Nubra API - Python SDK for Algorithmic Trading on Indian Stock Markets

nubra-talib is built for traders and developers who want technical indicators without setup pain.

This guide explains:

Why nubra-talib is important
How to install it from TestPyPI
How to use it with simple, practical examples

Why nubra-talib Is Important#

Most strategy builders spend too much time on environment issues before writing actual trading logic.

nubra-talib helps by giving you:

A cleaner TA-Lib installation experience
Faster setup for indicator-based strategies
Less friction between idea and backtest

That means you can move faster from:

data -> indicators -> signals -> execution

Prerequisites#

Before installation, ensure:

Python 3.10+ installed
pip is available in your terminal

Step 1: Install nubra-talib from TestPyPI#

Use this command:

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple nubra-talib

Step 2: Real Example with Nubra SDK Market Data#

If you are already using Nubra SDK, you can pull historical candles and add indicators in a few lines.

from nubra_python_sdk.marketdata.market_data import MarketData
from nubra_python_sdk.start_sdk import InitNubraSdk, NubraEnv

from nubra_talib import to_ohlcv_df, add_talib


def main():
    # Initialize SDK (use UAT for testing, PROD for live)
    nubra = InitNubraSdk(NubraEnv.PROD)
    md_instance = MarketData(nubra)

    result = md_instance.historical_data({
        "exchange": "NSE",
        "type": "STOCK",
        "values": ["ASHOKLEY", "TMPV"],
        "fields": ["close", "high", "low", "open", "cumulative_volume"],
        "startDate": "2026-01-01T11:01:57.000Z",
        "endDate": "2026-02-16T06:13:57.000Z",
        "interval": "3m",
        "intraDay": False,
        "realTime": False,
    })

    # Converts to IST and rupee prices by default
    df = to_ohlcv_df(result, symbol="ASHOKLEY", interval="3m")

    df = add_talib(
        df,
        funcs={
            "RSI": {"timeperiod": 14},
            "EMA": {"timeperiod": 21},
            "CCI": {"timeperiod": 14},
        },
    )

    print(df.tail())


if __name__ == "__main__":
    main()

Example output:

timestamp                  open    high     low   close   volume        rsi         ema         cci
2026-02-16 11:30:00+05:30 204.77  205.24  204.76 204.96  93818.0  44.475316  205.173272 -123.537259
2026-02-16 11:33:00+05:30 204.97  205.08  204.66 204.79  51620.0  41.298525  205.138429 -154.290271
2026-02-16 11:36:00+05:30 204.75  204.96  204.75 204.85  17842.0  42.850098  205.112209 -117.266927
2026-02-16 11:39:00+05:30 204.86  204.97  204.65 204.71  35121.0  40.181341  205.075644 -117.205064
2026-02-16 11:42:00+05:30 204.71  204.88  204.65 204.71  19405.0  40.181341  205.042404 -104.804139

This is the easiest path from:

historical market data -> cleaned OHLCV DataFrame -> ready-to-use indicators

Step 3: Add It to Strategy Logic Quickly#

Example signal conditions:

Buy when RSI < 30 and close crosses above SMA
Exit when RSI > 70 or close falls below SMA

This gives you a simple indicator-driven structure you can backtest and iterate.

Common Installation Checks#

If import fails, check:

You are inside the correct virtual environment
pip installed into the same Python you are running
You used both --index-url and --extra-index-url
Python version is compatible

Helpful debug command:

python -m pip --version
python --version

Why This Is Easy for Developers#

nubra-talib keeps the workflow simple:

Install with one command
Import in one line
Use production-standard indicators immediately

So you spend less time fixing setup issues and more time improving strategy quality.

Conclusion#

If you want a faster way to work with technical indicators in Python, nubra-talib gives a clean path:

Install from TestPyPI
Verify import
Start building strategy signals immediately

How to Install and Use nubra-talib in Minutes