### Install nselib

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Use pip to install the nselib library. For an upgrade, use the --upgrade flag.

```bash
pip install nselib
```

```bash
pip install nselib --upgrade
```

--------------------------------

### Get Top Gainers in Live Market

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Fetches a list of top gainers in the live market. Accepts 'gainers' or 'losers' as an argument.

```python
df = capital_market.top_gainers_or_losers('gainers')
```

--------------------------------

### Get FII/DII Trading Activity

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Returns the current-day or last available FII and DII buy/sell activity summary. No parameters are required.

```python
from nselib import capital_market

df = capital_market.fii_dii_trading_activity()
print(df)
# Columns: category, buyValue, sellValue, netValue
```

--------------------------------

### Fetch SME Bhav Copy and Band Complete Data

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Get SME bhav copy and SME price band complete data for a specific trading date. Requires the 'nselib.capital_market' module.

```python
from nselib import capital_market

sme_bhav = capital_market.sme_bhav_copy(trade_date='20-06-2024')
sme_band = capital_market.sme_band_complete(trade_date='20-06-2024')
print(sme_bhav.head())
print(sme_band.head())
```

--------------------------------

### Get Option Expiry Dates by Index

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves a dictionary mapping index names to their respective option expiry dates. Use specific index keys like 'NIFTY' or 'BANKNIFTY' to get their expiry lists.

```python
from nselib import derivatives

index_expiry_map = derivatives.expiry_dates_option_index()
print(index_expiry_map['NIFTY'])
print(index_expiry_map['BANKNIFTY'])
```

--------------------------------

### Get Deliverable Quantity Data (Capital Market)

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetch only the deliverable quantity related columns for an equity symbol. Supports period shorthands or explicit date ranges.

```python
from nselib import capital_market

df = capital_market.deliverable_position_data(symbol='HDFCBANK', period='1M')
print(df)
# Expected columns: Symbol, Series, Date, TotalTradedQuantity, DeliverableQty, % Dly Qt to Traded Qty
```

--------------------------------

### Get Live Snapshot of All Indices

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Provides a live intraday snapshot, or an end-of-day snapshot after market hours, for all NSE indices. Useful for a quick overview of market performance across different indices.

```python
from nselib import capital_market

df = capital_market.market_watch_all_indices()
print(df[['index', 'last', 'percentChange', 'yearHigh', 'yearLow']].head(10))
```

--------------------------------

### Get Live Index Performances

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves the live performance data for all indices. This function does not require any parameters.

```python
df = indices.live_index_performances()
```

--------------------------------

### Get Bhav Copy with Delivery Data

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieve the daily bhav copy including delivery data for a specific trade date.

```python
df = capital_market.bhav_copy_with_delivery(trade_date='20-06-2024')
```

--------------------------------

### Get Available Debt Securities

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves a list of debt securities available for trading on a specific date. Requires the `debt` module from nselib.

```python
from nselib import debt
df = debt.securities_available_for_trading(trade_date='20-12-2025')
```

--------------------------------

### Get Total Traded Stocks Summary

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Returns a summary dictionary of live market statistics and a DataFrame of all traded stocks. No parameters are required.

```python
from nselib import capital_market

summary, detail_df = capital_market.total_traded_stocks()
print(f"Summary: {summary}")  # dict with advances, declines, unchanged counts
print(detail_df.head())
```

--------------------------------

### Get Live Index Performances

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves live or last-traded performance data for all NSE indices. Displays key metrics like last traded price, percentage change, and yearly high/low.

```python
from nselib import indices

df = indices.live_index_performances()
print(df[['indexSymbol', 'last', 'percentChange', 'yearHigh', 'yearLow']].head(10))
```

--------------------------------

### Get Live Option Chain Data

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Fetches the live option chain for a specific symbol and expiry date. The expiry date is optional; if omitted, the nearest expiry is used. `oi_mode` can be set to 'compact' for fewer columns.

```python
df = derivatives.nse_live_option_chain(symbol='BANKNIFTY', expiry_date='27-03-2025')
```

```python
df = derivatives.nse_live_option_chain(symbol='NIFTY', oi_mode='compact')
```

--------------------------------

### Fetch Options Price and Volume Data

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Get contract-wise options price and volume data for a symbol, instrument type, and optional option type. Iterates over both CE and PE if `option_type` is omitted. Use 'OPTIDX' for index options and 'OPTSTK' for stock options.

```python
from nselib import derivatives

# NIFTY index call options for last 1 week
df_ce = derivatives.option_price_volume_data(
    symbol='NIFTY',
    instrument='OPTIDX',   # OPTIDX = index options, OPTSTK = stock options
    option_type='CE',
    period='1W'
)
print(df_ce.head())

```

```python
# All SBIN stock option contracts (CE + PE) for a date range
df_all = derivatives.option_price_volume_data(
    symbol='SBIN',
    instrument='OPTSTK',
    from_date='01-12-2024',
    to_date='31-12-2024'
)
print(df_all.groupby('OPTION_TYP').size())
```

