nubra-iv-monitor

Real-time intraday ATM Implied Volatility monitor for NSE options, built on the Nubra SDK.

Tracks the IV of the at-the-money strike for any number of underlyings simultaneously, plots them on a single normalised chart, and fires a callback with a structured DataFrame on every candle close.

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Install

pip install nubra-iv-monitor

Requires Python 3.12 and an active Nubra account with API access.

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Quick start

from nubra_python_sdk.start_sdk import InitNubraSdk, NubraEnv
from nubra_python_sdk.refdata.instruments import InstrumentData
from nubra_python_sdk.marketdata.market_data import MarketData
from nubra_iv_monitor import monitor_iv

nubra = InitNubraSdk(NubraEnv.PROD, env_creds=True)

monitor_iv(
    stocks          = ["NIFTY", "BANKNIFTY", "RELIANCE"],
    market_data_obj = MarketData(nubra),
    instruments_obj = InstrumentData(nubra),
    interval        = "5m",
)

This opens a live chart and prints an IV table to the terminal every 5 minutes, aligned to the NSE candle grid (09:15 open).

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Authentication

The library does not handle authentication — you initialise the Nubra SDK yourself and pass the objects in.

from nubra_python_sdk.start_sdk import InitNubraSdk, NubraEnv

# Reads PHONE_NO and MPIN from a .env file
nubra = InitNubraSdk(NubraEnv.PROD, env_creds=True)

.env file format:

PHONE_NO="9999999999"
MPIN="1234"

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Parameters

monitor_iv(
    stocks,           # list[str]   — underlying symbols
    market_data_obj,  # MarketData  — authenticated Nubra instance
    instruments_obj,  # InstrumentData — authenticated Nubra instance
    interval   = "1m",    # str   — candle interval (see below)
    intraday   = True,    # bool  — fetch today's session only
    continuous = True,    # bool  — keep refreshing on candle closes
    plot       = True,    # bool  — show live matplotlib chart
    on_update  = None,    # callable(df) — callback fired every pass
)

interval options

Value	Candle size	Grid ticks from 09:15
"1m"	1 minute	09:16, 09:17, 09:18 …
"3m"	3 minutes	09:18, 09:21, 09:24 …
"5m"	5 minutes	09:20, 09:25, 09:30 …
"15m"	15 minutes	09:30, 09:45, 10:00 …
"30m"	30 minutes	09:45, 10:15, 10:45 …
"1h"	1 hour	10:15, 11:15, 12:15 …

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Live chart

When plot=True (default), a matplotlib window opens showing all stocks on the same graph.

• Y-axis: IV change from open (%) — all lines start at 0 so stocks with very different absolute IV levels can be compared directly.
• X-axis: Time in IST.
• Each line is labelled at its endpoint with the stock name.
• The chart redraws on every candle close and saves a snapshot to iv_monitor.png.

Set plot=False for headless / server environments.

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DataFrame output via on_update

Pass a callback to receive a long-format DataFrame on every candle close:

def on_update(df):
    print(df)

monitor_iv(["NIFTY", "HDFCBANK"], market_data, instruments,
           interval="5m", on_update=on_update)

DataFrame schema

Column	Type	Description
time	datetime (IST, tz-aware)	Candle timestamp
stock	str	Underlying symbol, e.g. "NIFTY"
avg_iv_pct	float	Average of CE + PE ATM IV in % (e.g. 18.42)
delta_from_open	float	IV change since market open in % points (e.g. -1.30)
ce_symbol	str	ATM call option symbol, e.g. "NIFTY2611324500CE"
pe_symbol	str	ATM put option symbol, e.g. "NIFTY2611324500PE"
current_price	float	Underlying spot price in ₹ at time of fetch
atm_strike	float	ATM strike in ₹

Sample output

                       time   stock  avg_iv_pct  delta_from_open         ce_symbol         pe_symbol  current_price  atm_strike
2026-05-12 09:20:00+05:30   NIFTY       18.42             0.00  NIFTY2611324500CE  NIFTY2611324500PE       24487.30     24500.0
2026-05-12 09:25:00+05:30   NIFTY       18.10            -0.32  NIFTY2611324500CE  NIFTY2611324500PE       24487.30     24500.0
2026-05-12 09:30:00+05:30   NIFTY       17.85            -0.57  NIFTY2611324500CE  NIFTY2611324500PE       24487.30     24500.0
2026-05-12 09:20:00+05:30  HDFCBANK     22.10             0.00  HDFCBANK26MAY1800CE HDFCBANK26MAY1800PE     1812.50      1800.0

The DataFrame contains all historical candles from 09:15 to now for every stock in a single flat table — suitable for filtering, alerting, database writes, or further analysis.

Accessing the latest value per stock

def on_update(df):
    latest = df.sort_values("time").groupby("stock").last().reset_index()
    print(latest[["stock", "avg_iv_pct", "delta_from_open"]])

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Continuous vs one-shot

# Run once and exit
monitor_iv([...], market_data, instruments, continuous=False)

# Run continuously until you stop the script (Ctrl+C)
monitor_iv([...], market_data, instruments, continuous=True)

When continuous=True and the market is closed, the monitor waits 60 seconds and rechecks. It runs automatically when the market opens.

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Requirements

• Python = 3.12
• nubra-sdk >= 0.3.8
• pandas >= 2.0
• matplotlib >= 3.7

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