### Install NSEPython Library

Source: https://context7.com/aeron7/nsepython/llms.txt

Install the NSEPython library using pip. Choose the Local edition for Windows or the Server edition for cloud environments.

```bash
# Local edition (Windows laptops)
pip install nsepython
```

```bash
# Server edition (AWS, Google Colab, DigitalOcean)
pip install nsepythonserver
```

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

### Install NSEPython Local Edition

Source: https://github.com/aeron7/nsepython/blob/master/README.md

Use pip to install the local edition of NSEPython, compatible with Windows 11 and 10.

```bash
pip install nsepython
```

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

### Install NSEPython Server Edition

Source: https://github.com/aeron7/nsepython/blob/master/README.md

Use pip to install the server edition of NSEPython, suitable for AWS, Google Colab, and DigitalOcean environments.

```bash
pip install nsepythonserver
```

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

### Get Full Bulk Deals Archive with get_bulkdeals()

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads the complete archive of bulk deals as a CSV file, accessible as a pandas DataFrame. Provides details on large trades.

```python
from nsepython import get_bulkdeals

df = get_bulkdeals()
print(df.columns.tolist())
# ['Date', 'Symbol', 'Security Name', 'Client Name', 'Buy/Sell', 'Quantity Traded', 'Trade Price / Wght Avg Price']
print(df.head())
```

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

### Get Live Large Deals

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves intraday bulk deals, block deals, or short selling data as a pandas DataFrame. Displays deal details.

```python
from nsepython import nse_largedeals

# Bulk deals
bulk = nse_largedeals(mode="bulk_deals")
print(bulk.head())

# Block deals
block = nse_largedeals(mode="block_deals")
print(block.head())
```

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

### Get All NSE Equity Symbols with nse_eq_symbols()

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns a list of all symbols currently listed in the NSE equity segment. Useful for validating symbols or getting a complete list.

```python
from nsepython import nse_eq_symbols

symbols = nse_eq_symbols()
print(f"Total NSE equity symbols: {len(symbols)}")  # e.g. 2400+
print(symbols[:10])
# ['20MICRONS', '21STCENMGM', '360ONE', '3IINFOLTD', '3MINDIA', ...]
```

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

### Get Full Block Deals Archive with get_blockdeals()

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads the complete archive of block deals as a CSV file, accessible as a pandas DataFrame. Useful for tracking significant block trades.

```python
from nsepython import get_blockdeals

df = get_blockdeals()
print(df.head())
```

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

### Get Live Market Status

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns the current market status (open/closed) for all segments of the NSE.

```python
from nsepython import nse_marketStatus

status = nse_marketStatus()
print(status)
```

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

### Get Pre-Open Gainers and Losers

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns two DataFrames for pre-open gainers (pChange > filter) and losers (pChange < -filter). Displays symbol and percentage change for top entries.

```python
from nsepython import nse_preopen_movers

gainers, losers = nse_preopen_movers(key="FO", filter=1.5)
print("Top pre-open gainers:")
print(gainers[["symbol", "pChange"]].head())
print("Top pre-open losers:")
print(losers[["symbol", "pChange"]].head())
```

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

### Get F&O Lot Sizes with nse_get_fno_lot_sizes()

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves lot sizes for NSE F&O instruments. Can return all lot sizes as a dictionary, a specific symbol's lot size, or a full DataFrame.

```python
from nsepython import nse_get_fno_lot_sizes

# All lot sizes as dict
all_lots = nse_get_fno_lot_sizes()
print(all_lots["NIFTY"])    # e.g. 50
print(all_lots["RELIANCE"]) # e.g. 250

# Specific symbol
lot = nse_get_fno_lot_sizes("BANKNIFTY")
print(lot)  # e.g. 15

# Full DataFrame
df = nse_get_fno_lot_sizes(mode="pandas")
print(df.head())
```

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

### Get List of NSE Index Names

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches a list of all available index names from the live indices feed.

```python
from nsepython import nse_get_index_list

indices = nse_get_index_list()
print(indices[:5])
# ['NIFTY 50', 'NIFTY NEXT 50', 'NIFTY 100', 'NIFTY 200', 'NIFTY 500']
```

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

### Get Historical Index PE, PB, and Dividend Yield

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves historical Price-to-Earnings (PE), Price-to-Book (PB), and Dividend Yield ratios for a NIFTY index over a specified period.

```python
from nsepython import index_pe_pb_div

df = index_pe_pb_div("NIFTY 50", "01-Jan-2023", "31-Dec-2023")
print(df.columns.tolist())
# ['HistoricalDate', 'pe', 'pb', 'divYield']
print(df.sort_values("HistoricalDate").tail())
```

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

### Get Price Band Hitters

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches stocks that have hit their upper circuit, lower circuit, or both. Displays symbol, last price, and percentage change for top entries.

```python
from nsepython import nse_price_band_hitters

# All stocks hitting either circuit
df = nse_price_band_hitters(bandtype="both", view="AllSec")
print(df[["symbol", "lastPrice", "pChange"]].head())

# Only upper circuit
df_upper = nse_price_band_hitters(bandtype="upper")
```

