indian-market-data

Download NSE and MCX India market data as pandas DataFrames.
Bhavcopy · Nifty indices · F&O · Commodity spot prices · Works on AWS Lambda

pip install indian-market-data

What's included

Install individually or together:

pip install nse-archives            # NSE only
pip install mcx-data            # MCX only
pip install indian-market-data  # Both

NSE — Quick Start

from nsedata import nse
# or: from indianmarketdata import nse

# Daily prices
df = nse.get("capital_market", "equities_sme", "sec_bhavdata_full", "2026-05-22")
df = nse.get("capital_market", "indices", "ind_close_all", "2026-05-22")

# F&O
df = nse.get("derivatives", "equity", "fo_bhav_udiff", "2026-05-22")
df = nse.get("derivatives", "equity", "fo_secban", "2026-05-22")

# Debt
df = nse.get("debt", "corporate", "cbm_trd", "2026-05-22")

# Historical index + TRI (from niftyindices.com)
df = nse.get_historical_index("NIFTY 50", "01-Jan-2026", "31-Mar-2026")
df = nse.get_tri("NIFTY 50", "01-Jan-2026", "31-Mar-2026")

# Download to S3
nse.download("capital_market", "equities_sme", "sec_bhavdata_full", "2026-05-22",
             s3_bucket="my-bucket", s3_prefix="raw/nse/")

# 91 datasets across equities, F&O, debt, indices, EGR
nse.list_datasets()

86 datasets confirmed working on AWS Lambda (May 2026). Covers:

• Capital Market: Equities & SME (32), Indices (2), Mutual Fund (1), SLB (12)
• Derivatives: Equity F&O (8), Commodity (3), Currency (3), Interest Rate (9)
• Debt: Corporate (15), Debt Segment (4), Tri-Party Repo (1)
• EGR (1)

MCX — Quick Start

from mcxdata import mcx
# or: from indianmarketdata import mcx

# Today's spot prices — all 28 commodities
df = mcx.get_spot_recent()
# → Commodity, Unit, Location, Spot Price (Rs.), Up/Down, Date

# Single commodity
df = mcx.get_spot_recent(commodity="GOLD")

# Historical (requires specific commodity — ALL not supported by MCX)
df = mcx.get_spot_archive("2026-05-01", "2026-05-22", commodity="GOLD")
df = mcx.get_spot_archive("2026-05-01", "2026-05-22", commodity="SILVER")
df = mcx.get_spot_archive("2026-05-01", "2026-05-22", commodity="CRUDEOIL")

# Download to S3
mcx.download("spot", "market", "spot_recent",
             s3_bucket="my-bucket", s3_prefix="raw/mcx/")

# Available commodities (28 total)
mcx.list_commodities()
# → ['ALUMINI', 'ALUMINIUM', 'CARDAMOM', 'COPPER', 'COTTON', 'COTTONOIL',
#    'CPO', 'CRUDEOIL', 'CRUDEOILM', 'ELECDMBL', 'GOLD', 'GOLDGUINEA',
#    'GOLDM', 'GOLDPETAL', 'GOLDTEN', 'KAPAS', 'LEAD', 'LEADMINI',
#    'MENTHAOIL', 'NATGASMINI', 'NATURALGAS', 'NICKEL', 'SILVER', 'SILVERM',
#    'SILVERMIC', 'STEELREBAR', 'ZINC', 'ZINCMINI']

Combined Usage

from indianmarketdata import nse, mcx

date = "2026-05-22"

# NSE equity prices
nse_df = nse.get("capital_market", "equities_sme", "sec_bhavdata_full", date)

# MCX gold spot
mcx_df = mcx.get_spot_recent(commodity="GOLD")

print(f"NSE: {len(nse_df)} securities")
print(f"MCX Gold: ₹{mcx_df['Spot Price (Rs.)'].iloc[0]:,.2f}/10g")

AWS Lambda

Both packages are designed to work from Lambda:

# lambda_function.py
import json
from nsedata import nse
from mcxdata import mcx

def lambda_handler(event, context):
    date = event["date"]

    # NSE bhav
    nse.download("capital_market", "equities_sme", "sec_bhavdata_full", date,
                 s3_bucket=event["bucket"], s3_prefix="nse/")

    # MCX gold
    mcx.download("spot", "market", "spot_archive",
                 from_date=date, to_date=date, commodity="GOLD",
                 s3_bucket=event["bucket"], s3_prefix="mcx/")

    return {"statusCode": 200}

Build the layer:

cd .lambda_layer
./build.sh          # nse-data + mcx-data + pandas + curl-cffi + openpyxl

CLI

# NSE
nse-data --help
nse-data list

# MCX
mcx-data --help
mcx-data spot-recent --commodity GOLD
mcx-data spot-archive --from 01/05/2026 --to 22/05/2026 --commodity GOLD

Documentation

Full docs at View Documentation →

Polars output (optional)

By default every function returns a pandas DataFrame. To get polars DataFrames instead, install the extra and set one environment variable before importing — no code changes needed, and it applies to NSE, BSE and MCX alike:

pip install indian-market-data[polars]

import os
os.environ["IMD_DATAFRAME"] = "polars"   # set before importing

from indianmarketdata import nse, bse, mcx
type(nse.get("capital_market", "equities_sme", "sec_bhavdata_full", "2026-05-22"))  # polars.DataFrame
type(bse.get_index("SENSEX", "2026-01-01", "2026-05-22"))                            # polars.DataFrame
type(mcx.get_spot_recent())                                                          # polars.DataFrame

All internal logic stays in pandas; conversion happens only at the final return step. Leave IMD_DATAFRAME unset (or =pandas) for the default pandas output.

License

MIT — data sourced from NSE India and MCX India.