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Introduction
Are you looking for an easy way to fetch live stock market data directly from the National Stock Exchange of India (NSE)? Whether you’re a trader, investor, or developer, accessing real-time equity and F&O data, option chains, price details, and daily Bhav copies can be a game-changer.
Introducing NseUtility—a Python script that allows you to seamlessly pull live data from the new NSE website (nseindia.com). In this article, I’ll walk you through how this utility works, what data you can fetch, and how you can integrate it into your own trading or analytics workflow.
What is NseUtility?
NseUtility is a lightweight Python class that interacts with the NSE website and extracts essential market data. You can use it to:
- ✅Fetch the latest Equity and F&O instrument list
- ✅Retrieve live price details for any stock or index
- ✅Access real-time option chain data for derivatives traders
- ✅Download daily Bhav copies to analyze historical price movements
- ✅Fetch FII/DII activity, Top Gainers and Losers
- ✅Get Nse Holiday list and check for clearing and trading holidays
- ✅Get 5 levels of Market Depth for any NSE instrument
- ✅Retrieve live data on all Futures and Options Instruments
- ✅Fetch Corporate Actions like Bonus, Dividends, Splits, Buy backs
- ✅Get Corporate Announcements
- ✅Advances, Declines and Unchanged data across all indices
- ✅PE, PB Ratio and Dividend Yield of Indices
- ✅Most Active Equities and Derivative options / futures contracts
- ✅Fetch Insider / Promoter Trading data
- ✅Upcoming Financial Results Event Calendar
- ✅List of ETFs
This utility makes it effortless to automate data collection without relying on third-party APIs or paid services.
Future Enhancements
FabTrader, given the vibrant community it is, we get constant suggestions for new inclusions into this utility and I tend to each one of those!
So, this script will constantly be updated with new functionalities and bug fixes. So, its essential that you copy the latest Class file and keep your code up-to-date
How to Use NseUtility?
1️⃣ Installation & Setup
To get started, simply copy the NseUtility class (given at the bottom of this article) into Python script and save the file as NseUtility.py within your project source folder. Remember to pip install the dependency ‘Pandas’ before executing the code.
2️⃣ Creating an Instance
Once you have the class in your project, create an instance within your own program as seen below and start using the methods within the main class!
import NseUtility
nse = NseUtility.NseUtils()
import NseUtility
nse = NseUtility.NseUtils()
import NseUtility nse = NseUtility.NseUtils()
3️⃣ Fetching Live Data
Here are some quick examples of how you could use this utility and fetch live data:
# -------------------------------------------------------------------------
# FabTrader Algorithmic Trading - Tutorials
# -------------------------------------------------------------------------
# CONTACT:
# - Website: https://fabtrader.in
# - Email: fabtraderinc@gmail.com
#
# Usage: Educational Purposes & training use only. Not for commercial redistribution.
# ------------------------------------------------------------------------
import NseUtility
import pandas as pd
from pprint import pprint
# Create a NSE instance of NSEUtility
nse = NseUtility.NseUtils()
# Display settings
pd.set_option("display.max_rows", None, "display.max_columns", None)
# Input Reference
# Following Indices exist - To be used as input within NseUtility
"""
equity_market_list = ['NIFTY 50', 'NIFTY NEXT 50', 'NIFTY MIDCAP 50', 'NIFTY MIDCAP 100', 'NIFTY MIDCAP 150',
'NIFTY SMALLCAP 50',
'NIFTY SMALLCAP 100', 'NIFTY SMALLCAP 250', 'NIFTY MIDSMALLCAP 400', 'NIFTY 100', 'NIFTY 200',
'NIFTY AUTO',
'NIFTY BANK', 'NIFTY ENERGY', 'NIFTY FINANCIAL SERVICES', 'NIFTY FINANCIAL SERVICES 25/50',
'NIFTY FMCG',
'NIFTY IT', 'NIFTY MEDIA', 'NIFTY METAL', 'NIFTY PHARMA', 'NIFTY PSU BANK', 'NIFTY REALTY',
'NIFTY PRIVATE BANK', 'Securities in F&O', 'Permitted to Trade',
'NIFTY DIVIDEND OPPORTUNITIES 50',
'NIFTY50 VALUE 20', 'NIFTY100 QUALITY 30', 'NIFTY50 EQUAL WEIGHT', 'NIFTY100 EQUAL WEIGHT',
'NIFTY100 LOW VOLATILITY 30', 'NIFTY ALPHA 50', 'NIFTY200 QUALITY 30',
'NIFTY ALPHA LOW-VOLATILITY 30',
'NIFTY200 MOMENTUM 30', 'NIFTY COMMODITIES', 'NIFTY INDIA CONSUMPTION', 'NIFTY CPSE',
'NIFTY INFRASTRUCTURE',
'NIFTY MNC', 'NIFTY GROWTH SECTORS 15', 'NIFTY PSE', 'NIFTY SERVICES SECTOR',
'NIFTY100 LIQUID 15',
'NIFTY MIDCAP LIQUID 15']
pre_market_list = ['NIFTY 50', 'Nifty Bank', 'Emerge', 'Securities in F&O', 'Others', 'All']
"""
#--------------------- Full List of Equity Symbols --------------------#
# print(nse.get_equity_full_list())
# print(nse.get_equity_full_list(list_only=True))
#------------------------ Full List of FNO Symbols --------------------#
# print(nse.get_fno_full_list())
# print(nse.get_fno_full_list(list_only=True))
#--------------------------- Pre Market Data -------------------------#
# pprint(nse.pre_market_info('All'))
# pprint(nse.pre_market_info('NIFTY 50'))
# pprint(nse.pre_market_info('Nifty Bank'))
# pprint(nse.pre_market_info('Emerge'))
# pprint(nse.pre_market_info('Securities in F&O'))
# pprint(nse.pre_market_info('Others'))
#-------------------------- Index Symbol List ------------------------#
# pprint(nse.get_index_details('NIFTY 50'))
# pprint(nse.get_index_details('NIFTY 100'))
# pprint(nse.get_index_details('NIFTY 200'))
# pprint(nse.get_index_details('NIFTY MIDCAP 50'))
# pprint(nse.get_index_details('NIFTY AUTO'))
# print(nse.get_index_details('NIFTY IT', list_only=True))
# pprint(nse.get_index_details('NIFTY 50', list_only=True))
#----------------------------- NSE Holiday ---------------------------#
# print(nse.clearing_holidays())
# print(nse.clearing_holidays(list_only=True))
# print(nse.trading_holidays())
# print(nse.trading_holidays(list_only=True))
# print(nse.is_nse_clearing_holiday())
# print(nse.is_nse_clearing_holiday('19-FEB-2025'))
# print(nse.is_nse_trading_holiday())
# print(nse.is_nse_trading_holiday('26-JAN-2025'))
#----------------------------- Equity Info ---------------------------#
# pprint(nse.equity_info('INFY'))
#----------------------------- Price Info ---------------------------#
# pprint(nse.price_info('INFY'))
# pprint(nse.get_52week_high_low('INFY'))
#-------------------------- Market Depth --------------------------------#
# pprint(nse.get_market_depth("INFY"))
#---------------------- Futures Instrument /Details -------------------#
# print(nse.futures_data('INFY'))
# print(nse.futures_data('NIFTY', indices=True))
#----------------------------- Options Data ---------------------------#
# In case of indices, use the indices=True parameter
# print(nse.get_option_chain('INFY').head())
# print(nse.get_option_chain('NIFTY', indices=True).head())
# print(nse.get_option_chain('BANKNIFTY', indices=True).head())
#---------------------------- Bhav Copy Download ---------------------#
# Delivery Bhav Copy
# print(nse.bhav_copy_with_delivery('24-01-2024').head())
# Equity Bhav Copy
# print(nse.equity_bhav_copy('24-01-2024').head())
# Indices Bhav Copy
# print(nse.bhav_copy_indices('24-01-2024').head())
# FNO Bhav Copy
# print(nse.fno_bhav_copy('24-01-2024').head())
#------------------------ FII / DII Activity ---------------------------#
# print(nse.fii_dii_activity())
#----------------- Index Historic Data (Daily Data) ----------------------#
# print(nse.get_index_historic_data('NIFTY 50', '01-01-2025', '31-01-2025'))
#---------------------------- Top Gainers / Losers ----------------------#
# pprint(nse.get_gainers_losers())
#----- Corporate Actions / Bonus / Dividend / Splits / Buy Back-----------#
# print(nse.get_corporate_action().head()) # To download corp actions in the last one month
# print(nse.get_corporate_action('01-01-2025','31-01-2025').head()) # To download for specific period
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Bonus")) # To download Bonus data only
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Dividend")) # To download Dividend data only
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Split")) # To download Split data only
