Pulling Cotton Continuous data

Asher · 2025-03-17T10:50:30+00:00

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Hi I have this code, it doesn't pull the EXPIR_DATE and CRT_MNTH prior to 2021, please fix it

import lseg.data as ld
import pandas as pd

ld.open_session()

continuous_rics = [
"CTc1", "CTc2", "CTc3", "CTc4", "CTc5",
"CTc6", "CTc7", "CTc8", "CTc9", "CTc10"
]

df_continuous = ld.get_history(continuous_rics, 'EXPIR_DATE', 'CRT_MNTH',
start='01-Jan-2020', end='13-Mar-2025', interval='daily')
print(df_continuous)

Could you please adapt it using the search API

Thanks

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Jirapongse

@Asher

Thank you for reaching out to us.

I assume that you would like to retrive all expiration dates for the cotton.

The code could be like this:

df = ld.discovery.search(
        view = ld.discovery.Views.SEARCH_ALL,
        filter = "TickerSymbol eq 'CT' and ExpiryDate ne null",
        select = "RIC,RCSAssetCategoryLeaf,DocumentTitle,ExpiryDate",
        top = 10000
    )
df

Asher

@Jirapongse
Is it possible to combine this with the continuous data? ie we append the expiration dates and contract months names with the continuous data?

raksina.samasiri

Hi @Asher ,

Is this what you're looking for?

Modifying result of search as below

import lseg.data as ld

ld.open_session()


df = ld.discovery.search(

        view = ld.discovery.Views.SEARCH_ALL,

        filter = "TickerSymbol eq 'CT' and ExpiryDate ne null",

        select = "RIC,RCSAssetCategoryLeaf,DocumentTitle,ExpiryDate",

        top = 10000

    )

df['ExpiryDateString'] = df['ExpiryDate'].astype(str)

df['ContractMonth'] = df['ExpiryDateString'].str[:4]+df['ExpiryDateString'].str[5:7]

df

Then sort data by ExpiryDate column

df[['RIC', 'ExpiryDate', 'ContractMonth']].sort_values('ExpiryDate')

I hope this helps and please let me know in case you have any further questions

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