--------------------------------

### Get Future Expiry Dates

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves a list of future expiry dates for derivatives. The output is a list of strings representing dates.

```python
from nselib import derivatives

future_expiries = derivatives.expiry_dates_future()
print(future_expiries)  # ['27-Mar-2025', '24-Apr-2025', ...]
```

--------------------------------

### Get Volume by Participant Category

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Returns the trading volume breakdown by participant category for a specific trading date.

```python
from nselib import derivatives

df = derivatives.participant_wise_trading_volume(trade_date='16-09-2024')
print(df)
```

--------------------------------

### Get FII Derivatives Statistics

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves Foreign Institutional Investor (FII) derivatives statistics for a specific trade date.

```python
df = derivatives.fii_derivatives_statistics(trade_date='20-12-2025')
```

--------------------------------

### Get F&O-eligible index list with lot sizes

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches a list of all F&O-eligible index underlyings, such as NIFTY and BANKNIFTY, including their respective lot sizes. This is essential for F&O index trading analysis.

```python
from nselib import capital_market

df = capital_market.fno_index_list()
print(df)
```

--------------------------------

### Get VaR Margin Data for Trading Day

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches Value-at-Risk (VaR) margin data at different intraday snapshots for a given trading date. This includes data from the beginning of the day, multiple intraday updates, and the end of the day.

```python
from nselib import capital_market

# VaR at beginning of day
df_begin = capital_market.var_begin_day(trade_date='20-06-2024')

# VaR at 1st intraday update
df_intra1 = capital_market.var_1st_intra_day(trade_date='20-06-2024')

# VaR at end of day
df_eod = capital_market.var_end_of_day(trade_date='20-06-2024')
print(df_eod.columns.tolist())  # var_columns from constants
```

--------------------------------

### Get Constituent Stocks for an Index

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Downloads the current constituent stock list for a given NSE index. Provides company details and a link to the index's fact sheet for more information.

```python
from nselib import indices

df = indices.constituent_stock_list(
    index_category='BroadMarketIndices',
    index_name='Nifty 50'
)
print(df.head())
# Also prints: "Note: For more detail information related to Nifty 50. Please check the Fact Sheet - <url>"
```

```python
sectoral_df = indices.constituent_stock_list(
    index_category='SectoralIndices',
    index_name='Nifty Bank'
)
print(sectoral_df[['Company Name', 'Symbol', 'Industry']])
```

--------------------------------

### Get F&O-eligible equity list with lot sizes

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves a list of all F&O-eligible equity underlyings along with their current lot sizes. Useful for understanding the universe of F&O tradable equities.

```python
from nselib import capital_market

df = capital_market.fno_equity_list()
print(df.columns.tolist())   # includes symbol, lotSize, etc.
print(df.head())
```

--------------------------------

### Get OHLCV + Deliverable Position Data (Capital Market)

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetch Open, High, Low, Close, Volume, and Deliverable Quantity data for a given equity symbol. Supports period shorthands or explicit date ranges. Handles long date ranges by auto-chunking.

```python
from nselib import capital_market

# Using a period shorthand
df = capital_market.price_volume_and_deliverable_position_data(symbol='SBIN', period='1M')
print(df.columns.tolist())
# ['Symbol', 'Series', 'Date', 'Prev Close', 'Open Price', 'High Price', 'Low Price',
#  'Last Price', 'Close Price', 'Average Price', 'TotalTradedQuantity', 'TurnoverInRs',
#  'No.ofTrades', 'DeliverableQty', '% Dly Qt to Traded Qty']
```

```python
from nselib import capital_market

# Using an explicit date range
df = capital_market.price_volume_and_deliverable_position_data(
    symbol='RELIANCE',
    from_date='01-01-2024',
    to_date='31-03-2024'
)
print(df.shape)   # (num_rows, 15)
print(df.head(3))
```

--------------------------------

### Get OHLCV Price and Volume Data (Capital Market)

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetch Open, High, Low, Close, and Volume data for an equity symbol without deliverable quantity details. Supports period shorthands or explicit date ranges. Handles multi-year ranges by auto-chunking.

```python
from nselib import capital_market

df = capital_market.price_volume_data(symbol='INFY', period='6M')
print(df[['Date', 'Open Price', 'High Price', 'Low Price', 'Close Price', 'TotalTradedQuantity']])
```

```python
from nselib import capital_market

# Multi-year range (auto-chunked into 365-day windows)
df = capital_market.price_volume_data(
    symbol='TCS',
    from_date='01-01-2022',
    to_date='31-12-2023'
)
print(df.shape)
```

--------------------------------

### Fetch Stock Price Volume Data

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Get price volume data for a stock using the capital_market module. You can specify a period like '1M' for one month, or provide custom date ranges.

```python
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())
```

```python
# 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)
```