# print(nse.get_corporate_action('01-01-2025','31-03-2025', "Buy Back")) # To download Buy Back data only
#---------------------------- Corporate Announcements ------------------#
# print(nse.get_corporate_announcement().head()) # To download Corp announcements in the last one month
# print(nse.get_corporate_announcement('01-03-2025','31-03-2025').head()) # To download for specific period
#----------------------------- ADVANCES AND DECLINES ---------------------#
# print(nse.get_advance_decline())
#----------------------------- PE / PB / Div Yield ---------------------#
# print(nse.get_index_pe_ratio().head())
# print(nse.get_index_pb_ratio().head())
# print(nse.get_index_div_yield().head())
#----------------------- MOST ACTIVE EQUITIES -------------------#
# print(nse.most_active_equity_stocks_by_volume())
# print(nse.most_active_equity_stocks_by_value())
#----------------------- MOST ACTIVE DERIVATIVES -------------------#
# print(nse.most_active_index_calls())
# print(nse.most_active_index_puts())
# print(nse.most_active_stock_calls())
# print(nse.most_active_stock_puts())
# print(nse.most_active_contracts_by_oi())
# print(nse.most_active_contracts_by_volume())
# print(nse.most_active_futures_contracts_by_volume())
# print(nse.most_active_options_contracts_by_volume())
#----------------------- Get Insider Trading / Promoter Trading Data -------------------#
# print(nse.get_insider_trading()) # Will extract all insider trading info for last 30 days
# print(nse.get_insider_trading(from_date='24-03-2025', to_date='26-03-2025'))
#----------------------- Get Upcoming Company Results Calendar -------------------#
# print(nse.get_upcoming_results_calendar())
#---------------------------- Get List of ETFs --------------------------#
# print(nse.get_etf_list())
# -------------------------------------------------------------------------
# FabTrader Algorithmic Trading - Tutorials
# -------------------------------------------------------------------------
# CONTACT:
# - Website: https://fabtrader.in
# - Email: fabtraderinc@gmail.com
#
# Usage: Educational Purposes & training use only. Not for commercial redistribution.
# ------------------------------------------------------------------------
import NseUtility
import pandas as pd
from pprint import pprint
# Create a NSE instance of NSEUtility
nse = NseUtility.NseUtils()
# Display settings
pd.set_option("display.max_rows", None, "display.max_columns", None)
# Input Reference
# Following Indices exist - To be used as input within NseUtility
"""
equity_market_list = ['NIFTY 50', 'NIFTY NEXT 50', 'NIFTY MIDCAP 50', 'NIFTY MIDCAP 100', 'NIFTY MIDCAP 150',
'NIFTY SMALLCAP 50',
'NIFTY SMALLCAP 100', 'NIFTY SMALLCAP 250', 'NIFTY MIDSMALLCAP 400', 'NIFTY 100', 'NIFTY 200',
'NIFTY AUTO',
'NIFTY BANK', 'NIFTY ENERGY', 'NIFTY FINANCIAL SERVICES', 'NIFTY FINANCIAL SERVICES 25/50',
'NIFTY FMCG',
'NIFTY IT', 'NIFTY MEDIA', 'NIFTY METAL', 'NIFTY PHARMA', 'NIFTY PSU BANK', 'NIFTY REALTY',
'NIFTY PRIVATE BANK', 'Securities in F&O', 'Permitted to Trade',
'NIFTY DIVIDEND OPPORTUNITIES 50',
'NIFTY50 VALUE 20', 'NIFTY100 QUALITY 30', 'NIFTY50 EQUAL WEIGHT', 'NIFTY100 EQUAL WEIGHT',
'NIFTY100 LOW VOLATILITY 30', 'NIFTY ALPHA 50', 'NIFTY200 QUALITY 30',
'NIFTY ALPHA LOW-VOLATILITY 30',
'NIFTY200 MOMENTUM 30', 'NIFTY COMMODITIES', 'NIFTY INDIA CONSUMPTION', 'NIFTY CPSE',
'NIFTY INFRASTRUCTURE',
'NIFTY MNC', 'NIFTY GROWTH SECTORS 15', 'NIFTY PSE', 'NIFTY SERVICES SECTOR',
'NIFTY100 LIQUID 15',
'NIFTY MIDCAP LIQUID 15']
pre_market_list = ['NIFTY 50', 'Nifty Bank', 'Emerge', 'Securities in F&O', 'Others', 'All']
"""
#--------------------- Full List of Equity Symbols --------------------#
# print(nse.get_equity_full_list())
# print(nse.get_equity_full_list(list_only=True))
#------------------------ Full List of FNO Symbols --------------------#
# print(nse.get_fno_full_list())
# print(nse.get_fno_full_list(list_only=True))
#--------------------------- Pre Market Data -------------------------#
# pprint(nse.pre_market_info('All'))
# pprint(nse.pre_market_info('NIFTY 50'))
# pprint(nse.pre_market_info('Nifty Bank'))
# pprint(nse.pre_market_info('Emerge'))
# pprint(nse.pre_market_info('Securities in F&O'))
# pprint(nse.pre_market_info('Others'))
#-------------------------- Index Symbol List ------------------------#
# pprint(nse.get_index_details('NIFTY 50'))
# pprint(nse.get_index_details('NIFTY 100'))
# pprint(nse.get_index_details('NIFTY 200'))
# pprint(nse.get_index_details('NIFTY MIDCAP 50'))
# pprint(nse.get_index_details('NIFTY AUTO'))
# print(nse.get_index_details('NIFTY IT', list_only=True))
# pprint(nse.get_index_details('NIFTY 50', list_only=True))
#----------------------------- NSE Holiday ---------------------------#
# print(nse.clearing_holidays())
# print(nse.clearing_holidays(list_only=True))
# print(nse.trading_holidays())
# print(nse.trading_holidays(list_only=True))
# print(nse.is_nse_clearing_holiday())
# print(nse.is_nse_clearing_holiday('19-FEB-2025'))
# print(nse.is_nse_trading_holiday())
# print(nse.is_nse_trading_holiday('26-JAN-2025'))
#----------------------------- Equity Info ---------------------------#
# pprint(nse.equity_info('INFY'))
#----------------------------- Price Info ---------------------------#
# pprint(nse.price_info('INFY'))
# pprint(nse.get_52week_high_low('INFY'))
#-------------------------- Market Depth --------------------------------#
# pprint(nse.get_market_depth("INFY"))
#---------------------- Futures Instrument /Details -------------------#
# print(nse.futures_data('INFY'))
# print(nse.futures_data('NIFTY', indices=True))
#----------------------------- Options Data ---------------------------#
# In case of indices, use the indices=True parameter
# print(nse.get_option_chain('INFY').head())
# print(nse.get_option_chain('NIFTY', indices=True).head())
# print(nse.get_option_chain('BANKNIFTY', indices=True).head())
#---------------------------- Bhav Copy Download ---------------------#
# Delivery Bhav Copy
# print(nse.bhav_copy_with_delivery('24-01-2024').head())
# Equity Bhav Copy
# print(nse.equity_bhav_copy('24-01-2024').head())
# Indices Bhav Copy
# print(nse.bhav_copy_indices('24-01-2024').head())
# FNO Bhav Copy
# print(nse.fno_bhav_copy('24-01-2024').head())
#------------------------ FII / DII Activity ---------------------------#
# print(nse.fii_dii_activity())
#----------------- Index Historic Data (Daily Data) ----------------------#
# print(nse.get_index_historic_data('NIFTY 50', '01-01-2025', '31-01-2025'))
#---------------------------- Top Gainers / Losers ----------------------#
# pprint(nse.get_gainers_losers())
#----- Corporate Actions / Bonus / Dividend / Splits / Buy Back-----------#
# print(nse.get_corporate_action().head()) # To download corp actions in the last one month
# print(nse.get_corporate_action('01-01-2025','31-01-2025').head()) # To download for specific period
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Bonus")) # To download Bonus data only
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Dividend")) # To download Dividend data only
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Split")) # To download Split data only
# print(nse.get_corporate_action('01-01-2025','31-03-2025', "Buy Back")) # To download Buy Back data only
#---------------------------- Corporate Announcements ------------------#
# print(nse.get_corporate_announcement().head()) # To download Corp announcements in the last one month
# print(nse.get_corporate_announcement('01-03-2025','31-03-2025').head()) # To download for specific period
#----------------------------- ADVANCES AND DECLINES ---------------------#
# print(nse.get_advance_decline())
#----------------------------- PE / PB / Div Yield ---------------------#
# print(nse.get_index_pe_ratio().head())
# print(nse.get_index_pb_ratio().head())
# print(nse.get_index_div_yield().head())
#----------------------- MOST ACTIVE EQUITIES -------------------#
# print(nse.most_active_equity_stocks_by_volume())
# print(nse.most_active_equity_stocks_by_value())
#----------------------- MOST ACTIVE DERIVATIVES -------------------#
