Daily Historical Rate for all Exchange Currency for USD via Jupyter Notebook

Question

We are extracting all of the Exchange rate - Daily for all currencies paired with with USD.

Timeframe requirement would be 20 to 40 years.

Please see attached excel file extracted using Workspace Excel Add-in.

This is only limited to one year time range due to limitations. but we are looking for a samples API script that works with Jupyter Notebook that can run the similar data with this WS Excel file.

What is the recommended API Script that can run this data even for chunks or partial depending on time range (if 20-40 years daily data is too much, extraction per 1 year, etc is okay)

Thank you Team!

Oriana Rahman-15895428_ExchangeRates.xlsx

Jirapongse · Answer

You can change the start and end dates in the ld.get_history call.

df = ld.get_history(

    universe = chain.constituents,

    fields = ['BID'],

    start = '2025-04-01',

    end = '2026-04-01',

    interval = 'daily')

Christian_Ada · Answer

Hello @Jirapongse Thank you so much for the script

What would be the correct script based on the one you provided, if we want to retrieve a specific timespan instead?

For example: 01-April-2026 to 01-April-2006 or any shorter timespan that we can customize to retrieve data by chunks?

The one we are using for now is Workspace Codebook app.

Current script we use for relative date (based on your previous input)

from datetime import date

from dateutil.relativedelta import relativedelta

import lseg.data as ld

from lseg.data.discovery import Chain

import pandas as pd

ld.open_session()

end_date = date.today()

start_date = end_date - relativedelta(years=1)

chain = Chain("0#FX=")

name = ld.get_data(

universe = chain.constituents,

fields = ['CF_NAME'],

)

df = ld.get_history(

universe = chain.constituents,

fields = ['BID'],

start = start_date.strftime('%Y-%m-%d'),

end = end_date.strftime('%Y-%m-%d'),

interval = 'daily')

df.ffill()

pd.set_option('display.max_rows', None)

pd.set_option('display.max_columns', None)

display(df)

Thank you!

Jirapongse · Answer

@Christian_Ada

Thank you for reaching out to us.

Typically, the client can use the LSEG Data Library for Python to extract historical data through the Workspace desktop session.

0#FX= is a chain RIC. Therefore, the client can use the Chain object in the library to expand the chain RIC and then use the get_history method to retrieve historical data. For example:

from datetime import date
from dateutil.relativedelta import relativedelta
import lseg.data as ld
from lseg.data.discovery import Chain

ld.open_session()

end_date = date.today()
start_date = end_date - relativedelta(years=1)

chain = Chain("0#FX=")

name = ld.get_data(
    universe = chain.constituents,
    fields = ['CF_NAME'],   
)

df = ld.get_history(
    universe = chain.constituents,
    fields = ['BID'],
    start = start_date.strftime('%Y-%m-%d'),
    end = end_date.strftime('%Y-%m-%d'),
    interval = 'daily'
)
df.ffill()

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