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

### Get Top 5 FNO Gainers

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns the top 5 gainers from the FNO universe, ranked by percentage change. The output is provided as a pandas DataFrame.

```python
from nsepython import nse_get_top_gainers

df = nse_get_top_gainers()
print(df[["symbol", "lastPrice", "pChange"]])
#    symbol  lastPrice  pChange
# 0    IDEA      15.30     4.80
# 1    SAIL     125.50     3.10
```

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

### Archive Security Price-Volume Data with security_wise_archive()

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves historical price, volume, and deliverable quantity data for a specific security symbol within a date range. Requires start date, end date, and symbol.

```python
from nsepython import security_wise_archive

df = security_wise_archive("01-01-2024", "31-03-2024", "HDFCBANK", series="EQ")
print(df[["CH_TIMESTAMP", "CH_CLOSING_PRICE", "CH_TOT_TRADED_QTY", "COP_DELIV_QTY"]].head())
```

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

### Get Corporate Financial Results with nse_results()

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches corporate financial results as a pandas DataFrame. Specify 'Quarterly' or 'Annual' for the period. Useful for analyzing company performance.

```python
from nsepython import nse_results

df = nse_results(index="equities", period="Quarterly")
print(df[["symbol", "companyName", "meetingDate", "purpose"]].head())

# Annual results
df_annual = nse_results(index="equities", period="Annual")
```

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

### Get India VIX (Volatility Index)

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves the current value of India VIX (Volatility Index) from the live all-indices feed.

```python
from nsepython import indiavix

vix = indiavix()
print(f"India VIX: {vix}")  # e.g. 13.45
```

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

### Get Full Index Information

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves a complete live data dictionary for any specified index by name. Includes details like last traded price, percentage change, high, and low.

```python
from nsepython import index_info

info = index_info("NIFTY BANK")
print(info["last"])
# e.g. 49800.0
print(info["percentChange"])
# e.g. -0.25
print(info["high"])
print(info["low"])
```

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

### Get Full Equity Quote with nse_eq

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches the complete equity quote JSON for a given stock symbol. Automatically detects if the symbol is an index or equity.

```python
from nsepython import nse_eq

data = nse_eq("RELIANCE")
print(data["priceInfo"]["lastPrice"])   # e.g. 2945.5
print(data["priceInfo"]["change"])      # e.g. 12.3
print(data["priceInfo"]["pChange"])     # e.g. 0.42
print(data["metadata"]["companyName"])  # "Reliance Industries Limited"
```

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

### Get Pre-Open Session Data

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves pre-open session data for specified keys like NIFTY, BANKNIFTY, or FO segment. Supports pandas DataFrame or raw JSON output. Displays symbol, prices, and percentage change.

```python
from nsepython import nse_preopen

# Pre-open for NIFTY constituents
df = nse_preopen("NIFTY")
print(df[["symbol", "lastPrice", "pChange", "finalPrice"]].head())

# Raw JSON
raw = nse_preopen("FO", type="raw")
```

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

### Get Top 5 FNO Losers

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves the top 5 losers from the FNO universe as a pandas DataFrame. Displays symbol, last price, and percentage change.

```python
from nsepython import nse_get_top_losers

df = nse_get_top_losers()
print(df[["symbol", "lastPrice", "pChange"]])
```

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

### Get Market Holidays

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves trading or clearing holiday data from NSE for the current year. Iterates through the first three F&O holidays to print the date and description.

```python
from nsepython import nse_holidays

holidays = nse_holidays(type="trading")
fo_holidays = holidays["FO"]
for h in fo_holidays[:3]:
    print(h["tradingDate"], "-", h["description"])
# 26-Jan-2024 - Republic Day
# 19-Apr-2024 - Good Friday
```

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

### Get Historical Large Deals

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches historical bulk, block, or short-selling deals for a specified date range. Displays symbol, client name, buy/sell status, quantity, and trade price.

```python
from nsepython import nse_largedeals_historical

df = nse_largedeals_historical("01-01-2024", "31-01-2024", mode="bulk_deals")
print(df[["symbol", "clientName", "buyOrSell", "quantityTraded", "tradePrice"]].head())
```

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

### Get Historical Index Total Returns

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches Total Return Index (TRI) values, which include price and reinvested dividends, for a NIFTY index over a given date range.

```python
from nsepython import index_total_returns

df = index_total_returns("NIFTY 50", "01-Jan-2024", "30-Apr-2024")
print(df[["HistoricalDate", "totalReturnsIndex"]].head())
```

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

### Get Full Metadata with nse_quote_meta

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns the complete metadata dictionary for an equity, future, or specific option contract. Useful for retrieving details like company name, industry, open interest, and implied volatility.

```python
from nsepython import nse_quote_meta

# Equity metadata
meta = nse_quote_meta("WIPRO")
print(meta.get("companyName"), meta.get("industry"))

# Option contract metadata
opt_meta = nse_quote_meta("NIFTY", "latest", "CE", 22500)
print(opt_meta.get("openInterest"))       # e.g. 845000
print(opt_meta.get("impliedVolatility"))  # e.g. 12.5
print(opt_meta.get("lastPrice"))          # e.g. 150.25
```