# print(nse.most_active_index_calls())
# print(nse.most_active_index_puts())
# print(nse.most_active_stock_calls())
# print(nse.most_active_stock_puts())
# print(nse.most_active_contracts_by_oi())
# print(nse.most_active_contracts_by_volume())
# print(nse.most_active_futures_contracts_by_volume())
# print(nse.most_active_options_contracts_by_volume())
#----------------------- Get Insider Trading / Promoter Trading Data -------------------#
# print(nse.get_insider_trading()) # Will extract all insider trading info for last 30 days
# print(nse.get_insider_trading(from_date='24-03-2025', to_date='26-03-2025'))
#----------------------- Get Upcoming Company Results Calendar -------------------#
# print(nse.get_upcoming_results_calendar())
#---------------------------- Get List of ETFs --------------------------#
# print(nse.get_etf_list())
# -------------------------------------------------------------------------
# FabTrader Algorithmic Trading - Tutorials
# -------------------------------------------------------------------------
# CONTACT:
# - Website: https://fabtrader.in
# - Email: fabtraderinc@gmail.com
#
# Usage: Educational Purposes & training use only. Not for commercial redistribution.
# ------------------------------------------------------------------------
import NseUtility
import pandas as pd
from pprint import pprint
# Create a NSE instance of NSEUtility
nse = NseUtility.NseUtils()
# Display settings
pd.set_option("display.max_rows", None, "display.max_columns", None)
# Input Reference
# Following Indices exist - To be used as input within NseUtility
"""
equity_market_list = ['NIFTY 50', 'NIFTY NEXT 50', 'NIFTY MIDCAP 50', 'NIFTY MIDCAP 100', 'NIFTY MIDCAP 150',
'NIFTY SMALLCAP 50',
'NIFTY SMALLCAP 100', 'NIFTY SMALLCAP 250', 'NIFTY MIDSMALLCAP 400', 'NIFTY 100', 'NIFTY 200',
'NIFTY AUTO',
'NIFTY BANK', 'NIFTY ENERGY', 'NIFTY FINANCIAL SERVICES', 'NIFTY FINANCIAL SERVICES 25/50',
'NIFTY FMCG',
'NIFTY IT', 'NIFTY MEDIA', 'NIFTY METAL', 'NIFTY PHARMA', 'NIFTY PSU BANK', 'NIFTY REALTY',
'NIFTY PRIVATE BANK', 'Securities in F&O', 'Permitted to Trade',
'NIFTY DIVIDEND OPPORTUNITIES 50',
'NIFTY50 VALUE 20', 'NIFTY100 QUALITY 30', 'NIFTY50 EQUAL WEIGHT', 'NIFTY100 EQUAL WEIGHT',
'NIFTY100 LOW VOLATILITY 30', 'NIFTY ALPHA 50', 'NIFTY200 QUALITY 30',
'NIFTY ALPHA LOW-VOLATILITY 30',
'NIFTY200 MOMENTUM 30', 'NIFTY COMMODITIES', 'NIFTY INDIA CONSUMPTION', 'NIFTY CPSE',
'NIFTY INFRASTRUCTURE',
'NIFTY MNC', 'NIFTY GROWTH SECTORS 15', 'NIFTY PSE', 'NIFTY SERVICES SECTOR',
'NIFTY100 LIQUID 15',
'NIFTY MIDCAP LIQUID 15']
pre_market_list = ['NIFTY 50', 'Nifty Bank', 'Emerge', 'Securities in F&O', 'Others', 'All']
"""
#--------------------- Full List of Equity Symbols --------------------#
# print(nse.get_equity_full_list())
# print(nse.get_equity_full_list(list_only=True))
#------------------------ Full List of FNO Symbols --------------------#
# print(nse.get_fno_full_list())
# print(nse.get_fno_full_list(list_only=True))
#--------------------------- Pre Market Data -------------------------#
# pprint(nse.pre_market_info('All'))
# pprint(nse.pre_market_info('NIFTY 50'))
# pprint(nse.pre_market_info('Nifty Bank'))
# pprint(nse.pre_market_info('Emerge'))
# pprint(nse.pre_market_info('Securities in F&O'))
# pprint(nse.pre_market_info('Others'))
#-------------------------- Index Symbol List ------------------------#
# pprint(nse.get_index_details('NIFTY 50'))
# pprint(nse.get_index_details('NIFTY 100'))
# pprint(nse.get_index_details('NIFTY 200'))
# pprint(nse.get_index_details('NIFTY MIDCAP 50'))
# pprint(nse.get_index_details('NIFTY AUTO'))
# print(nse.get_index_details('NIFTY IT', list_only=True))
# pprint(nse.get_index_details('NIFTY 50', list_only=True))
#----------------------------- NSE Holiday ---------------------------#
# print(nse.clearing_holidays())
# print(nse.clearing_holidays(list_only=True))
# print(nse.trading_holidays())
# print(nse.trading_holidays(list_only=True))
# print(nse.is_nse_clearing_holiday())
# print(nse.is_nse_clearing_holiday('19-FEB-2025'))
# print(nse.is_nse_trading_holiday())
# print(nse.is_nse_trading_holiday('26-JAN-2025'))
#----------------------------- Equity Info ---------------------------#
# pprint(nse.equity_info('INFY'))
#----------------------------- Price Info ---------------------------#
# pprint(nse.price_info('INFY'))
# pprint(nse.get_52week_high_low('INFY'))
#-------------------------- Market Depth --------------------------------#
# pprint(nse.get_market_depth("INFY"))
#---------------------- Futures Instrument /Details -------------------#
# print(nse.futures_data('INFY'))
# print(nse.futures_data('NIFTY', indices=True))
#----------------------------- Options Data ---------------------------#
# In case of indices, use the indices=True parameter
# print(nse.get_option_chain('INFY').head())
# print(nse.get_option_chain('NIFTY', indices=True).head())
# print(nse.get_option_chain('BANKNIFTY', indices=True).head())
#---------------------------- Bhav Copy Download ---------------------#
# Delivery Bhav Copy
# print(nse.bhav_copy_with_delivery('24-01-2024').head())
# Equity Bhav Copy
# print(nse.equity_bhav_copy('24-01-2024').head())
# Indices Bhav Copy
# print(nse.bhav_copy_indices('24-01-2024').head())
# FNO Bhav Copy
# print(nse.fno_bhav_copy('24-01-2024').head())
#------------------------ FII / DII Activity ---------------------------#
# print(nse.fii_dii_activity())
#----------------- Index Historic Data (Daily Data) ----------------------#
# print(nse.get_index_historic_data('NIFTY 50', '01-01-2025', '31-01-2025'))
#---------------------------- Top Gainers / Losers ----------------------#
# pprint(nse.get_gainers_losers())
#----- Corporate Actions / Bonus / Dividend / Splits / Buy Back-----------#
# print(nse.get_corporate_action().head()) # To download corp actions in the last one month
# print(nse.get_corporate_action('01-01-2025','31-01-2025').head()) # To download for specific period
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Bonus")) # To download Bonus data only
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Dividend")) # To download Dividend data only
# print(nse.get_corporate_action('01-01-2025','31-01-2025', "Split")) # To download Split data only
# print(nse.get_corporate_action('01-01-2025','31-03-2025', "Buy Back")) # To download Buy Back data only
#---------------------------- Corporate Announcements ------------------#
# print(nse.get_corporate_announcement().head()) # To download Corp announcements in the last one month
# print(nse.get_corporate_announcement('01-03-2025','31-03-2025').head()) # To download for specific period
#----------------------------- ADVANCES AND DECLINES ---------------------#
# print(nse.get_advance_decline())
#----------------------------- PE / PB / Div Yield ---------------------#
# print(nse.get_index_pe_ratio().head())
# print(nse.get_index_pb_ratio().head())
# print(nse.get_index_div_yield().head())
#----------------------- MOST ACTIVE EQUITIES -------------------#
# print(nse.most_active_equity_stocks_by_volume())
# print(nse.most_active_equity_stocks_by_value())
#----------------------- MOST ACTIVE DERIVATIVES -------------------#
# print(nse.most_active_index_calls())
# print(nse.most_active_index_puts())
# print(nse.most_active_stock_calls())
# print(nse.most_active_stock_puts())
# print(nse.most_active_contracts_by_oi())
# print(nse.most_active_contracts_by_volume())
# print(nse.most_active_futures_contracts_by_volume())
# print(nse.most_active_options_contracts_by_volume())
#----------------------- Get Insider Trading / Promoter Trading Data -------------------#
# print(nse.get_insider_trading()) # Will extract all insider trading info for last 30 days
# print(nse.get_insider_trading(from_date='24-03-2025', to_date='26-03-2025'))
#----------------------- Get Upcoming Company Results Calendar -------------------#
# print(nse.get_upcoming_results_calendar())
#---------------------------- Get List of ETFs --------------------------#
# print(nse.get_etf_list())Main Python Utility Code
The following is the main python utility class file. Copy this file and save this as ‘NseUtility.py‘ within the same folder as your own code. This code contains the class file that will be imported into your respective code.
