Scrape historical economic data from investing.com

The code from here shows nicely how to extract economic data from https://www.investing.com/economic-calendar/. However, the code just scrapes the data from the current economic data table that is from today. There is a calendar icon where the timeframe can be set manually and past economic events can be viewed (highlighted red on the screenshot):

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Is there a way to scrape the past econonomic event data? So far, I have not really an idea …

Answer

I’m not 100% sure if you are looking excactly for this BUT have you tried investpy? It is a great python module, that seems to do exactly what you are trying to achieve but with less pain.

investpy is a Python package to retrieve data from Investing.com… investpy allows the user to download both recent and historical data from all the financial products indexed at Investing.com. It includes data from all over the world, from countries such as United States, France, India, Spain, Russia, or Germany, amongst many others.

pip install investpy

and then use it as seen below (and also here):

import investpy

data = investpy.economic_calendar(
    from_date='12/09/2021',
    to_date  ='13/09/2021'
)
print(data.head())
       id        date   time                  zone currency importance                                     event  actual forecast previous
0  436810  12/09/2021  09:59  united arab emirates      AED        low         Private Sector Loans (YoY)  (Jul)  -1.80%     None   -1.20%
1  435581  13/09/2021  01:45           new zealand      NZD        low                          FPI (MoM)  (Aug)    0.3%     None     1.3%
2  435584  13/09/2021  02:50                 japan      JPY     medium  BSI Large Manufacturing Conditions  (Q3)     7.0     None     -1.4
3  435583  13/09/2021  02:50                 japan      JPY        low                          PPI (YoY)  (Aug)    5.5%     5.6%     5.6%
4  435582  13/09/2021  02:50                 japan      JPY        low                          PPI (MoM)  (Aug)    0.0%     0.2%     1.1%

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