nselib

A Python library to fetch publicly available data from NSE India.

✨ Features

• Capital Market — Price volume data, deliverable positions, bhav copies, bulk/block deals, short selling, VaR margins, PE ratios, 52-week highs/lows, and more
• Cash Market — NSDL FPI investment and derivative activity plus AMFI monthly archive reports
• Derivatives — Futures & options price volume data, bhav copies, participant-wise OI & volume, live option chains, FII statistics, ban period securities
• Indices — Index constituent lists, live index performances across Broad Market, Sectoral, Thematic, and Strategy categories
• Debt — Securities available for trading
• Corporate Filings — Financial results, corporate actions, event calendars
• Market Activity — Top gainers/losers, most active equities, total traded stocks, FII/DII activity
• Utilities — Trading holiday calendar, India VIX historical data

📦 Installation

Fresh install:

pip install nselib

Upgrade to latest:

pip install nselib --upgrade

Note: Compatible and tested with Python 3.8 and above.

🚀 Quick Start

from nselib import capital_market

# Get price volume data for a stock (last 1 month)
df = capital_market.price_volume_data(symbol='SBIN', period='1M')
print(df.head())

# Or specify a custom date range
df = capital_market.price_volume_and_deliverable_position_data(
    symbol='SBIN',
    from_date='01-01-2024',
    to_date='31-01-2024'
)
print(df)

📖 API Reference

Date Parameters

Most functions accept dates in two ways:

You must provide either from_date + to_date or period, not both.

Capital Market

from nselib import capital_market

Examples:

# Bhav copy for a specific date
df = capital_market.bhav_copy_with_delivery(trade_date='20-06-2024')

# India VIX for last 1 week
df = capital_market.india_vix_data(period='1W')

# CM daily volatility report
df = capital_market.daily_volatility(trade_date='17-04-2026')

# Historical index data
df = capital_market.index_data(index='NIFTY 50', from_date='01-01-2024', to_date='31-03-2024')

# Financial results (quarterly, F&O securities only)
df = capital_market.financial_results_for_equity(period='6M', fo_sec=True, fin_period='Quarterly')

# Top gainers in live market
df = capital_market.top_gainers_or_losers('gainers')

Derivatives

from nselib import derivatives

Instrument Types:

Examples:

# Futures price data
df = derivatives.future_price_volume_data(
    symbol='SBIN', instrument='FUTSTK', period='1M'
)

# Live option chain
df = derivatives.nse_live_option_chain(symbol='BANKNIFTY', expiry_date='27-03-2025')

# Compact option chain (fewer columns)
df = derivatives.nse_live_option_chain(symbol='NIFTY', oi_mode='compact')

# FII derivatives statistics
df = derivatives.fii_derivatives_statistics(trade_date='20-12-2025')

# F&O daily volatility report
df = derivatives.daily_volatility(trade_date='17-04-2026')

Cash Market

from nselib import cash_market

Examples:

# NSDL FPI investment activity for a specific reporting date
df = cash_market.nsdl_fpi_investment_activity(trade_date='30-10-2025')

# Latest NSDL FPI derivative activity
df = cash_market.nsdl_fpi_latest_derivative_activity()

# List available AMFI archive reports
links = cash_market.amfi_monthly_report_links()

# Parse a single AMFI monthly report
df = cash_market.amfi_monthly_data(report_month='01-03-2026')

# Parse AMFI history for a month range
history = cash_market.amfi_monthly_historical_data(from_month='01-01-2024', to_month='01-03-2026')

Indices

from nselib import indices

Index Categories: BroadMarketIndices, SectoralIndices, ThematicIndices, StrategyIndices

Examples:

# List all broad market indices
index_names = indices.index_list(index_category='BroadMarketIndices')

# Get Nifty 50 constituents
df = indices.constituent_stock_list(index_category='BroadMarketIndices', index_name='Nifty 50')

# Live index performances
df = indices.live_index_performances()

Debt

from nselib import debt

Example:

df = debt.securities_available_for_trading(trade_date='20-12-2025')

Utilities

import nselib

Example:

df = nselib.trading_holiday_calendar()

🐞 Logging & Debugging

nselib comes with a built-in logger that is silent by default so it doesn't pollute your application's logs. If you want to see detailed network requests, API responses, or debug errors while working with the library, you can easily enable it.

import nselib
import logging

# Enable the logger to output to the console
nselib.enable_logging(level=logging.DEBUG)

# Now, function calls will emit helpful trace logs
df = nselib.capital_market.price_volume_data('SBIN', period='1W')

🤝 How to Contribute

There are multiple ways to contribute to nselib:

Report Issues & Suggest Features

Found a bug or have a feature request? Please open an issue on the GitHub Issues page.

Submit Pull Requests

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add your feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

Write About nselib

Help the community by writing tutorials, blog posts, or example projects using nselib.

Contact

• Original Author: Ruchi Tanmay
• GitHub: RuchiTanmay/nselib

📄 License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.