Note: Ensure that the file name is spelt correctly as instructed above!
"""
* NSE UTILITY *
Description: This utility is Python Library to get publicly available data on new NSE india website
Disclaimer : This utility is meant for educational purposes only. Downloading data from NSE
website requires explicit approval from the exchange. Hence, the usage of this utility is for
limited purposes only under proper/explicit approvals.
Requirements : Following packages are to be installed (using pip) prior to using this utility
- pandas
- python 3.8 and above
"""
import requests
import pandas as pd
from datetime import datetime, timedelta
from io import StringIO, BytesIO
import zipfile
class NseUtils:
equity_market_list = ['NIFTY 50', 'NIFTY NEXT 50', 'NIFTY MIDCAP 50', 'NIFTY MIDCAP 100',
'NIFTY MIDCAP 150', 'NIFTY SMALLCAP 50', 'NIFTY SMALLCAP 100', 'NIFTY SMALLCAP 250',
'NIFTY MIDSMALLCAP 400', 'NIFTY 100', 'NIFTY 200', 'NIFTY AUTO',
'NIFTY BANK', 'NIFTY ENERGY', 'NIFTY FINANCIAL SERVICES', 'NIFTY FINANCIAL SERVICES 25/50',
'NIFTY FMCG',
'NIFTY IT', 'NIFTY MEDIA', 'NIFTY METAL', 'NIFTY PHARMA', 'NIFTY PSU BANK', 'NIFTY REALTY',
'NIFTY PRIVATE BANK', 'Securities in F&O', 'Permitted to Trade',
'NIFTY DIVIDEND OPPORTUNITIES 50',
'NIFTY50 VALUE 20', 'NIFTY100 QUALITY 30', 'NIFTY50 EQUAL WEIGHT', 'NIFTY100 EQUAL WEIGHT',
'NIFTY100 LOW VOLATILITY 30', 'NIFTY ALPHA 50', 'NIFTY200 QUALITY 30',
'NIFTY ALPHA LOW-VOLATILITY 30',
'NIFTY200 MOMENTUM 30', 'NIFTY COMMODITIES', 'NIFTY INDIA CONSUMPTION', 'NIFTY CPSE',
'NIFTY INFRASTRUCTURE',
'NIFTY MNC', 'NIFTY GROWTH SECTORS 15', 'NIFTY PSE', 'NIFTY SERVICES SECTOR',
'NIFTY100 LIQUID 15',
'NIFTY MIDCAP LIQUID 15']
pre_market_list = ['NIFTY 50', 'Nifty Bank', 'Emerge', 'Securities in F&O', 'Others', 'All']
def __init__(self):
self.headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.83 Safari/537.36',
'Upgrade-Insecure-Requests': "1",
"DNT": "1",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*,q=0.8",
'Accept-Language': 'en-US,en;q=0.9',
# 'Accept-Encoding': 'gzip, deflate, br, zstd',
'Accept-Encoding': 'gzip, deflate',
'Connection': 'keep-alive'
}
self.session = requests.Session()
self.session.get("http://nseindia.com", headers=self.headers)
self.cookies = self.session.cookies.get_dict()
def pre_market_info(self, category='All'):
pre_market_xref = {"NIFTY 50": "NIFTY", "Nifty Bank": "BANKNIFTY", "Emerge": "SME", "Securities in F&O": "FO",
"Others": "OTHERS", "All": "ALL"}
ref_url = 'https://www.nseindia.com/market-data/pre-open-market-cm-and-emerge-market'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/market-data-pre-open?key={pre_market_xref[category]}"
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
processed_data = []
data = response.json()['data']
for i in data:
processed_data.append(i["metadata"])
df = pd.DataFrame(processed_data)
df = df.set_index("symbol", drop=True)
return df
def get_index_details(self, category, list_only=False):
category = category.upper().replace('&', '%26').replace(' ', '%20')
ref_url = "https://www.nseindia.com/market-data/live-equity-market?symbol={category}"
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/equity-stockIndices?index={category}"
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
df = pd.DataFrame(data['data'])
df = df.drop(["meta"], axis=1)
df = df.set_index("symbol", drop=True)
if list_only:
symbol_list = sorted(df.index[1:].tolist())
return symbol_list
else:
return df
def clearing_holidays(self, list_only=False):
"""
Returns the list of NSE clearing holidays
:param list_only: If case of list_only, the holidays are returned as a simple list. Else
full details are provided in a dataframe
:return:
"""
data = self.session.get(f'https://www.nseindia.com/api/holiday-master?type={'Clearing'.lower()}',
headers=self.headers).json()
df = pd.DataFrame(list(data.values())[0])
if list_only:
holiday_list = df['tradingDate'].tolist()
return holiday_list
return df
def trading_holidays(self, list_only=False):
"""
Returns the list of NSE trading holidays
:param list_only: If case of list_only, the holidays are returned as a simple list. Else
full details are provided in a dataframe
:return:
"""
data = self.session.get(f'https://www.nseindia.com/api/holiday-master?type={'Trading'.lower()}',
headers=self.headers).json()
df = pd.DataFrame(list(data.values())[0])
if list_only:
holiday_list = df['tradingDate'].tolist()
return holiday_list
else:
return df
def is_nse_trading_holiday(self, date_str=None):
"""
Return True if the date supplied in a NSE trading holiday, else False
:param date_str: Optional. If no date provided, current data will be assumed
:return:
"""
holidays = self.trading_holidays(list_only=True) # Fetch Trading holiday list from NSE
date_format = "%d-%b-%Y" # Define the expected date format
# If date_str is provided, validate and parse it
if date_str:
try:
date_obj = datetime.strptime(date_str, date_format) # Convert string to datetime
except ValueError:
print("Error: Invalid date format! Please use 'DD-MMM-YYYY' (e.g., '19-Feb-2025').")
