nubra-ref-data 0.1.0

Reference-data helpers for Nubra InstrumentData option, future, and underlying DataFrames

nubra_ref_data

nubra_ref_data is a small helper package for Nubra InstrumentData users who want filtered pandas DataFrames for:

• the underlying row
• futures rows
• option rows by expiry bucket and strike levels
• one combined DataFrame containing all of the above

It is designed to work with:

from nubra_python_sdk.refdata.instruments import InstrumentData

instruments = InstrumentData(nubra)

Install

pip install nubra_ref_data

Functions

• underlying_data(instruments, underlying, exchange="NSE")
• futures_data(instruments, underlying, exchange="NSE")
• options_data(instruments, underlying, exchange="NSE", expiry_bucket="week0", levels=10, option_side="BOTH")
• all_data(instruments, underlying, exchange="NSE", expiry_bucket="week0", levels=10, option_side="BOTH")

Example

from nubra_python_sdk.start_sdk import InitNubraSdk, NubraEnv
from nubra_python_sdk.refdata.instruments import InstrumentData

from nubra_ref_data import all_data, options_data


nubra = InitNubraSdk(NubraEnv.UAT, env_creds=True)
instruments = InstrumentData(nubra)

df_all = all_data(
    instruments=instruments,
    underlying="NIFTY",
    exchange="NSE",
    expiry_bucket="week0",
    levels=8,
    option_side="BOTH",
)

df_ce = options_data(
    instruments=instruments,
    underlying="NIFTY",
    exchange="NSE",
    expiry_bucket="month",
    levels=5,
    option_side="CE",
)

df_sensex = all_data(
    instruments=instruments,
    underlying="SENSEX",
    exchange="BSE",
    expiry_bucket="week0",
    levels=6,
    option_side="BOTH",
)

Behavior

• week0, week1, week2, week3, and week4 resolve against the sorted available option expiries for that underlying.
• month resolves to the first month-end expiry available in the option data.
• levels picks the nearest strikes using underlying_prev_close.
• option_side="BOTH" only selects strikes where both CE and PE exist.
• all_data() returns rows in this order: underlying, futures, then options.
• If the underlying cash/index row is missing from the instruments master, a placeholder UNDERLYING row is added.
• The returned DataFrames preserve the original instrument columns only. No helper columns are added.