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

### Get F&O Participant Open Interest

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads the NSE F&O participant-wise open interest data as a CSV for a given date. Displays the head of the DataFrame with participant OI details.

```python
from nsepython import get_fao_participant_oi

df = get_fao_participant_oi("03-05-2024")
print(df.head())
# Columns: Client Type, Future Index Long, Future Index Short,
#          Option Index Call Long, Option Index Put Long, ...
```

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

### Get Live NSE Index Data

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves live data for all NIFTY indices from the niftyindices.com feed. Returns a DataFrame containing index names, last traded price, and percentage change.

```python
from nsepython import nse_index

df = nse_index()
print(df[["indexName", "last", "percentChange"]].head())
#        indexName      last  percentChange
# 0       NIFTY 50  22300.50           0.81
# 1  NIFTY NEXT 50  63120.00           0.45
```

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

### Get FII and DII Trading Activity

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns today's Foreign Institutional Investor (FII) and Domestic Institutional Investor (DII) net buy/sell data as a DataFrame, including category, buy value, sell value, and net value.

```python
from nsepython import nse_fiidii

df = nse_fiidii()
print(df)
#   category  buyValue  sellValue  netValue
# 0      FII   12345.5    11200.0    1145.5
# 1      DII    8900.0     9200.5    -300.5
```

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

### Download Daily Bhavcopy with get_bhavcopy()

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads end-of-day price and volume data for all securities for a specified date. Returns a pandas DataFrame.

```python
from nsepython import get_bhavcopy

df = get_bhavcopy("02-05-2024")
print(df.columns.tolist())
# ['SYMBOL', 'SERIES', 'DATE1', 'PREV_CLOSE', 'OPEN_PRICE', 'HIGH_PRICE',
#  'LOW_PRICE', 'LAST_PRICE', 'CLOSE_PRICE', 'AVG_PRICE', 'TTL_TRD_QNTY', ...]
print(df[df["SYMBOL"] == "RELIANCE"])
```

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

### get_bulkdeals()

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads the complete archive of bulk deals from the NSE as a CSV file. Returns a pandas DataFrame.

```APIDOC
## get_bulkdeals()

### Description
Downloads the complete bulk deals archive CSV.

### Returns
- pandas.DataFrame: A DataFrame containing the full bulk deals archive.

### Example
```python
from nsepython import get_bulkdeals

df = get_bulkdeals()
print(df.columns.tolist())
# ['Date', 'Symbol', 'Security Name', 'Client Name', 'Buy/Sell', 'Quantity Traded', 'Trade Price / Wght Avg Price']
print(df.head())
```
```

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

### Get Advances and Declines Data

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves advances and declines data for all Securities in the F&O segment. Can be returned as a pandas DataFrame or a raw dictionary.

```python
from nsepython import nse_get_advances_declines

df = nse_get_advances_declines()
print(df[["symbol", "lastPrice", "change", "pChange"]].head())
```

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

### nse_largedeals(mode='bulk_deals')

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches live intraday data for bulk deals, block deals, or short selling. Returns data as a pandas DataFrame.

```APIDOC
## nse_largedeals(mode="bulk_deals")

### Description
Returns intraday bulk deals, block deals, or short selling data as a DataFrame.

### Method
`nse_largedeals(mode="bulk_deals")`

### Parameters
* **mode** (string) - Optional - Type of deals to fetch ('bulk_deals', 'block_deals', 'short_selling'). Defaults to 'bulk_deals'.

### Response
Returns a pandas DataFrame containing deal information.

### Request Example
```python
from nsepython import nse_largedeals

# Bulk deals
bulk = nse_largedeals(mode="bulk_deals")
print(bulk.head())

# Block deals
block = nse_largedeals(mode="block_deals")
print(block.head())
```
```

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

### Calculate Stock Beta vs Index with get_beta()

Source: https://context7.com/aeron7/nsepython/llms.txt

Calculates the beta of a stock relative to a specified index using historical daily returns over a given number of days. Defaults to NIFTY 50 and 365 days.

```python
from nsepython import get_beta


```

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

### Calculate Beta of RELIANCE vs NIFTY 50

Source: https://context7.com/aeron7/nsepython/llms.txt

Calculates the beta of RELIANCE against NIFTY 50 for a 1-year period. Ensure the 'get_beta' function is available in your environment.

```python
beta = get_beta("RELIANCE", days=365, symbol2="NIFTY 50")
print(f"RELIANCE Beta: {beta}")  # e.g. 1.032
```

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

### Get Unified Quote with nse_quote

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns equity data or index derivative data based on the symbol. Accepts an optional `section` parameter for granular data.

```python
from nsepython import nse_quote

# Equity stock quote
eq_data = nse_quote("TCS")

# Index derivative data
idx_data = nse_quote("NIFTY")

# Specific section (trade info with delivery data)
trade_data = nse_quote("INFY", section="trade_info")
print(trade_data["marketDeptOrderBook"]["tradeInfo"]["totalBuyQuantity"])
```