return None
else:
date_obj = datetime.today() # Use today's date if no input is given
formatted_date = date_obj.strftime(date_format) # Convert date back to the required format
return formatted_date in holidays # Check if it’s a holiday
def is_nse_clearing_holiday(self, date_str=None):
"""
Return True if the date supplied in a NSE clearing holiday, else False
:param date_str: Optional. If no date provided, current data will be assumed
:return:
"""
holidays = self.clearing_holidays(list_only=True) # Fetch Trading holiday list from NSE
date_format = "%d-%b-%Y" # Define the expected date format
# If date_str is provided, validate and parse it
if date_str:
try:
date_obj = datetime.strptime(date_str, date_format) # Convert string to datetime
except ValueError:
print("Error: Invalid date format! Please use 'DD-MMM-YYYY' (e.g., '19-Feb-2025').")
return None
else:
date_obj = datetime.today() # Use today's date if no input is given
formatted_date = date_obj.strftime(date_format) # Convert date back to the required format
return formatted_date in holidays # Check if it’s a holiday
def equity_info(self, symbol):
"""
Extracts the full details of a symbol as see on NSE website
:param symbol:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
# Fetch primary details
ref_url = 'https://www.nseindia.com/get-quotes/equity?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# Fetch Trade Data for symbol ('Trade Information' tab on NSE website)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol + "§ion=trade_info"
trade_data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# Merge Meta data with Trade Information into final dataset
data['tradeData'] = trade_data
return data
def price_info(self, symbol):
"""
Gets all key price related information for a given stock
:param symbol:
:param trade_info:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/get-quotes/equity?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
if not data:
return None
if 'error' in data:
return None
return {
"Symbol": symbol,
"LastTradedPrice": data['priceInfo']['lastPrice'],
"PreviousClose": data['priceInfo']['previousClose'],
"Change": data['priceInfo']['change'],
"PercentChange": data['priceInfo']['pChange'],
"Open": data['priceInfo']['open'],
"Close": data['priceInfo']['close'],
"High": data['priceInfo']['intraDayHighLow']['max'],
"Low": data['priceInfo']['intraDayHighLow']['min'],
"VWAP": data['priceInfo']['vwap'],
"UpperCircuit": data['priceInfo']['upperCP'],
"LowerCircuit": data['priceInfo']['lowerCP'],
}
def futures_data(self, symbol, indices=False):
"""
Returns the list of futures instruments for a given stock and its details
:param symbol:
:param indices: This is to set to true if symbol is an index
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-derivative?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
lst = []
for i in data["stocks"]:
if i["metadata"]["instrumentType"] == ("Index Futures" if indices else "Stock Futures"):
lst.append(i["metadata"])
df = pd.DataFrame(lst)
df = df.set_index("identifier", drop=True)
return df
def get_option_chain(self, symbol, indices=False):
"""
Returns the full option chain table as seen on NSE website for the given stock/index
:param symbol:
:param indices:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
if not indices:
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-equities?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()["records"]
else:
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-indices?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()["records"]
my_df = []
for i in data["data"]:
for k, v in i.items():
if k == "CE" or k == "PE":
info = v
info["instrumentType"] = k
info["timestamp"] = data["timestamp"]
my_df.append(info)
df = pd.DataFrame(my_df)
df = df.set_index("identifier", drop=True)
return df
def get_52week_high_low(self, stock=None):
"""
Get 52 Week High and Low data. If stock is provided, the High/Low data for that
particular stock is returned. If not, the full list is returned
:param stock: Optional
:return:
"""
url = 'https://nsearchives.nseindia.com/content/CM_52_wk_High_low_25012024.csv'
response = requests.get(url, headers=self.headers)
data = StringIO(response.text.replace(
'"Disclaimer - The Data provided in the adjusted 52 week high and adjusted 52 week low columns are adjusted for corporate actions (bonus, splits & rights).For actual (unadjusted) 52 week high & low prices, kindly refer bhavcopy."\n"Effective for 25-Jan-2024"\n',
''))
df = pd.read_csv(data)
if stock is not None:
# Return the full 52 Week High/Low list of stock input
row = df[df['SYMBOL'] == stock]
if row.empty:
return None
return {
"Symbol": stock,
"52 Week High": row["Adjusted 52_Week_High"].values[0],
"52 Week High Date": row["52_Week_High_Date"].values[0],
"52 Week Low": row["Adjusted 52_Week_Low"].values[0],
"52 Week Low Date": row["52_Week_Low_DT"].values[0]
}
# Return the full 52 Week High/Low list of all stocks
return df
def fno_bhav_copy(self, trade_date: str = ""):
"""
Get the NSE FNO bhav copy data as per the traded date
:param trade_date: eg:'20-06-2023'
:return: pandas data frame
"""
bhav_df = pd.DataFrame()
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
url = 'https://nsearchives.nseindia.com/content/fo/BhavCopy_NSE_FO_0_0_0_'
payload = f"{str(trade_date.strftime('%Y%m%d'))}_F_0000.csv.zip"
request_bhav = requests.get(url + payload, headers=self.headers)
bhav_df = pd.DataFrame()
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
url2 = "https://www.nseindia.com/api/reports?archives=" \
"%5B%7B%22name%22%3A%22F%26O%20-%20Bhavcopy(csv)%22%2C%22type%22%3A%22archives%22%2C%22category%22" \
f"%3A%22derivatives%22%2C%22section%22%3A%22equity%22%7D%5D&date={str(trade_date.strftime('%d-%b-%Y'))}" \
f"&type=equity&mode=single"
request_bhav = requests.get(url2 + payload, headers=self.headers)
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
raise FileNotFoundError(f' Data not found, change the date...')
return bhav_df
def bhav_copy_with_delivery(self, trade_date: str):
"""
Get the NSE bhav copy with delivery data as per the traded date
:param trade_date: eg:'20-06-2023'
:return: pandas data frame
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
use_date = trade_date.strftime("%d%m%Y")
url = f'https://nsearchives.nseindia.com/products/content/sec_bhavdata_full_{use_date}.csv'
request_bhav = requests.get(url, headers=self.headers)
if request_bhav.status_code == 200:
bhav_df = pd.read_csv(BytesIO(request_bhav.content))
else:
raise FileNotFoundError(f' Data not found, change the trade_date...')
bhav_df.columns = [name.replace(' ', '') for name in bhav_df.columns]
bhav_df['SERIES'] = bhav_df['SERIES'].str.replace(' ', '')
bhav_df['DATE1'] = bhav_df['DATE1'].str.replace(' ', '')
return bhav_df
def equity_bhav_copy(self, trade_date: str):
"""
Extract Equity Bhav Copy per the traded date provided
:param trade_date:
:return: pandas dataframe
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
# trade_date = datetime.strptime(trade_date, dd_mm_yyyy)
url = 'https://nsearchives.nseindia.com/content/cm/BhavCopy_NSE_CM_0_0_0_'
payload = f"{str(trade_date.strftime('%Y%m%d'))}_F_0000.csv.zip"
request_bhav = requests.get(url + payload, headers=self.headers)
bhav_df = pd.DataFrame()
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
raise FileNotFoundError(f' Data not found, change the trade_date...')
return bhav_df
def bhav_copy_indices(self, trade_date: str):
"""
Get nse bhav copy as per the traded date provided
:param trade_date: eg:'20-06-2023'
:return: pandas dataframe
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
url = f"https://nsearchives.nseindia.com/content/indices/ind_close_all_{str(trade_date.strftime('%d%m%Y').upper())}.csv"
# nse_resp = nse_urlfetch(url)
nse_resp = requests.get(url, headers=self.headers)
if nse_resp.status_code != 200:
raise FileNotFoundError(f" No data available for : {trade_date}")
try:
bhav_df = pd.read_csv(BytesIO(nse_resp.content))
except Exception as e:
raise FileNotFoundError(f' Bhav copy indices not found for : {trade_date} :: NSE error : {e}')
return bhav_df
def fii_dii_activity(self):
"""
FII and DII trading activity of the day in data frame
:return: pd.DataFrame
"""
url = "https://www.nseindia.com/api/fiidiiTradeReact"
# data_json = nse_urlfetch(url).json()
data_json = requests.get(url, headers=self.headers)
data_df = pd.DataFrame(data_json.json())
return data_df
def get_live_option_chain(self, symbol: str, expiry_date: str = None, oi_mode: str = "full", indices=False):
"""
get live nse option chain.