--------------------------------

### Get Equity Financial Results

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches quarterly or annual financial results for equities. Can filter by F&O securities and specify a period or date range.

```python
from nselib import capital_market

# Quarterly results for F&O securities over the last 6 months
df = capital_market.financial_results_for_equity(
    period='6M',
    fo_sec=True,
    fin_period='Quarterly'
)
print(df.head())

# Annual results for all equities in a date range
df_annual = capital_market.financial_results_for_equity(
    from_date='01-01-2024',
    to_date='31-03-2024',
    fo_sec=False,
    fin_period='Annual'
)
```

--------------------------------

### Fetch AMFI Monthly Data with File Type Priority

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves AMFI monthly data, prioritizing XLS and XLSX files over PDF. Use this to get specific monthly financial reports.

```python
from nselib import cash_market

df_xls = cash_market.amfi_monthly_data(
    report_month='01-01-2026',
    file_type_priority=('xls', 'xlsx', 'pdf')
)
print(df_xls.head())
```

--------------------------------

### Get Futures Price and Volume Data

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves futures price and volume data for a given symbol and instrument type over a specified period. Valid instrument types include FUTIDX and FUTSTK.

```python
df = derivatives.future_price_volume_data(
    symbol='SBIN',
    instrument='FUTSTK',
    period='1M'
)
```

--------------------------------

### Get List of All NSE-Listed Equities

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieve a DataFrame containing all equity instruments listed on the NSE. Includes details like symbol, company name, series, listing date, and face value.

```python
from nselib import capital_market

df = capital_market.equity_list()
print(df.shape)          # (total_listed_equities, 5)
print(df.head())
# SYMBOL  | NAME OF COMPANY          |  SERIES | DATE OF LISTING | FACE VALUE
# 20MICRONS | 20 Microns Limited      |  EQ     | 06-10-2008      | 5
```

--------------------------------

### Get Capital Market Daily Volatility Report

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the daily volatility report for the capital market (NSCCL CMVOLT file) for a specific trading date. The function automatically attempts to use multiple URL sources if one fails.

```python
from nselib import capital_market

df = capital_market.daily_volatility(trade_date='17-04-2026')
print(df.head())
# Columns typically include Symbol, Series, VaR%, ELM%, Adhoc Margin%, Applicable Margin%

try:
    df = capital_market.daily_volatility(trade_date='01-01-2026')  # holiday
except FileNotFoundError as e:
    print(f"Not found: {e}")
```

--------------------------------

### Get CM Segment Business Growth

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches historical business growth statistics for the NSE capital market segment at yearly, monthly, or daily granularity. Use `data_type='yearly'`, `'monthly'`, or `'daily'`.

```python
from nselib import capital_market

# Yearly data (all available years)
df_yearly = capital_market.business_growth_cm_segment(data_type='yearly')
print(df_yearly.head())
```

--------------------------------

### Get 52-Week High/Low Report

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Downloads the 52-week high and low report for all NSE Capital Market (CM) securities for a specified trading date. This report is useful for identifying stocks reaching significant price levels.

```python
from nselib import capital_market

df = capital_market.week_52_high_low_report(trade_date='20-06-2024')
print(df.head())
```

--------------------------------

### Download Daily Bhav Copy with Delivery Data (Capital Market)

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Download the end-of-day bhav copy for all traded securities on a specific date, including delivery quantities. Raises FileNotFoundError if data is unavailable for the given date (e.g., holidays).

```python
from nselib import capital_market

df = capital_market.bhav_copy_with_delivery(trade_date='20-06-2024')
print(df.head())
# Columns: SYMBOL, SERIES, OPEN, HIGH, LOW, CLOSE, LAST,
#          PREVCLOSE,
#          TOTTRDQTY, TOTTRDVAL, TIMESTAMP, TOTALTRADES, ISIN, DeliverableQty, ...
```

```python
from nselib import capital_market

try:
    df = capital_market.bhav_copy_with_delivery(trade_date='01-01-2024')  # holiday
except FileNotFoundError as e:
    print(f"No data: {e}")
```

--------------------------------

### var_begin_day

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves VaR (Value at Risk) data for the beginning of the day for a specific trade date.

```APIDOC
## var_begin_day()

### Description
Retrieves VaR data for the beginning of the day.

### Method
`capital_market.var_begin_day()`

### Parameters
#### Path Parameters
- **trade_date** (string) - Required - The date in 'DD-MM-YYYY' format.
```

--------------------------------

### Get NSDL FPI Investment Activity

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves NSDL Foreign Portfolio Investor (FPI) investment activity for a specific reporting date. Use the latest function to get the most recent data without a date.

```python
df = cash_market.nsdl_fpi_investment_activity(trade_date='30-10-2025')
```

```python
df = cash_market.nsdl_fpi_latest_derivative_activity()
```