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

### nse_preopen(key='NIFTY', type='pandas')

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves pre-open session data for specified market segments like NIFTY, BANKNIFTY, or FO. Supports pandas DataFrame or raw JSON output.

```APIDOC
## nse_preopen(key="NIFTY", type="pandas")

### Description
Returns pre-open session data for NIFTY, BANKNIFTY, FO segment, or any other key.

### Method
`nse_preopen(key="NIFTY", type="pandas")`

### Parameters
* **key** (string) - Optional - The market key to fetch data for (e.g., 'NIFTY', 'BANKNIFTY', 'FO'). Defaults to 'NIFTY'.
* **type** (string) - Optional - The output format ('pandas' for DataFrame, 'raw' for JSON). Defaults to 'pandas'.

### Response
Returns a pandas DataFrame or raw JSON containing pre-open session data with columns like 'symbol', 'lastPrice', 'pChange', 'finalPrice'.

### Request Example
```python
from nsepython import nse_preopen

# Pre-open for NIFTY constituents
df = nse_preopen("NIFTY")
print(df[["symbol", "lastPrice", "pChange", "finalPrice"]].head())

# Raw JSON
raw = nse_preopen("FO", type="raw")
```
```

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

### Download Historical Derivatives Data

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads historical derivatives (futures or options) data in 40-day chunks. Requires symbol, date range, instrument type, and expiry date.

```python
from nsepython import derivative_history

# Historical futures data for NIFTY
df = derivative_history(
    symbol="NIFTY",
    start_date="01-01-2024",
    end_date="31-03-2024",
    instrumentType="futures",
    expiry_date="28-Mar-2024"
)
print(df[["FH_TIMESTAMP", "FH_CLOSE_PRICE", "FH_OPEN_INT"]].head())
```

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

### Fetch Raw NSE API Response with nsefetch

Source: https://context7.com/aeron7/nsepython/llms.txt

Use the `nsefetch` function for low-level NSE API requests. It establishes a session, refreshes cookies, and returns parsed JSON. Handles `ValueError` by returning an empty dict.

```python
from nsepython import nsefetch

# Fetch raw NSE API response for any endpoint
data = nsefetch("https://www.nseindia.com/api/allIndices")
print(data["data"][0])
# {'key': 'BROAD MARKET INDICES', 'index': 'NIFTY 50', 'indexSymbol': 'NIFTY 50',
#  'open': 22100.0, 'high': 22350.0, 'low': 22050.0, 'previousClose': 22120.0,
#  'last': 22300.0, 'change': 180.0, 'percentChange': 0.81, ...}
```

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

### get_blockdeals()

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads the complete archive of block deals from the NSE as a CSV file. Returns a pandas DataFrame.

```APIDOC
## get_blockdeals()

### Description
Downloads the complete block deals archive CSV.

### Returns
- pandas.DataFrame: A DataFrame containing the full block deals archive.

### Example
```python
from nsepython import get_blockdeals

df = get_blockdeals()
print(df.head())
```
```

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

### Get Last Traded Price with nse_quote_ltp

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns the Last Traded Price (LTP) for equity, futures, or options. Resolves 'latest' and 'next' expiry keywords automatically.

```python
from nsepython import nse_quote_ltp

# Equity LTP
print(nse_quote_ltp("RELIANCE"))           # e.g. 2945.5

# Nearest futures LTP
print(nse_quote_ltp("RELIANCE", "latest", "Fut"))  # e.g. 2950.0

# Next expiry futures
print(nse_quote_ltp("RELIANCE", "next", "Fut"))    # e.g. 2955.0

# BANKNIFTY Put option LTP
print(nse_quote_ltp("BANKNIFTY", "latest", "PE", 48000))  # e.g. 320.5

# Specific expiry date
print(nse_quote_ltp("BANKNIFTY", "19-Jun-2025", "PE", 48000))
```

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

### Fetch Raw Option Chain Data

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches the complete option chain for a given symbol and transforms it into a standardized format. Useful for further analysis of options data.

```python
from nsepython import nse_optionchain_scrapper

payload = nse_optionchain_scrapper("NIFTY")
print(payload["underlyingValue"])
# e.g. 22300.5
print(len(payload["data"]))
# number of strike-expiry combinations

# First entry
entry = payload["data"][0]
print(entry["strikePrice"])
print(entry["CE"]["lastPrice"])
# Call LTP
print(entry["PE"]["openInterest"])
# Put OI
```

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

### index_pe_pb_div

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns historical Price-to-Earnings, Price-to-Book, and Dividend Yield ratios for any NIFTY index.

```APIDOC
## index_pe_pb_div(symbol, start_date, end_date)

### Description
Returns historical Price-to-Earnings, Price-to-Book, and Dividend Yield ratios for any NIFTY index.

### Parameters
#### Path Parameters
- **symbol** (string) - Required - The index symbol (e.g., "NIFTY 50").
- **start_date** (string) - Required - The start date for the historical data (format: DD-Mon-YYYY).
- **end_date** (string) - Required - The end date for the historical data (format: DD-Mon-YYYY).

### Request Example
```python
from nsepython import index_pe_pb_div

df = index_pe_pb_div("NIFTY 50", "01-Jan-2023", "31-Dec-2023")
print(df.columns.tolist())
print(df.sort_values("HistoricalDate").tail())
```

### Response
#### Success Response (200)
- **DataFrame** - A pandas DataFrame containing historical PE, PB, and Dividend Yield ratios.
```