:param symbol: eg:SBIN/BANKNIFTY
:param expiry_date: '20-06-2023'
:param oi_mode: eg: full/compact
:return: pands dataframe
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/option-chain'
ref = requests.get(ref_url, headers=self.headers)
if not indices:
url = 'https://www.nseindia.com/api/option-chain-equities?symbol=' + symbol
else:
# ref_url = 'https://www.nseindia.com/option-chain'
# ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-indices?symbol=' + symbol
payload = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# payload = get_nse_option_chain(symbol).json()
if expiry_date:
exp_date = pd.to_datetime(expiry_date, format='%d-%m-%Y')
expiry_date = exp_date.strftime('%d-%b-%Y')
if oi_mode == 'compact':
col_names = ['Fetch_Time', 'Symbol', 'Expiry_Date', 'CALLS_OI', 'CALLS_Chng_in_OI', 'CALLS_Volume',
'CALLS_IV',
'CALLS_LTP', 'CALLS_Net_Chng', 'Strike_Price', 'PUTS_OI', 'PUTS_Chng_in_OI', 'PUTS_Volume',
'PUTS_IV', 'PUTS_LTP', 'PUTS_Net_Chng']
else:
col_names = ['Fetch_Time', 'Symbol', 'Expiry_Date', 'CALLS_OI', 'CALLS_Chng_in_OI', 'CALLS_Volume',
'CALLS_IV',
'CALLS_LTP', 'CALLS_Net_Chng', 'CALLS_Bid_Qty', 'CALLS_Bid_Price', 'CALLS_Ask_Price',
'CALLS_Ask_Qty', 'Strike_Price', 'PUTS_Bid_Qty', 'PUTS_Bid_Price', 'PUTS_Ask_Price',
'PUTS_Ask_Qty',
'PUTS_Net_Chng', 'PUTS_LTP', 'PUTS_IV', 'PUTS_Volume', 'PUTS_Chng_in_OI', 'PUTS_OI']
oi_data = pd.DataFrame(columns=col_names)
oi_row = {'Fetch_Time': None, 'Symbol': None, 'Expiry_Date': None, 'CALLS_OI': 0, 'CALLS_Chng_in_OI': 0,
'CALLS_Volume': 0,
'CALLS_IV': 0, 'CALLS_LTP': 0, 'CALLS_Net_Chng': 0, 'CALLS_Bid_Qty': 0, 'CALLS_Bid_Price': 0,
'CALLS_Ask_Price': 0, 'CALLS_Ask_Qty': 0, 'Strike_Price': 0, 'PUTS_OI': 0, 'PUTS_Chng_in_OI': 0,
'PUTS_Volume': 0, 'PUTS_IV': 0, 'PUTS_LTP': 0, 'PUTS_Net_Chng': 0, 'PUTS_Bid_Qty': 0,
'PUTS_Bid_Price': 0, 'PUTS_Ask_Price': 0, 'PUTS_Ask_Qty': 0}
for m in range(len(payload['records']['data'])):
if not expiry_date or (payload['records']['data'][m]['expiryDate'] == expiry_date):
try:
oi_row['Expiry_Date'] = payload['records']['data'][m]['expiryDate']
oi_row['CALLS_OI'] = payload['records']['data'][m]['CE']['openInterest']
oi_row['CALLS_Chng_in_OI'] = payload['records']['data'][m]['CE']['changeinOpenInterest']
oi_row['CALLS_Volume'] = payload['records']['data'][m]['CE']['totalTradedVolume']
oi_row['CALLS_IV'] = payload['records']['data'][m]['CE']['impliedVolatility']
oi_row['CALLS_LTP'] = payload['records']['data'][m]['CE']['lastPrice']
oi_row['CALLS_Net_Chng'] = payload['records']['data'][m]['CE']['change']
if oi_mode == 'full':
oi_row['CALLS_Bid_Qty'] = payload['records']['data'][m]['CE']['bidQty']
oi_row['CALLS_Bid_Price'] = payload['records']['data'][m]['CE']['bidprice']
oi_row['CALLS_Ask_Price'] = payload['records']['data'][m]['CE']['askPrice']
oi_row['CALLS_Ask_Qty'] = payload['records']['data'][m]['CE']['askQty']
except KeyError:
oi_row['CALLS_OI'], oi_row['CALLS_Chng_in_OI'], oi_row['CALLS_Volume'], oi_row['CALLS_IV'], oi_row[
'CALLS_LTP'], oi_row['CALLS_Net_Chng'] = 0, 0, 0, 0, 0, 0
if oi_mode == 'full':
oi_row['CALLS_Bid_Qty'], oi_row['CALLS_Bid_Price'], oi_row['CALLS_Ask_Price'], oi_row[
'CALLS_Ask_Qty'] = 0, 0, 0, 0
pass
oi_row['Strike_Price'] = payload['records']['data'][m]['strikePrice']
try:
oi_row['PUTS_OI'] = payload['records']['data'][m]['PE']['openInterest']
oi_row['PUTS_Chng_in_OI'] = payload['records']['data'][m]['PE']['changeinOpenInterest']
oi_row['PUTS_Volume'] = payload['records']['data'][m]['PE']['totalTradedVolume']
oi_row['PUTS_IV'] = payload['records']['data'][m]['PE']['impliedVolatility']
oi_row['PUTS_LTP'] = payload['records']['data'][m]['PE']['lastPrice']
oi_row['PUTS_Net_Chng'] = payload['records']['data'][m]['PE']['change']
if oi_mode == 'full':
oi_row['PUTS_Bid_Qty'] = payload['records']['data'][m]['PE']['bidQty']
oi_row['PUTS_Bid_Price'] = payload['records']['data'][m]['PE']['bidprice']
oi_row['PUTS_Ask_Price'] = payload['records']['data'][m]['PE']['askPrice']
oi_row['PUTS_Ask_Qty'] = payload['records']['data'][m]['PE']['askQty']
except KeyError:
oi_row['PUTS_OI'], oi_row['PUTS_Chng_in_OI'], oi_row['PUTS_Volume'], oi_row['PUTS_IV'], oi_row[
'PUTS_LTP'], oi_row['PUTS_Net_Chng'] = 0, 0, 0, 0, 0, 0
if oi_mode == 'full':
oi_row['PUTS_Bid_Qty'], oi_row['PUTS_Bid_Price'], oi_row['PUTS_Ask_Price'], oi_row[
'PUTS_Ask_Qty'] = 0, 0, 0, 0
if oi_data.empty:
oi_data = pd.DataFrame([oi_row]).copy()
else:
oi_data = pd.concat([oi_data, pd.DataFrame([oi_row])], ignore_index=True)
oi_data['Symbol'] = symbol
oi_data['Fetch_Time'] = payload['records']['timestamp']
return oi_data
def get_market_depth(self, symbol):
"""
Function to retrieve market depth for a given symbol
:param symbol:
:return: Market Dept as Dict
"""
data = self.equity_info(symbol)
merged_dict = {
'ask': data['tradeData']['marketDeptOrderBook']['ask'],
'bid': data['tradeData']['marketDeptOrderBook']['bid']
}
return merged_dict
def get_index_historic_data(self, index: str, from_date: str = None, to_date: str = None):
"""
get historical index data set for the specific time period.