--------------------------------

### Get Most Active Equities

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Returns the most actively traded equities, sortable by traded value or volume. Use `fetch_by='value'` or `fetch_by='volume'`.

```python
from nselib import capital_market

# Most active by value
df_val = capital_market.most_active_equities(fetch_by='value')
print(df_val[['symbol', 'lastPrice', 'totalTradedVolume', 'totalTradedValue']].head())

# Most active by volume
df_vol = capital_market.most_active_equities(fetch_by='volume')
print(df_vol.head())
```

--------------------------------

### sme_bhav_copy() / sme_band_complete()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the SME bhav copy and SME price band complete data for a trading date.

```APIDOC
## sme_bhav_copy() / sme_band_complete()

### Description
Fetches the SME bhav copy and SME price band complete data for a trading date.

### Method
```python
capital_market.sme_bhav_copy(trade_date: str)
capital_market.sme_band_complete(trade_date: str)
```

### Parameters
#### Path Parameters
- **trade_date** (str) - Required - The trading date in DD-MM-YYYY format.
```

--------------------------------

### bhav_copy_with_delivery()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Downloads the full NSE equity bhav copy for a specific trading date, including delivery quantities.

```APIDOC
## bhav_copy_with_delivery()

### Description
Downloads the full NSE equity bhav copy (end-of-day snapshot for all traded securities) for a specific trading date, including delivery quantities.

### Method
`capital_market.bhav_copy_with_delivery(trade_date: str)`

### Parameters
#### Path Parameters
None

#### Query Parameters
- **trade_date** (str) - Required - The trading date in 'dd-mm-YYYY' format (e.g., '20-06-2024').

### Request Example
```python
from nselib import capital_market

df = capital_market.bhav_copy_with_delivery(trade_date='20-06-2024')
print(df.head())

try:
    df = capital_market.bhav_copy_with_delivery(trade_date='01-01-2024')  # holiday
except FileNotFoundError as e:
    print(f"No data: {e}")
```

### Response
#### Success Response (pandas.DataFrame)
DataFrame containing columns such as 'SYMBOL', 'SERIES', 'OPEN', 'HIGH', 'LOW', 'CLOSE', 'LAST', 'PREVCLOSE', 'TOTTRDQTY', 'TOTTRDVAL', 'TIMESTAMP', 'TOTALTRADES', 'ISIN', 'DeliverableQty'.

#### Response Example
```
SYMBOL | SERIES | OPEN | HIGH | LOW | CLOSE | LAST | PREVCLOSE | TOTTRDQTY | TOTTRDVAL | TIMESTAMP | TOTALTRADES | ISIN | DeliverableQty | ...
```
```

--------------------------------

### Get Financial Results for Equity

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves financial results for F&O securities, specifying the period and financial reporting frequency.

```python
df = capital_market.financial_results_for_equity(period='6M', fo_sec=True, fin_period='Quarterly')
```

--------------------------------

### var_begin_day(), var_1st_intra_day(), ..., var_end_of_day()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves Value-at-Risk (VaR) margin data at different intraday snapshots for a trading date, including beginning of day, multiple intraday updates, and end of day.

```APIDOC
## var_begin_day(), var_1st_intra_day(), ..., var_end_of_day()

### Description
Fetches Value-at-Risk (VaR) margin data at different intraday snapshots for a trading date: beginning of day, 1st–4th intraday updates, and end of day.

### Method
`capital_market.var_begin_day(trade_date='YYYY-MM-DD')`
`capital_market.var_1st_intra_day(trade_date='YYYY-MM-DD')`
`capital_market.var_end_of_day(trade_date='YYYY-MM-DD')`
(Other intraday functions available: `var_2nd_intra_day`, `var_3rd_intra_day`, `var_4th_intra_day`)

### Parameters
#### Query Parameters
- **trade_date** (string) - Required - The trading date for which to fetch VaR data in 'DD-MM-YYYY' format.

### Response Example
```json
{
  "Symbol": "INFY",
  "Margin": 15000.00
}
```
```

--------------------------------

### nse_live_option_chain()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the real-time option chain for any F&O symbol. Supports `full` mode (includes bid/ask) or `compact` mode (fewer columns).

```APIDOC
## nse_live_option_chain()

### Description
Fetches the real-time option chain for any F&O symbol. Supports `full` mode (includes bid/ask) or `compact` mode (fewer columns).

### Method
```python
derivatives.nse_live_option_chain(symbol: str, expiry_date: str = None, oi_mode: str = 'full')
```

### Parameters
#### Query Parameters
- **symbol** (str) - Required - The trading symbol (e.g., 'BANKNIFTY', 'NIFTY').
- **expiry_date** (str) - Optional - The expiry date in DD-MM-YYYY format. If omitted, the nearest expiry is used.
- **oi_mode** (str) - Optional - The mode for fetching option chain data. Options are 'full' (default) or 'compact'.
```

--------------------------------

### Get India VIX Data for a Period

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Fetch historical India VIX data for a specified period, such as the last week.

```python
df = capital_market.india_vix_data(period='1W')
```

