nselib
Advanced tools
A Python library to fetch publicly available data from NSE India.
Fresh install:
pip install nselib
Upgrade to latest:
pip install nselib --upgrade
Note: Compatible and tested with Python 3.8 and above.
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)
Most functions accept dates in two ways:
| Parameter | Format | Example |
|---|---|---|
from_date / to_date | dd-mm-YYYY | '01-06-2024' |
period | Shorthand code | '1D', '1W', '1M', '6M', '1Y' |
You must provide either
from_date+to_dateorperiod, not both.
from nselib import capital_market
| Function | Description | Key Parameters |
|---|---|---|
price_volume_and_deliverable_position_data() | OHLCV + delivery data | symbol, from_date/to_date or period |
price_volume_data() | OHLCV price volume data | symbol, from_date/to_date or period |
deliverable_position_data() | Delivery position data | symbol, from_date/to_date or period |
bulk_deal_data() | Bulk deal transactions | from_date/to_date or period |
block_deals_data() | Block deal transactions | from_date/to_date or period |
short_selling_data() | Short selling reports | from_date/to_date or period |
bhav_copy_with_delivery() | Daily bhav copy with delivery | trade_date |
bhav_copy_equities() | CM-UDiFF bhav copy | trade_date |
bhav_copy_sme() | SME bhav copy | trade_date |
equity_list() | All listed equities | — |
fno_equity_list() | F&O equity list with lot sizes | — |
fno_index_list() | F&O index list with lot sizes | — |
nifty50_equity_list() | Nifty 50 constituents | — |
niftynext50_equity_list() | Nifty Next 50 constituents | — |
niftymidcap150_equity_list() | Nifty Midcap 150 constituents | — |
niftysmallcap250_equity_list() | Nifty Smallcap 250 constituents | — |
india_vix_data() | India VIX historical data | from_date/to_date or period |
index_data() | Historical index OHLC data | index, from_date/to_date or period |
market_watch_all_indices() | Live snapshot of all indices | — |
daily_volatility() | CM daily volatility report | trade_date |
fii_dii_trading_activity() | FII/DII buy-sell activity | — |
var_begin_day() | VaR — begin of day | trade_date |
var_1st_intra_day() | VaR — 1st intraday | trade_date |
var_2nd_intra_day() | VaR — 2nd intraday | trade_date |
var_3rd_intra_day() | VaR — 3rd intraday | trade_date |
var_4th_intra_day() | VaR — 4th intraday | trade_date |
var_end_of_day() | VaR — end of day | trade_date |
sme_bhav_copy() | SME bhav copy | trade_date |
sme_band_complete() | SME band complete data | trade_date |
week_52_high_low_report() | 52-week high/low report | trade_date |
financial_results_for_equity() | Quarterly/annual financials | from_date/to_date or period, fin_period, fo_sec |
corporate_bond_trade_report() | Corporate bond trades | trade_date |
pe_ratio() | PE ratio for all equities | trade_date |
corporate_actions_for_equity() | Corporate actions | from_date/to_date or period, fno_only |
event_calendar_for_equity() | Event calendar | from_date/to_date or period, fno_only |
top_gainers_or_losers() | Top gainers or losers | to_get ('gainers' / 'loosers') |
most_active_equities() | Most active by value/volume | fetch_by ('value' / 'volume') |
total_traded_stocks() | All traded stocks summary | — |
category_turnover_cash() | category-wise turnover data | trade_date |
business_growth_cm_segment() | business growth data for the NSE capital market | data_type, from_year , to_year |
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')
from nselib import derivatives
| Function | Description | Key Parameters |
|---|---|---|
future_price_volume_data() | Futures price & volume | symbol, instrument (FUTIDX/FUTSTK), dates |
option_price_volume_data() | Options price & volume | symbol, instrument (OPTIDX/OPTSTK), option_type (PE/CE), dates |
fno_bhav_copy() | F&O daily bhav copy | trade_date |
participant_wise_open_interest() | OI by participant category | trade_date |
participant_wise_trading_volume() | Volume by participant category | trade_date |
daily_volatility() | F&O daily volatility report | trade_date |
expiry_dates_future() | Upcoming futures expiry dates | — |
expiry_dates_option_index() | Upcoming options expiry dates | — |
nse_live_option_chain() | Live option chain | symbol, expiry_date (optional), oi_mode |
fii_derivatives_statistics() | FII derivatives stats | trade_date |
fno_security_in_ban_period() | Securities in F&O ban | trade_date |
live_most_active_underlying() | Most active underlyings | — |
category_turnover_fo() | derivatives category-wise turnover data | trade_date |
business_growth_fo_segment() | business growth data for the NSE F&O segment | data_type, from_year , to_year |
Instrument Types:
| Code | Description |
|---|---|
FUTIDX | Future Index |
FUTSTK | Future Stock |
OPTIDX | Option Index |
OPTSTK | Option Stock |
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')
from nselib import cash_market
| Function | Description | Key Parameters |
|---|---|---|
nsdl_fpi_investment_activity() | NSDL FPI investment activity for a reporting date | trade_date |
nsdl_fpi_latest_investment_activity() | Latest NSDL FPI investment activity | — |
nsdl_fpi_derivative_activity() | NSDL FPI derivative activity for a reporting date | trade_date |
nsdl_fpi_latest_derivative_activity() | Latest NSDL FPI derivative activity | — |
amfi_monthly_report_links() | List AMFI monthly archive links | — |
amfi_monthly_data() | Parse one AMFI monthly report | report_month, file_type_priority |
amfi_monthly_historical_data() | Parse AMFI monthly reports across a range | from_month, to_month, file_type_priority |
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')
from nselib import indices
| Function | Description | Key Parameters |
|---|---|---|
index_list() | Available indices by category | index_category |
constituent_stock_list() | Stocks in a given index | index_category, index_name |
live_index_performances() | Live performance of all 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()
from nselib import debt
| Function | Description | Key Parameters |
|---|---|---|
securities_available_for_trading() | Debt securities available | trade_date |
Example:
df = debt.securities_available_for_trading(trade_date='20-12-2025')
import nselib
| Function | Description |
|---|---|
trading_holiday_calendar() | NSE trading holidays for all segments |
Example:
df = nselib.trading_holiday_calendar()
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')
There are multiple ways to contribute to nselib:
Found a bug or have a feature request? Please open an issue on the GitHub Issues page.
git checkout -b feature/your-feature)git commit -m 'Add your feature')git push origin feature/your-feature)Help the community by writing tutorials, blog posts, or example projects using nselib.
This project is licensed under the Apache License 2.0 — see the LICENSE file for details.
FAQs
library to get NSE India data
We found that nselib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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