apply the index name as per the nse india site
:param index: 'NIFTY 50'/'NIFTY BANK'
:param from_date: '17-03-2022' ('dd-mm-YYYY')
:param to_date: '17-06-2023' ('dd-mm-YYYY')
:return: pandas.DataFrame
:raise ValueError if the parameter input is not proper
"""
index_data_columns = ['TIMESTAMP', 'INDEX_NAME', 'OPEN_INDEX_VAL', 'HIGH_INDEX_VAL', 'CLOSE_INDEX_VAL',
'LOW_INDEX_VAL', 'TRADED_QTY', 'TURN_OVER']
# Check for valid dates and period inputs
if not from_date or not to_date:
raise ValueError(' Please provide the valid parameters')
try:
from_dt = datetime.strptime(from_date, "%d-%m-%Y")
to_dt = datetime.strptime(to_date, "%d-%m-%Y")
time_delta = (to_dt - from_dt).days
if time_delta < 1:
raise ValueError(f'to_date should greater than from_date ')
except Exception as e:
print(e)
raise ValueError(f'either or both from_date = {from_date} || to_date = {to_date} are not valid value')
nse_df = pd.DataFrame(columns=index_data_columns)
from_date = datetime.strptime(from_date, "%d-%m-%Y")
to_date = datetime.strptime(to_date, "%d-%m-%Y")
load_days = (to_date - from_date).days
while load_days > 0:
if load_days > 365:
end_date = (from_date + timedelta(364)).strftime("%d-%m-%Y")
start_date = from_date.strftime("%d-%m-%Y")
else:
end_date = to_date.strftime("%d-%m-%Y")
start_date = from_date.strftime("%d-%m-%Y")
data_df = self.get_index_data(index=index, from_date=start_date, to_date=end_date)
from_date = from_date + timedelta(365)
load_days = (to_date - from_date).days
if nse_df.empty:
nse_df = data_df
else:
nse_df = pd.concat([nse_df, data_df], ignore_index=True)
return nse_df
def get_index_data(self, index: str, from_date: str, to_date: str):
index_data_columns = ['TIMESTAMP', 'INDEX_NAME', 'OPEN_INDEX_VAL', 'HIGH_INDEX_VAL', 'CLOSE_INDEX_VAL',
'LOW_INDEX_VAL', 'TRADED_QTY', 'TURN_OVER']
index = index.replace(' ', '%20').upper()
ref_url = 'https://www.nseindia.com/reports-indices-historical-index-data'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/historical/indicesHistory?indexType={index}&from={from_date}&to={to_date}"
try:
data_json = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
data_close_df = pd.DataFrame(data_json['data']['indexCloseOnlineRecords']).drop(
columns=['_id', "EOD_TIMESTAMP"])
data_turnover_df = pd.DataFrame(data_json['data']['indexTurnoverRecords']).drop(columns=['_id',
'HIT_INDEX_NAME_UPPER'])
data_df = pd.merge(data_close_df, data_turnover_df, on='TIMESTAMP', how='inner')
except Exception as e:
raise " Resource not available"
data_df.drop(columns='TIMESTAMP', inplace=True)
unwanted_str_list = ['FH_', 'EOD_', 'HIT_']
new_col = data_df.columns
for unwanted in unwanted_str_list:
new_col = [name.replace(f'{unwanted}', '') for name in new_col]
data_df.columns = new_col
return data_df[index_data_columns]
def get_equity_full_list(self, list_only=False):
"""
get list of all equity available to trade in NSE
:param list_only: Optional. If you only need the symbols in a list, set this to true.
Otherwise, the full table is downloaded as a dataframe
:return: pandas data frame
"""
url = "https://archives.nseindia.com/content/equities/EQUITY_L.csv"
nse_resp = self.session.get(url, headers=self.headers, cookies=self.cookies)
if nse_resp.status_code != 200:
raise FileNotFoundError(f" No data equity list available")
try:
data_df = pd.read_csv(BytesIO(nse_resp.content))
except Exception as e:
raise FileNotFoundError(f' Equity List not found :: NSE error : {e}')
data_df = data_df[['SYMBOL', 'NAME OF COMPANY', ' SERIES', ' DATE OF LISTING', ' FACE VALUE']]
if list_only:
symbol_list = data_df['SYMBOL'].tolist()
return symbol_list
return data_df
def get_fno_full_list(self, list_only=False):
"""
get a dataframe of all listed derivative list with the recent lot size to trade
:param list_only: Optional. If you only need the symbols in a list, set this to true.
Otherwise, the full table is downloaded as a dataframe
:return: pandas data frame
"""
ref_url = 'https://www.nseindia.com/products-services/equity-derivatives-list-underlyings-information'
ref = requests.get(ref_url, headers=self.headers)
url = "https://www.nseindia.com/api/underlying-information"
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if response.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = response.json()
data_df = pd.DataFrame(data_dict['data']['UnderlyingList'])
if list_only:
symbol_list = data_df['symbol'].tolist()
return symbol_list
return data_df
def get_gainers_losers(self):
gain_loss_dict = {}
ref_url = 'https://www.nseindia.com/market-data/top-gainers-losers'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-variations?index=gainers'
data_obj = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if data_obj.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = data_obj.json()
# Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTY']['data'])
nifty_gainer = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['BANKNIFTY']['data'])
banknifty_gainer = data_df['symbol'].to_list()
# Nifty Next 50 Gainer
data_df = pd.DataFrame(data_dict['NIFTYNEXT50']['data'])
next50_gainer = data_df['symbol'].to_list()
# All Securities Gainer
data_df = pd.DataFrame(data_dict['allSec']['data'])
allsec_gainer = data_df['symbol'].to_list()
# FNO Securities Gainer
data_df = pd.DataFrame(data_dict['FOSec']['data'])
fno_gainer = data_df['symbol'].to_list()
url = 'https://www.nseindia.com/api/live-analysis-variations?index=loosers'
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if data_obj.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = data_obj.json()
# Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTY']['data'])
nifty_loser = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['BANKNIFTY']['data'])
banknifty_loser = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTYNEXT50']['data'])
next50_loser = data_df['symbol'].to_list()
# All Securities Gainer
data_df = pd.DataFrame(data_dict['allSec']['data'])
allsec_loser = data_df['symbol'].to_list()
# FNO Securities Gainer
data_df = pd.DataFrame(data_dict['FOSec']['data'])
fno_loser = data_df['symbol'].to_list()
gain_dict = {
'Nifty Gainer': nifty_gainer,
'Bank Nifty Gainer': banknifty_gainer,
'Nifty Next 50 Gainer': next50_gainer,
'All Securities Gainer': allsec_gainer,
'FNO Gainer': fno_gainer
}
loss_dict = {
'Nifty Loser': nifty_loser,
'Bank Nifty Loser': banknifty_loser,
'Nifty Next 50 Loser': next50_loser,
'All Securities Loser': allsec_loser,
'FNO Loser': fno_loser
}
return gain_dict, loss_dict
def get_corporate_action(self, from_date_str: str = None, to_date_str: str = None, filter: str = None):
# Fetch Corporate Action data from NSE
if from_date_str is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
if to_date_str is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
try:
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-actions'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/corporates-corporateActions?index=equities&from_date={from_date_str}&to_date={to_date_str}"
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
corp_action = pd.DataFrame(data_obj.json())
if filter is not None:
corp_action = corp_action[corp_action['subject'].str.contains(filter, case=False, na=False)]
return corp_action
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_corporate_announcement(self, from_date_str: str = None, to_date_str: str = None):
# Fetch Corporate Announcements data from NSE
if from_date_str is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
if to_date_str is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
try:
ref_url = ('https://www.nseindia.com/companies-listing/corporate-filings-announcements')
ref = requests.get(ref_url, headers=self.headers)
url = f'https://www.nseindia.com/api/corporate-announcements?index=equities&from_date={from_date_str}&to_date={to_date_str}'
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
corp_announcement = pd.DataFrame(data_obj.json())
return corp_announcement
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_index_pe_ratio(self):
# try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'pe']]
df = df[df['pe'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['pe'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Profit Earning Ratio']
return df
else:
return None
# except:
# print("Error fetching Corporate Action Data. Check your input")
# return None
def get_index_pb_ratio(self):
try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'pb']]
df = df[df['pb'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['pb'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Price Book Ratio']
return df
else:
return None
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_index_div_yield(self):
try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'dy']]
df = df[df['dy'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['dy'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Div Yield']
return df
else:
return None
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_advance_decline(self):
try:
ref_url = 'https://www.nseindia.com/market-data/live-market-indices'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'advances', 'declines', 'unchanged']]
df.dropna(inplace=True)
df.columns = ['Index', 'Advances', 'Declines', 'Unchanged']
return df
else:
return None
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_equity_stocks_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-equities'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-most-active-securities?index=volume'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_equity_stocks_by_value(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-equities'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-most-active-securities?index=value'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_index_calls(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=calls-index-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTIDX']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_index_puts(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=puts-index-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTIDX']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_stock_calls(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=calls-stocks-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTSTK']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_stock_puts(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=puts-stocks-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTSTK']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_contracts_by_oi(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=oi'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=contracts'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_futures_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=futures'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_options_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=options&limit=20'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_insider_trading(self, from_date: str = None, to_date: str = None):
try:
if from_date is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
else:
from_date_str = from_date
if to_date is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
else:
to_date_str = to_date
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-insider-trading'
ref = requests.get(ref_url, headers=self.headers)
url= f'https://www.nseindia.com/api/corporates-pit?index=equities&from_date={from_date_str}&to_date={to_date_str}'
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json()
df = pd.DataFrame(data['data'])
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_upcoming_results_calendar(self):
# Extracts the events calendar from NSE - Filters only the upcoming Financial results related events
try:
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-event-calendar'
ref = requests.get(ref_url, headers=self.headers)
url= f'https://www.nseindia.com/api/event-calendar?'