--------------------------------

### Get Top Gainers or Losers

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Returns the top gaining or losing equities based on index category. Use `to_get='gainers'` or `to_get='loosers'`.

```python
from nselib import capital_market

# Top gainers
gainers = capital_market.top_gainers_or_losers(to_get='gainers')
print(gainers[['symbol', 'lastPrice', 'pChange']].head())

# Top losers
losers = capital_market.top_gainers_or_losers(to_get='loosers')
print(losers.head())
```

--------------------------------

### Download NSE PE Ratio Report

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Downloads the NSE PE ratio report for all listed equities on a specific trading date. Requires the `trade_date` parameter.

```python
from nselib import capital_market

df = capital_market.pe_ratio(trade_date='20-06-2024')
print(df.head())
```

--------------------------------

### Fetch Futures Price and Volume Data

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieve contract-wise futures price and volume data for a given symbol and instrument type. Automatically paginates in 90-day windows. Use 'FUTSTK' for stock futures and 'FUTIDX' for index futures.

```python
from nselib import derivatives

# SBIN stock futures for last 1 month
df = derivatives.future_price_volume_data(
    symbol='SBIN',
    instrument='FUTSTK',   # FUTSTK = stock futures, FUTIDX = index futures
    period='1M'
)
print(df.head())
# Columns: INSTRUMENT, SYMBOL, EXPIRY_DT, OPEN, HIGH, LOW, CLOSE,
#          SETTLE_PR, CONTRACTS, VAL_INLAKH, OPEN_INT, CHG_IN_OI, TIMESTAMP

```

```python
# BANKNIFTY index futures with explicit date range
df_idx = derivatives.future_price_volume_data(
    symbol='BANKNIFTY',
    instrument='FUTIDX',
    from_date='01-11-2025',
    to_date='08-12-2025'
)
```

--------------------------------

### Get F&O Daily Volatility Report

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Fetches the daily volatility report for the Futures and Options segment for a given trade date.

```python
df = derivatives.daily_volatility(trade_date='17-04-2026')
```

--------------------------------

### Get Daily Volatility Report

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves the daily volatility report for a specific trade date. Ensure the date is in the correct format.

```python
df = capital_market.daily_volatility(trade_date='17-04-2026')
```

--------------------------------

### Import Capital Market Module

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Import the necessary module for accessing capital market data.

```python
from nselib import capital_market
```

--------------------------------

### pe_ratio()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Downloads the NSE PE ratio report for all listed equities for a specific trading date.

```APIDOC
## pe_ratio()

### Description
Downloads the NSE PE ratio report for all listed equities for a specific trading date.

### Method
`capital_market.pe_ratio(trade_date: str)`

### Parameters
#### Path Parameters
None

#### Query Parameters
- **trade_date** (str) - Required - The trading date for which to fetch the PE ratio report (e.g., '20-06-2024').

### Request Example
```python
from nselib import capital_market
df = capital_market.pe_ratio(trade_date='20-06-2024')
print(df.head())
```

### Response
#### Success Response (200)
Returns a pandas DataFrame containing the PE ratio for all NSE equities for the specified date.
```

--------------------------------

### Download SME Segment Bhav Copy (Capital Market)

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetch the bhavcopy specifically for the NSE SME (Small and Medium Enterprises) segment for a given trading date.

```python
from nselib import capital_market

df = capital_market.bhav_copy_sme(trade_date='20-06-2024')
print(df.head())
```

--------------------------------

### Get Index Constituent Stocks

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Fetches the list of stocks that constitute a given index. Requires both the index category and the specific index name.

```python
df = indices.constituent_stock_list(index_category='BroadMarketIndices', index_name='Nifty 50')
```

--------------------------------

### Get Historical Index Data

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Fetches historical data for a specified index between two dates. Requires index name and date range.

```python
df = capital_market.index_data(index='NIFTY 50', from_date='01-01-2024', to_date='31-03-2024')
```

--------------------------------

### market_watch_all_indices()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Provides a live intraday snapshot of all NSE indices, including current price, percentage change, and yearly high/low.

```APIDOC
## market_watch_all_indices()

### Description
Returns a live intraday snapshot (or end-of-day snapshot after market hours) for all NSE indices.

### Method
`capital_market.market_watch_all_indices()`

### Parameters
None

### Response Example
```json
{
  "index": "NIFTY 50",
  "last": 23500.00,
  "percentChange": 0.50,
  "yearHigh": 24000.00,
  "yearLow": 18000.00
}
```
```

--------------------------------

### Get OI by Participant Category

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Returns the open interest breakdown by participant category (FII, DII, Pro, Client) for a specific trading date.

```python
from nselib import derivatives

df = derivatives.participant_wise_open_interest(trade_date='16-09-2024')
print(df)
# Columns: Client Type, Future Index Long, Future Index Short,
#          Option Index Call Long, Option Index Call Short, ...
```