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

### Calculate Black-Scholes Option Greeks with black_scholes_dexter()

Source: https://context7.com/aeron7/nsepython/llms.txt

Computes Black-Scholes option premiums and greeks (theta, delta, gamma, vega, rho). Defaults to India VIX for volatility if not provided. Requires spot price, strike price, and days to expiry.

```python
from nsepython import black_scholes_dexter

# NIFTY CE option: Spot=22300, Strike=22500, DTE=7 days
# σ auto-fetched from India VIX
call_theta, put_theta, call_premium, put_premium, \
call_delta, put_delta, gamma, vega, call_rho, put_rho = \
    black_scholes_dexter(S0=22300, X=22500, t=7)

print(f"Call Premium : {call_premium:.2f}")  # e.g. 145.30
print(f"Put Premium  : {put_premium:.2f}")   # e.g. 340.20
print(f"Call Delta   : {call_delta:.4f}")    # e.g. 0.4123
print(f"Put Delta    : {put_delta:.4f}")     # e.g. -0.5877
print(f"Gamma        : {gamma:.6f}")
print(f"Vega         : {vega:.4f}")

# With explicit volatility and risk-free rate
results = black_scholes_dexter(S0=22300, X=22500, t=7, σ=14.5, r=6.5)
```

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

### Get List of FNO-Eligible Symbols

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns a list of all symbols currently included in the Futures & Options (FNO) segment, including index names. Also provides the total count of such symbols.

```python
from nsepython import fnolist

symbols = fnolist()
print(symbols[:10])
# ['NIFTY', 'FINNIFTY', 'BANKNIFTY', 'RELIANCE', 'TCS', 'INFY', ...]
print(len(symbols))  # e.g. 185
```

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

### Get Quote for a Specific Index

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves the live quote dictionary for a specified index name. Provides details like last traded price, percentage change, high, and low.

```python
from nsepython import nse_get_index_quote

q = nse_get_index_quote("NIFTY 50")
print(q["last"])
# e.g. 22300.5
print(q["percentChange"])
# e.g. 0.81
print(q["high"])
# e.g. 22400.0
print(q["low"])
# e.g. 22100.0
```

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

### nse_fiidii(mode='pandas')

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches today's Foreign Institutional Investor (FII) and Domestic Institutional Investor (DII) net buy/sell data. Returns data as a pandas DataFrame.

```APIDOC
## nse_fiidii(mode="pandas")

### Description
Returns today's Foreign Institutional Investor (FII) and Domestic Institutional Investor (DII) net buy/sell data.

### Method
`nse_fiidii(mode="pandas")`

### Parameters
* **mode** (string) - Optional - Output format ('pandas' for DataFrame). Defaults to 'pandas'.

### Response
Returns a pandas DataFrame with columns 'category', 'buyValue', 'sellValue', 'netValue'.

### Request Example
```python
from nsepython import nse_fiidii

df = nse_fiidii()
print(df)
#   category  buyValue  sellValue  netValue
# 0      FII   12345.5    11200.0    1145.5
# 1      DII    8900.0     9200.5    -300.5
```
```

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

### Get Historical Expiry Dates

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches a list of expiry dates for a specified symbol within a date range. Includes the first expiry after the end date, useful for rolling strategies.

```python
from nsepython import expiry_history

dates = expiry_history("NIFTY", start_date="01-01-2024", end_date="31-03-2024", type="options")
print(dates)
# ['25-Jan-2024', '29-Feb-2024', '28-Mar-2024', '25-Apr-2024']
```

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

### get_beta(symbol, days=365, symbol2="NIFTY 50")

Source: https://context7.com/aeron7/nsepython/llms.txt

Calculates the beta of a specified stock relative to an index (defaulting to NIFTY 50) using historical daily returns over a given number of days. Returns a float.

```APIDOC
## get_beta(symbol, days=365, symbol2="NIFTY 50")

### Description
Calculates the beta of a stock relative to an index using historical daily returns over the specified number of days.

### Parameters
- **symbol** (str) - Required - The stock symbol for which to calculate beta.
- **days** (int) - Optional - The number of past days to consider for the calculation (default: 365).
- **symbol2** (str) - Optional - The index symbol to compare against (default: "NIFTY 50").

### Returns
- float: The calculated beta value of the stock relative to the index.

### Example
```python
from nsepython import get_beta

# Calculate beta for HDFCBANK against NIFTY 50 over 1 year
beta_hdfc = get_beta("HDFCBANK")
print(f"Beta of HDFCBANK: {beta_hdfc:.4f}")

# Calculate beta for RELIANCE against SENSEX over 6 months
beta_reliance_sensex = get_beta("RELIANCE", days=180, symbol2="SENSEX")
print(f"Beta of RELIANCE vs SENSEX: {beta_reliance_sensex:.4f}")
```
```

