Nifty Terminal

The Pythonic gateway to Indian Market Data

High-fidelity Index, Equity, ETF, and Fundamental data directly from NSE India APIs — as a Python library and a CLI tool.

Why Nifty Terminal?

Most NSE scrapers break due to strict session requirements, Akamai protection, or inconsistent API responses. Nifty Terminal solves these problems:

• Zero-Config Session Handling — automatically manages cookies and session handshakes for NSE India and Nifty Indices.
• Async-First Architecture — built on httpx and asyncio for high-performance concurrent fetching.
• Unified Fundamentals — normalizes modern XBRL and legacy HTML filings into consistent JSON objects.
• Smart ETF Categorization — auto-tags ETFs by asset class (Gold, Silver, Index) and variants (TRI, Alpha, Value).
• Built-in CLI — fetch market data directly from your terminal without writing a single line of Python.

Installation

Library only:

pip install niftyterminal

With CLI support (adds click and rich):

pip install "niftyterminal[cli]"

CLI Usage

Once installed with [cli], the niftyterminal command is available globally.

niftyterminal [--json] [--version] <group> <command> [args...]

Pass --json to any command to get raw JSON output suitable for piping to jq.

Market

# Check if the market is open
niftyterminal market status

# Check a specific segment
niftyterminal market status --market-type Currency

Indices

# List all NSE indices
niftyterminal index list

# Filter by name or type
niftyterminal index list --filter "NIFTY" --type "Sectoral"

# Live quotes for all indices
niftyterminal index quote

# Show only top 10, filter by name
niftyterminal index quote --filter "BANK" --top 10

# Constituent stocks of an index
niftyterminal index stocks "NIFTY 50"

# Historical OHLC + PE/PB/DivYield + Total Returns Index
niftyterminal index history "NIFTY 50" --from 2025-01-01
niftyterminal index history "NIFTY BANK" --from 2025-01-01 --to 2025-03-31

Stocks

# List all NSE-listed stocks (search by name or symbol)
niftyterminal stock list --search "reliance"
niftyterminal stock list --series EQ --top 20

# Detailed quote with sector, flags, and market cap
niftyterminal stock quote RELIANCE
niftyterminal stock quote TCS

# Quarterly financial results (P&L, EPS, segments)
niftyterminal stock financials RELIANCE
niftyterminal stock financials TCS --period Annual --consolidated --limit 4

# Balance sheet
niftyterminal stock balance-sheet RELIANCE

# Cash flow statement
niftyterminal stock cash-flow HDFCBANK

# Full annual report (P&L + Balance Sheet + Cash Flow)
niftyterminal stock annual-report INFY --standalone

ETFs

# List all ETFs with asset type and underlying asset
niftyterminal etf list
niftyterminal etf list --asset-type Gold
niftyterminal etf list --search "nifty"

# Historical OHLCV
niftyterminal etf history NIFTYBEES --from 2025-01-01

VIX

niftyterminal vix history --from 2025-01-01
niftyterminal vix history --from 2025-01-01 --to 2025-03-31

Commodities

# See all available commodity symbols
niftyterminal commodity list

# Historical spot prices
niftyterminal commodity history GOLD1G --from 2025-01-01
niftyterminal commodity history SILVER1KG --from 2025-01-01 --to 2025-03-31

JSON output

Any command can output raw JSON by prepending --json:

niftyterminal --json stock quote RELIANCE | jq '.ltp'
niftyterminal --json index history "NIFTY 50" --from 2025-01-01 > nifty50.json

Python Library Usage

All functions are async. Use asyncio.run() or await inside an async context.

Quick start

import asyncio
from niftyterminal import get_index_historical_data

async def main():
    data = await get_index_historical_data("NIFTY 50", "2025-01-01", "2025-03-31")
    for day in data['indexData']:
        print(f"{day['date']}: {day['close']}  PE={day['PE']}")

asyncio.run(main())

Market status

from niftyterminal import get_market_status

status = await get_market_status("Capital Market")
# {"marketStatus": "Open", "marketStatusMessage": "..."}

Index quotes

from niftyterminal import get_all_index_quote

data = await get_all_index_quote()
for q in data['indexQuote']:
    print(q['indexName'], q['ltp'], q['percentChange'])

Stock quote

from niftyterminal import get_stock_quote

q = await get_stock_quote("RELIANCE")
print(q['companyName'], q['ltp'], q['marketCap'])

Financial results

from niftyterminal import get_stock_financials

data = await get_stock_financials("TCS", consolidated=True, period="Quarterly")
for filing in data['filings'][:4]:
    fin = filing['financial_data']['financials']
    print(filing['to_date'], fin.get('net_profit'))

Annual report (P&L + Balance Sheet + Cash Flow)

from niftyterminal import get_stock_annual_report

report = await get_stock_annual_report("INFY")

ETF & VIX

from niftyterminal import get_all_etfs, get_etf_historical_data, get_vix_historical_data

etfs = await get_all_etfs()
gold_etfs = [e for e in etfs['etfList'] if e['assetType'] == 'Gold']

vix = await get_vix_historical_data("2025-01-01")

Commodities

from niftyterminal import get_commodity_list, get_commodity_historical_data

symbols = await get_commodity_list()
history = await get_commodity_historical_data("GOLD1G", "2025-01-01")

API Reference

Technical Highlights

Robust session management

NSE India requires a warm-up phase to acquire cookies before API requests are accepted. Nifty Terminal implements an automated session that:

1. Performs a headless handshake with nseindia.com.
2. Maintains persistent cookies with a shared httpx client (auto-refreshed every 10 minutes).
3. Applies rate limiting (3 req/s) and User-Agent rotation to avoid detection.
4. Falls back gracefully on 401 errors by re-establishing the session.

Financial data normalization

Filing formats change over time. Parsers handle both:

• XBRL (post-2018): High-precision extraction using BSE's in-bse-fin: taxonomy.
• Legacy HTML (pre-2018): Intelligent key mapping to the same normalized schema.

Banking and non-banking (IndAS) taxonomies are both supported.

Disclaimer

This library is for educational and research purposes only. It is not affiliated with, maintained, or endorsed by NSE India. Use market data responsibly and adhere to the NSE Terms of Service.

Built with ❤️ for Indian Quants.