--------------------------------

### Import Cash Market Module

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Imports the cash_market module from the nselib library. This is necessary to access cash market data functions.

```python
from nselib import cash_market
```

--------------------------------

### Enable Logging

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Enables the built-in logger for nselib to output detailed trace logs for network requests and API responses.

```APIDOC
## nselib.enable_logging()

### Description
Enables the nselib logger to output detailed network requests, API responses, or debug errors.

### Method
`nselib.enable_logging`

### Parameters
#### Query Parameters
- **level** (logging level) - Required - The logging level to set (e.g., `logging.DEBUG`).

### Request Example
```python
import nselib
import logging

nselib.enable_logging(level=logging.DEBUG)

df = nselib.capital_market.price_volume_data('SBIN', period='1W')
```
```

--------------------------------

### Get NSE Trading Holiday Calendar

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Fetches the NSE trading holiday calendar for all segments. This utility function is part of the main nselib module.

```python
import nselib
df = nselib.trading_holiday_calendar()
```

--------------------------------

### Fetch Live NSE Option Chain

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves the real-time option chain for any F&O symbol. Supports 'full' mode (includes bid/ask) or 'compact' mode (fewer columns).

```python
from nselib import derivatives

# Full option chain for BANKNIFTY nearest expiry
df_full = derivatives.nse_live_option_chain(symbol='BANKNIFTY')
print(df_full[['Strike_Price', 'CALLS_OI', 'CALLS_LTP', 'PUTS_LTP', 'PUTS_OI']].head(10))

```

```python
# Specific expiry with compact mode
df_compact = derivatives.nse_live_option_chain(
    symbol='NIFTY',
    expiry_date='27-03-2025',
    oi_mode='compact'
)
print(df_compact.columns.tolist())
# ['Fetch_Time', 'Symbol', 'Expiry_Date', 'CALLS_OI', 'CALLS_Chng_in_OI',
#  'CALLS_Volume', 'CALLS_IV', 'CALLS_LTP', 'CALLS_Net_Chng', 'Strike_Price', ...]
```

--------------------------------

### Get Equity Corporate Actions

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves corporate action announcements (dividend, bonus, split) for equities. Can filter by F&O securities and specify a period or date range.

```python
from nselib import capital_market

# All corporate actions over the last 6 months
df = capital_market.corporate_actions_for_equity(period='6M', fno_only=False)
print(df.head())

# F&O securities only
df_fno = capital_market.corporate_actions_for_equity(
    from_date='01-01-2024',
    to_date='31-03-2024',
    fno_only=True
)
```

--------------------------------

### Get AMFI Monthly Report Links

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Lists available AMFI (Association of Mutual Funds in India) monthly archive report links. This function does not require any parameters.

```python
links = cash_market.amfi_monthly_report_links()
```

--------------------------------

### Get Securities in F&O Ban Period

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Returns a list of securities currently banned from new F&O positions for a specified trading date. The output is a list of security symbols.

```python
from nselib import derivatives

banned = derivatives.fno_security_in_ban_period(trade_date='26-03-2025')
print(f"Banned securities: {banned}")
# e.g., ['BALRAMCHIN', 'GNFC', 'HINDCOPPER']
```

--------------------------------

### fno_index_list()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches a list of all F&O-eligible index underlyings, including popular indices like NIFTY and BANKNIFTY, along with their respective lot sizes.

```APIDOC
## fno_index_list()

### Description
Returns all F&O-eligible index underlyings (NIFTY, BANKNIFTY, etc.) with lot sizes.

### Method
`capital_market.fno_index_list()`

### Parameters
None

### Response Example
```json
{
  "index": "NIFTY 50",
  "lotSize": 50
}
```
```

--------------------------------

### equity_list()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Returns a DataFrame of all equity instruments listed on NSE, including symbol, company name, series, listing date, and face value.

```APIDOC
## equity_list()

### Description
Returns a DataFrame of all equity instruments listed on NSE with their symbol, company name, series, listing date, and face value.

### Method
`capital_market.equity_list()`

### Parameters
None

### Request Example
```python
from nselib import capital_market

df = capital_market.equity_list()
print(df.shape)
print(df.head())
```

### Response
#### Success Response (pandas.DataFrame)
DataFrame containing columns: 'SYMBOL', 'NAME OF COMPANY', 'SERIES', 'DATE OF LISTING', 'FACE VALUE'.

#### Response Example
```
SYMBOL | NAME OF COMPANY |  SERIES | DATE OF LISTING | FACE VALUE
20MICRONS | 20 Microns Limited      |  EQ     | 06-10-2008      | 5
```
```

--------------------------------

### Fetch Debt Securities Available for Trading

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the list of debt securities available for trading on the NSE WDM segment for a specific date. Handles 'FileNotFoundError' for holidays.

```python
from nselib import debt

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

```python
try:
    df = debt.securities_available_for_trading(trade_date='01-01-2026')  # holiday
except FileNotFoundError as e:
    print(f"No data: {e}")
```

--------------------------------

### Enable and Use nselib Logging

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Enables detailed logging for nselib to help with debugging network requests and API responses. Set the logging level to DEBUG to see trace logs.