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

### Get Full Derivatives Quote with nse_fno

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches the complete derivatives quote for an FNO-eligible symbol, including all futures and options data. This function returns the full payload from the NSE API.

```python
from nsepython import nse_fno

data = nse_fno("HDFC")
# Returns full payload with futures and options data
print(data.keys())
```

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

### index_info

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns the full live data dictionary for any index by name.

```APIDOC
## index_info(index)

### Description
Returns the complete live data dictionary for any index by name.

### Parameters
#### Path Parameters
- **index** (string) - Required - The name of the index (e.g., "NIFTY BANK").

### Request Example
```python
from nsepython import index_info

info = index_info("NIFTY BANK")
print(info["last"])
print(info["percentChange"])
print(info["high"])
print(info["low"])
```

### Response
#### Success Response (200)
- **dict** - A dictionary containing the full live data for the specified index.
```

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

### Get Most Active Stocks

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches most actively traded securities, ETFs, or SME stocks. Can be sorted by traded value or volume. Displays symbol, last price, and total turnover for top entries.

```python
from nsepython import nse_most_active

# Most active by value (default)
df = nse_most_active(type="securities", sort="value")
print(df[["symbol", "lastPrice", "totalTurnover"]].head())

# Most active ETFs by volume
df_etf = nse_most_active(type="etf", sort="volume")
```

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

### nse_get_fno_lot_sizes(symbol="all", mode="list")

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves lot sizes for NSE F&O instruments. Can return all lot sizes as a dictionary, a specific symbol's lot size, or a DataFrame of all lot sizes.

```APIDOC
## nse_get_fno_lot_sizes(symbol="all", mode="list")

### Description
Returns the lot sizes for FNO instruments, either as a dict or as a pandas DataFrame.

### Parameters
- **symbol** (str) - Optional - The FNO symbol to query (default: "all").
- **mode** (str) - Optional - The output format, "list" for dictionary, "pandas" for DataFrame (default: "list").

### Returns
- dict or pandas.DataFrame: Lot sizes information.

### Example
```python
from nsepython import nse_get_fno_lot_sizes

# All lot sizes as dict
all_lots = nse_get_fno_lot_sizes()
print(all_lots["NIFTY"])    # e.g. 50
print(all_lots["RELIANCE"]) # e.g. 250

# Specific symbol
lot = nse_get_fno_lot_sizes("BANKNIFTY")
print(lot)  # e.g. 15

# Full DataFrame
df = nse_get_fno_lot_sizes(mode="pandas")
print(df.head())
```
```

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

### nsefetch(payload)

Source: https://context7.com/aeron7/nsepython/llms.txt

Low-level NSE API request with session handling. This function establishes a browser-like session against NSEIndia, refreshes cookies, and returns the parsed JSON response. It handles ValueError gracefully by returning an empty dict.

```APIDOC
## nsefetch(payload) — Low-level NSE API request with session handling

### Description
The internal HTTP engine that establishes a browser-like session against NSEIndia, refreshes cookies by visiting the homepage and the option-chain page, and returns the parsed JSON response. All higher-level functions delegate to this. Handles `ValueError` gracefully by returning an empty dict.

### Parameters
#### Path Parameters
- **payload** (string) - Required - The URL endpoint to fetch data from.

### Request Example
```python
from nsepython import nsefetch

# Fetch raw NSE API response for any endpoint
data = nsefetch("https://www.nseindia.com/api/allIndices")
print(data["data"][0])
```

### Response
#### Success Response (200)
- **data** (dict) - Parsed JSON response from the NSE API.

#### Response Example
```json
{
  "data": [
    {
      "key": "BROAD MARKET INDICES",
      "index": "NIFTY 50",
      "indexSymbol": "NIFTY 50",
      "open": 22100.0,
      "high": 22350.0,
      "low": 22050.0,
      "previousClose": 22120.0,
      "last": 22300.0,
      "change": 180.0,
      "percentChange": 0.81
    }
  ]
}
```
```

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

### get_bhavcopy(date)

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads the daily bhavcopy (end-of-day price and volume data for all securities) for a specified date. Returns a pandas DataFrame.

```APIDOC
## get_bhavcopy(date)

### Description
Downloads the full-day bhavcopy (EOD price/volume data for all securities) for the given date as a DataFrame.

### Parameters
- **date** (str) - Required - The date for which to download the bhavcopy, in DD-MM-YYYY format.

### Returns
- pandas.DataFrame: A DataFrame containing the bhavcopy data for the specified date.

### Example
```python
from nsepython import get_bhavcopy

df = get_bhavcopy("02-05-2024")
print(df.columns.tolist())
# ['SYMBOL', 'SERIES', 'DATE1', 'PREV_CLOSE', 'OPEN_PRICE', 'HIGH_PRICE',
#  'LOW_PRICE', 'LAST_PRICE', 'CLOSE_PRICE', 'AVG_PRICE', 'TTL_TRD_QNTY', ...]
print(df[df["SYMBOL"] == "RELIANCE"])
```
```

