line 17: Line 14:
</syntaxhighlight>
</syntaxhighlight>


==Search==
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'>
<syntaxhighlight lang='py'>
fred.search("text", limit=1000, order_by=None, sort_order=None, filter=None)
s = fred.get_series(series_id="SP500")
</syntaxhighlight>
</syntaxhighlight>
Do a full text search for series in the FRED data set. Returns the results as a [[Pandas_Concepts#Data_Frame|data frame]].


==Get Series==
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)

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