```python
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')
```

--------------------------------

### Get Live Most Active F&O Underlyings

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves the most actively traded underlying assets in the live derivatives market. The output is a DataFrame, and you can select specific columns like 'symbol', 'totalVal', 'totalVol', and 'noofContracts'.

```python
from nselib import derivatives

df = derivatives.live_most_active_underlying()
print(df[['symbol', 'totalVal', 'totalVol', 'noofContracts']].head())
```

--------------------------------

### daily_volatility()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the NSCCL F&O daily volatility report (FOVOLT file) for a specific trading date.

```APIDOC
## daily_volatility()

### Description
Fetches the NSCCL F&O daily volatility report (FOVOLT file) for a specific trading date.

### Method
```python
derivatives.daily_volatility(trade_date: str)
```

### Parameters
#### Path Parameters
- **trade_date** (str) - Required - The trading date in DD-MM-YYYY format.
```

--------------------------------

### Import Derivatives Module

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Imports the derivatives module from the nselib library. This is a prerequisite for using derivative-related functions.

```python
from nselib import derivatives
```

--------------------------------

### Fetch Monthly and Daily Capital Market Data

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieve monthly or daily data for capital market segments by specifying the data type, year, and optionally month.

```python
df_monthly = capital_market.business_growth_cm_segment(
    data_type='monthly',
    from_year='2025',
    to_year='2026'
)
```

```python
# Daily data for a specific month
df_daily = capital_market.business_growth_cm_segment(
    data_type='daily',
    month='Mar',
    year='26'
)
```

--------------------------------

### Get Historical Index OHLC Data

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches historical OHLC data for any NSE index, such as NIFTY 50 or NIFTY BANK, for a specified period. Allows fetching data by a predefined period or a custom date range.

```python
from nselib import capital_market

df = capital_market.index_data(index='NIFTY 50', from_date='01-01-2024', to_date='31-03-2024')
print(df.head())

df_bank = capital_market.index_data(index='NIFTY BANK', period='6M')
print(df_bank[['Date', 'Open', 'High', 'Low', 'Close']].tail())
```

--------------------------------

### Fetch F&O Daily Volatility Report

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the NSCCL F&O daily volatility report (FOVOLT file) for a specific trading date.

```python
from nselib import derivatives

df = derivatives.daily_volatility(trade_date='17-04-2026')
print(df.head())
```

--------------------------------

### fii_dii_trading_activity

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves FII/DII buy-sell activity data.

```APIDOC
## fii_dii_trading_activity()

### Description
Retrieves FII/DII buy-sell activity data.

### Method
`capital_market.fii_dii_trading_activity()`

### Parameters
No parameters required.
```

--------------------------------

### List AMFI Monthly Report Links

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Scrapes the AMFI monthly archive page to get a DataFrame of all available report links, including month, year, file type, and URL. Useful for identifying and accessing historical reports.

```python
from nselib import cash_market

links_df = cash_market.amfi_monthly_report_links()
print(links_df.columns.tolist())
# ['REPORT_MONTH', 'PERIOD_LABEL', 'REPORT_YEAR', 'REPORT_MONTH_NUM', 'FILE_TYPE', 'IS_REVISED', 'SOURCE_URL']
print(links_df.tail())
```

--------------------------------

### Get Block Deal Transactions

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches all block deal transactions for the requested period. Block deals are large negotiated trades executed on a dedicated block window. Supports fetching data by a predefined period like '1W'.

```python
from nselib import capital_market

df = capital_market.block_deals_data(period='1W')
print(df)
```

--------------------------------

### Import Indices Module

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Imports the indices module from the nselib library, enabling access to index-related data functions.

```python
from nselib import indices
```

--------------------------------

### short_selling_data()

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the NSE short selling report for the given period, showing net short positions across securities.

```APIDOC
## short_selling_data()

### Description
Fetches the NSE short selling report for the given period, showing net short positions across securities.

### Method
`capital_market.short_selling_data(from_date: str, to_date: str)`

### Parameters
#### Path Parameters
None

#### Query Parameters
- **from_date** (str) - Required - The start date for the report (e.g., '01-06-2024').
- **to_date** (str) - Required - The end date for the report (e.g., '30-06-2024').

### Request Example
```python
from nselib import capital_market
df = capital_market.short_selling_data(from_date='01-06-2024', to_date='30-06-2024')
print(df)
```

### Response
#### Success Response (200)
Returns a pandas DataFrame containing the short selling data.
```

--------------------------------

### Get India VIX Historical Data

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves historical OHLC data for India VIX (volatility index) for a specified period. Supports predefined periods like '1W' or custom date ranges using 'from_date' and 'to_date'.