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

### derivative_history

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads historical derivatives (futures or options) data in 40-day chunks.

```APIDOC
## derivative_history(symbol, start_date, end_date, instrumentType, expiry_date, strikePrice="", optionType="")

### Description
Downloads historical derivatives (futures or options) data in 40-day chunks.

### Parameters
#### Path Parameters
- **symbol** (string) - Required - The symbol for the derivative (e.g., "NIFTY").
- **start_date** (string) - Required - The start date in DD-MM-YYYY format.
- **end_date** (string) - Required - The end date in DD-MM-YYYY format.
- **instrumentType** (string) - Required - Accepts "futures" or "options".
- **expiry_date** (string) - Required - The expiry date in DD-Mon-YYYY format (e.g., "28-Mar-2024").
- **strikePrice** (string) - Optional - The strike price for options. Defaults to an empty string.
- **optionType** (string) - Optional - The option type ("CE" or "PE") for options. Defaults to an empty string.

### Request Example
```python
from nsepython import derivative_history

# Historical futures data
df = derivative_history(
    symbol="NIFTY",
    start_date="01-01-2024",
    end_date="31-03-2024",
    instrumentType="futures",
    expiry_date="28-Mar-2024"
)
print(df[["FH_TIMESTAMP", "FH_CLOSE_PRICE", "FH_OPEN_INT"]].head())
```

### Response
#### Success Response (DataFrame)
Returns a pandas DataFrame with historical derivatives data. Columns include:
- FH_TIMESTAMP
- FH_CLOSE_PRICE
- FH_OPEN_INT
...
```

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

### Get Expiry Date and DTE from Payload

Source: https://context7.com/aeron7/nsepython/llms.txt

Extracts the expiry date and Days-To-Expiry (DTE) for the i-th expiry from an option chain payload, filtering out past dates. Requires an option chain payload.

```python
from nsepython import nse_optionchain_scrapper, nse_expirydetails

payload = nse_optionchain_scrapper("FINNIFTY")
expiry_date, dte = nse_expirydetails(payload, i=0)
print(f"Nearest expiry: {expiry_date}, DTE: {dte}")
# Nearest expiry: 2025-05-27, DTE: 5

next_expiry_date, next_dte = nse_expirydetails(payload, i=1)
print(f"Next expiry: {next_expiry_date}, DTE: {next_dte}")
```

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

### Build Structured OI Chain DataFrame

Source: https://context7.com/aeron7/nsepython/llms.txt

Builds a formatted Open Interest chain DataFrame for a given symbol and expiry. Returns the DataFrame, underlying spot value, and timestamp. Supports 'full' (with bid/ask) and 'compact' modes.

```python
from nsepython import oi_chain_builder

# Full mode (includes bid/ask)
df, spot, ts = oi_chain_builder("BANKNIFTY", expiry="latest", oi_mode="full")
print(f"Spot: {spot}, Timestamp: {ts}")
print(df[["Strike Price", "CALLS_OI", "CALLS_LTP", "PUTS_OI", "PUTS_LTP"]].head(10))

# Compact mode
df2, spot2, ts2 = oi_chain_builder("NIFTY", oi_mode="compact")
print(df2.columns.tolist())
# ['CALLS_OI', 'CALLS_Chng in OI', 'CALLS_Volume', 'CALLS_IV',
#  'CALLS_LTP', 'CALLS_Net Chng', 'Strike Price', 'PUTS_OI', ...]
```

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

### Calculate Put-Call Ratio (PCR)

Source: https://context7.com/aeron7/nsepython/llms.txt

Calculates the Put-Call Ratio (total PE OI / total CE OI) for a given expiry index from an option chain payload. Use `inp=0` for the nearest expiry and `inp=1` for the next.

```python
from nsepython import nse_optionchain_scrapper, pcr

payload = nse_optionchain_scrapper("NIFTY")

pcr_latest = pcr(payload, inp=0)
# Nearest expiry
pcr_next   = pcr(payload, inp=1)
# Next expiry

print(f"PCR (nearest expiry): {pcr_latest:.2f}")
# e.g. 1.23
print(f"PCR (next expiry):    {pcr_next:.2f}")
# e.g. 0.98
```

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

### fnolist

Source: https://context7.com/aeron7/nsepython/llms.txt

Returns a list of all symbols currently in the Futures & Options segment, including index names.

```APIDOC
## fnolist()

### Description
Returns a list of all symbols currently in the Futures & Options segment, including index names.

### Method
GET

### Endpoint
/api/fno-list

### Parameters
None

### Request Example
```python
from nsepython import fnolist

symbols = fnolist()
print(symbols[:10])
print(len(symbols))
```

### Response
#### Success Response (200)
- **list** - A list of FNO-eligible symbols.
```

