mcx-data 1.1.1

Download MCX India commodity market data as pandas DataFrames — spot prices (recent + archive), futures bhavcopy. Works on AWS Lambda.

mcx-data

Download MCX India commodity spot market data as pandas DataFrames.
28 commodities — GOLD, SILVER, CRUDEOIL, NATURALGAS and more.
Works on AWS Lambda via Chrome TLS impersonation (bypasses Akamai WAF).

Download MCX India commodity spot market data as pandas DataFrames. Works from AWS Lambda and any cloud environment.

Full Documentation → NikhilSuthar.github.io/indian-market-data/mcx-spot

Part of the indian-market-data monorepo — also see nse-archives.

pip install mcx-data

Quick Start

from mcxdata import mcx

# Today's spot prices — all 28 commodities
df = mcx.get_spot_recent()

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

# Historical (requires specific commodity)
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")

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

# Available commodities (28)
mcx.list_commodities()

Datasets

Dataset	Description	Date Param
spot_recent	Today's spot prices — all 28 commodities	None
spot_archive	Historical spot prices by commodity + date range	from_date, to_date

Available Commodities (28)

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

Notes

• MCX archive requires a specific commodity — "ALL" returns empty (MCX API limitation)
• Uses curl-cffi Chrome TLS impersonation to bypass MCX Akamai WAF
• Lambda IPs are generally unblocked — works reliably on AWS

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:

pip install mcx-data[polars]

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

from mcxdata import mcx
df = mcx.get_spot_recent()
type(df)   # 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 from MCX India.