```python
from nselib import capital_market

# Last 1 week
df = capital_market.india_vix_data(period='1W')
print(df.columns.tolist())  # ['Date', 'Open', 'High', 'Low', 'Close', 'PrevClose', 'Change', '%Change']

# Custom range
df = capital_market.india_vix_data(from_date='01-01-2024', to_date='30-06-2024')
print(df.tail())
```

--------------------------------

### market_watch_all_indices

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves a live snapshot of all indices.

```APIDOC
## market_watch_all_indices()

### Description
Retrieves a live snapshot of all indices on the NSE.

### Method
`capital_market.market_watch_all_indices()`

### Parameters
No parameters required.
```

--------------------------------

### securities_available_for_trading

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the list of debt securities available for trading on the NSE WDM segment for a specific date.

```APIDOC
## securities_available_for_trading()

### Description
Fetches the list of debt securities available for trading on the NSE WDM (Wholesale Debt Market) segment for a specific date. Raises `FileNotFoundError` if no data is available for the given date, which may occur on holidays.

### Parameters
#### Query Parameters
- **trade_date** (str) - Required - The date for which to fetch the securities list (e.g., '20-12-2025').

### Request Example
```python
from nselib import debt

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

try:
    df = debt.securities_available_for_trading(trade_date='01-01-2026')  # holiday
except FileNotFoundError as e:
    print(f"No data: {e}")
```
```

--------------------------------

### Get Bulk Deal Transactions

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves all reported bulk deal transactions for a specified period. Bulk deals involve trades exceeding 0.5% of total listed shares in a single transaction. Supports fetching data for a duration or a custom date range.

```python
from nselib import capital_market

df = capital_market.bulk_deal_data(period='1M')
print(df.columns.tolist())
# ['Date', 'Symbol', 'Security Name', 'Client Name', 'Buy / Sell', 'Quantity Traded', 'Trade Price / Wt. Avg. Price']

df_range = capital_market.bulk_deal_data(from_date='01-01-2024', to_date='31-03-2024')
print(df_range)
```

--------------------------------

### fno_equity_list

Source: https://github.com/ruchitanmay/nselib/blob/main/README.md

Retrieves a list of F&O (Futures and Options) eligible equities along with their lot sizes.

```APIDOC
## fno_equity_list()

### Description
Retrieves a list of F&O eligible equities with their lot sizes.

### Method
`capital_market.fno_equity_list()`

### Parameters
No parameters required.
```

--------------------------------

### Fetch NSE Short Selling Report

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Retrieves the NSE short selling report for a specified date range. Requires `from_date` and `to_date` parameters.

```python
from nselib import capital_market

df = capital_market.short_selling_data(from_date='01-06-2024', to_date='30-06-2024')
print(df)
```

--------------------------------

### Get Nifty Index Constituent Lists

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches the current constituents for major Nifty indices like NIFTY 50, NIFTY Next 50, NIFTY Midcap 150, and NIFTY Smallcap 250. Returns company name, industry, and symbol for each constituent.

```python
from nselib import capital_market

nifty50 = capital_market.nifty50_equity_list()
print(nifty50[['Company Name', 'Industry', 'Symbol']].head())

next50 = capital_market.niftynext50_equity_list()
midcap = capital_market.niftymidcap150_equity_list()
smallcap = capital_market.niftysmallcap250_equity_list()
```

--------------------------------

### nsdl_fpi_investment_activity

Source: https://context7.com/ruchitanmay/nselib/llms.txt

Fetches FPI (Foreign Portfolio Investor) equity and debt investment flow data for a specific reporting date from the NSDL portal.

```APIDOC
## `nsdl_fpi_investment_activity(trade_date: str)`

### Description
Fetches FPI (Foreign Portfolio Investor) equity and debt investment flow data for a specific reporting date from the NSDL portal.

### Parameters
#### Query Parameters
- **trade_date** (str) - Required - The reporting date for which to fetch FPI investment activity (e.g., '30-10-2025').

### Request Example
```python
from nselib import cash_market

df = cash_market.nsdl_fpi_investment_activity(trade_date='30-10-2025')
print(df)
```

### Response
#### Success Response (200)
- **fpi_investment_data** (DataFrame) - A DataFrame containing FPI investment flow data.
- **REPORT_DATE** (str) - The reporting date.
- **ASSET_CLASS** (str) - The asset class (Equity/Debt).
- **INVESTMENT_ROUTE** (str) - The investment route.
- **GROSS_PURCHASES_RS_CR** (float) - Gross purchases in Crores of Rupees.
- **GROSS_SALES_RS_CR** (float) - Gross sales in Crores of Rupees.
- **NET_INVESTMENT_RS_CR** (float) - Net investment in Crores of Rupees.
- **NET_INVESTMENT_USD_MN** (float) - Net investment in Millions of USD.
- **USD_INR_CONVERSION** (float) - USD to INR conversion rate.
```