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

### nse_marketStatus()

Source: https://context7.com/aeron7/nsepython/llms.txt

Retrieves the current live market status (open or closed) for all NSE market segments. Returns data as a dictionary.

```APIDOC
## nse_marketStatus()

### Description
Returns the current market status (open/closed) for all segments.

### Method
`nse_marketStatus()`

### Parameters
None

### Response
Returns a dictionary containing the market status for different segments.

### Request Example
```python
from nsepython import nse_marketStatus

status = nse_marketStatus()
print(status)
```
```

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

### Download Historical Equity Data

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads historical OHLCV data for an equity in batches. Merges data into a single DataFrame in reverse-chronological order.

```python
from nsepython import equity_history

df = equity_history("SBIN", "EQ", "01-01-2024", "30-04-2024")
print(df.columns.tolist())
# ['CH_SYMBOL', 'CH_SERIES', 'CH_MARKET_TYPE', 'CH_TRADE_HIGH_PRICE',
#  'CH_TRADE_LOW_PRICE', 'CH_OPENING_PRICE', 'CH_CLOSING_PRICE',
#  'CH_LAST_TRADED_PRICE', 'CH_PREVIOUS_CLS_PRICE', 'CH_TOT_TRADED_QTY',
#  'CH_TOT_TRADED_VAL', 'CH_52WEEK_HIGH_PRICE', 'CH_52WEEK_LOW_PRICE',
#  'CH_TOTAL_TRADES', 'CH_ISIN', 'CH_TIMESTAMP', ...]
print(df[["CH_TIMESTAMP", "CH_CLOSING_PRICE"]].head())
```

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

### equity_history

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads historical price-volume data for an equity in 40-day batches and merges them into a single DataFrame in reverse-chronological order.

```APIDOC
## equity_history(symbol, series, start_date, end_date)

### Description
Downloads historical price-volume data for an equity in 40-day batches (NSE API limit) and merges them into a single DataFrame in reverse-chronological order.

### Parameters
#### Path Parameters
- **symbol** (string) - Required - The equity symbol (e.g., "SBIN").
- **series** (string) - Required - The series type (e.g., "EQ").
- **start_date** (string) - Required - The start date in DD-MM-YYYY format.
- **end_date** (string) - Required - The end date in DD-MM-YYYY format.

### Request Example
```python
from nsepython import equity_history

df = equity_history("SBIN", "EQ", "01-01-2024", "30-04-2024")
print(df.columns.tolist())
print(df[["CH_TIMESTAMP", "CH_CLOSING_PRICE"]].head())
```

### Response
#### Success Response (DataFrame)
Returns a pandas DataFrame with historical OHLCV data. Columns include:
- CH_SYMBOL
- CH_SERIES
- CH_MARKET_TYPE
- CH_TRADE_HIGH_PRICE
- CH_TRADE_LOW_PRICE
- CH_OPENING_PRICE
- CH_CLOSING_PRICE
- CH_LAST_TRADED_PRICE
- CH_PREVIOUS_CLS_PRICE
- CH_TOT_TRADED_QTY
- CH_TOT_TRADED_VAL
- CH_52WEEK_HIGH_PRICE
- CH_52WEEK_LOW_PRICE
- CH_TOTAL_TRADES
- CH_ISIN
- CH_TIMESTAMP
...
```

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

### nse_optionchain_scrapper

Source: https://context7.com/aeron7/nsepython/llms.txt

Fetches the complete option chain for a given symbol (equity or index) and transforms the new NSE API structure into a standardized format.

```APIDOC
## nse_optionchain_scrapper(symbol)

### Description
Fetches the complete option chain for a given symbol (equity or index) and transforms the new NSE API structure into a standardized `{strikePrice, expiryDate, CE, PE}` format compatible with all downstream functions.

### Parameters
#### Path Parameters
- **symbol** (string) - Required - The equity or index symbol (e.g., "NIFTY").

### Request Example
```python
from nsepython import nse_optionchain_scrapper

payload = nse_optionchain_scrapper("NIFTY")
print(payload["underlyingValue"])
print(len(payload["data"]))

entry = payload["data"][0]
print(entry["strikePrice"])
print(entry["CE"]["lastPrice"])
print(entry["PE"]["openInterest"])
```

### Response
#### Success Response (JSON Object)
Returns a JSON object containing:
- **underlyingValue** (float): The current underlying spot price.
- **data** (list): A list of option chain data, where each element contains:
  - **strikePrice** (float)
  - **expiryDate** (string)
  - **CE** (object): Call European details.
  - **PE** (object): Put European details.
```

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

### get_fao_participant_oi(date)

Source: https://context7.com/aeron7/nsepython/llms.txt

Downloads the NSE F&O participant-wise open interest data for a specified date. Returns data as a pandas DataFrame.

```APIDOC
## get_fao_participant_oi(date)

### Description
Downloads the NSE F&O participant-wise open interest CSV for a given date.

### Method
`get_fao_participant_oi(date)`

### Parameters
* **date** (string) - Required - The date for which to fetch data (format: 'DD-MM-YYYY').

### Response
Returns a pandas DataFrame containing F&O participant-wise open interest data.

### Request Example
```python
from nsepython import get_fao_participant_oi

df = get_fao_participant_oi("03-05-2024")
print(df.head())
# Columns: Client Type, Future Index Long, Future Index Short,
#          Option Index Call Long, Option Index Put Long, ...
```
```