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json()
df = pd.DataFrame(data)
events = df[df['purpose'].str.contains('Results', case=False, na=False)]
return events
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_etf_list(self):
try:
ref_url = 'https://www.nseindia.com/market-data/exchange-traded-funds-etf'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/etf'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching ETF list. Check your input")
return None
"""
* NSE UTILITY *
Description: This utility is Python Library to get publicly available data on new NSE india website
Disclaimer : This utility is meant for educational purposes only. Downloading data from NSE
website requires explicit approval from the exchange. Hence, the usage of this utility is for
limited purposes only under proper/explicit approvals.
Requirements : Following packages are to be installed (using pip) prior to using this utility
- pandas
- python 3.8 and above
"""
import requests
import pandas as pd
from datetime import datetime, timedelta
from io import StringIO, BytesIO
import zipfile
class NseUtils:
equity_market_list = ['NIFTY 50', 'NIFTY NEXT 50', 'NIFTY MIDCAP 50', 'NIFTY MIDCAP 100',
'NIFTY MIDCAP 150', 'NIFTY SMALLCAP 50', 'NIFTY SMALLCAP 100', 'NIFTY SMALLCAP 250',
'NIFTY MIDSMALLCAP 400', 'NIFTY 100', 'NIFTY 200', 'NIFTY AUTO',
'NIFTY BANK', 'NIFTY ENERGY', 'NIFTY FINANCIAL SERVICES', 'NIFTY FINANCIAL SERVICES 25/50',
'NIFTY FMCG',
'NIFTY IT', 'NIFTY MEDIA', 'NIFTY METAL', 'NIFTY PHARMA', 'NIFTY PSU BANK', 'NIFTY REALTY',
'NIFTY PRIVATE BANK', 'Securities in F&O', 'Permitted to Trade',
'NIFTY DIVIDEND OPPORTUNITIES 50',
'NIFTY50 VALUE 20', 'NIFTY100 QUALITY 30', 'NIFTY50 EQUAL WEIGHT', 'NIFTY100 EQUAL WEIGHT',
'NIFTY100 LOW VOLATILITY 30', 'NIFTY ALPHA 50', 'NIFTY200 QUALITY 30',
'NIFTY ALPHA LOW-VOLATILITY 30',
'NIFTY200 MOMENTUM 30', 'NIFTY COMMODITIES', 'NIFTY INDIA CONSUMPTION', 'NIFTY CPSE',
'NIFTY INFRASTRUCTURE',
'NIFTY MNC', 'NIFTY GROWTH SECTORS 15', 'NIFTY PSE', 'NIFTY SERVICES SECTOR',
'NIFTY100 LIQUID 15',
'NIFTY MIDCAP LIQUID 15']
pre_market_list = ['NIFTY 50', 'Nifty Bank', 'Emerge', 'Securities in F&O', 'Others', 'All']
def __init__(self):
self.headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.83 Safari/537.36',
'Upgrade-Insecure-Requests': "1",
"DNT": "1",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*,q=0.8",
'Accept-Language': 'en-US,en;q=0.9',
# 'Accept-Encoding': 'gzip, deflate, br, zstd',
'Accept-Encoding': 'gzip, deflate',
'Connection': 'keep-alive'
}
self.session = requests.Session()
self.session.get("http://nseindia.com", headers=self.headers)
self.cookies = self.session.cookies.get_dict()
def pre_market_info(self, category='All'):
pre_market_xref = {"NIFTY 50": "NIFTY", "Nifty Bank": "BANKNIFTY", "Emerge": "SME", "Securities in F&O": "FO",
"Others": "OTHERS", "All": "ALL"}
ref_url = 'https://www.nseindia.com/market-data/pre-open-market-cm-and-emerge-market'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/market-data-pre-open?key={pre_market_xref[category]}"
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
processed_data = []
data = response.json()['data']
for i in data:
processed_data.append(i["metadata"])
df = pd.DataFrame(processed_data)
df = df.set_index("symbol", drop=True)
return df
def get_index_details(self, category, list_only=False):
category = category.upper().replace('&', '%26').replace(' ', '%20')
ref_url = "https://www.nseindia.com/market-data/live-equity-market?symbol={category}"
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/equity-stockIndices?index={category}"
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
df = pd.DataFrame(data['data'])
df = df.drop(["meta"], axis=1)
df = df.set_index("symbol", drop=True)
if list_only:
symbol_list = sorted(df.index[1:].tolist())
return symbol_list
else:
return df
def clearing_holidays(self, list_only=False):
"""
Returns the list of NSE clearing holidays
:param list_only: If case of list_only, the holidays are returned as a simple list. Else
full details are provided in a dataframe
:return:
"""
data = self.session.get(f'https://www.nseindia.com/api/holiday-master?type={'Clearing'.lower()}',
headers=self.headers).json()
df = pd.DataFrame(list(data.values())[0])
if list_only:
holiday_list = df['tradingDate'].tolist()
return holiday_list
return df
def trading_holidays(self, list_only=False):
"""
Returns the list of NSE trading holidays
:param list_only: If case of list_only, the holidays are returned as a simple list. Else
full details are provided in a dataframe
:return:
"""
data = self.session.get(f'https://www.nseindia.com/api/holiday-master?type={'Trading'.lower()}',
headers=self.headers).json()
df = pd.DataFrame(list(data.values())[0])
if list_only:
holiday_list = df['tradingDate'].tolist()
return holiday_list
else:
return df
def is_nse_trading_holiday(self, date_str=None):
"""
Return True if the date supplied in a NSE trading holiday, else False
:param date_str: Optional. If no date provided, current data will be assumed
:return:
"""
holidays = self.trading_holidays(list_only=True) # Fetch Trading holiday list from NSE
date_format = "%d-%b-%Y" # Define the expected date format
# If date_str is provided, validate and parse it
if date_str:
try:
date_obj = datetime.strptime(date_str, date_format) # Convert string to datetime
except ValueError:
print("Error: Invalid date format! Please use 'DD-MMM-YYYY' (e.g., '19-Feb-2025').")
return None
else:
date_obj = datetime.today() # Use today's date if no input is given
formatted_date = date_obj.strftime(date_format) # Convert date back to the required format
return formatted_date in holidays # Check if it’s a holiday
def is_nse_clearing_holiday(self, date_str=None):
"""
Return True if the date supplied in a NSE clearing holiday, else False
:param date_str: Optional. If no date provided, current data will be assumed
:return:
"""
holidays = self.clearing_holidays(list_only=True) # Fetch Trading holiday list from NSE
date_format = "%d-%b-%Y" # Define the expected date format
# If date_str is provided, validate and parse it
if date_str:
try:
date_obj = datetime.strptime(date_str, date_format) # Convert string to datetime
except ValueError:
print("Error: Invalid date format! Please use 'DD-MMM-YYYY' (e.g., '19-Feb-2025').")
return None
else:
date_obj = datetime.today() # Use today's date if no input is given
formatted_date = date_obj.strftime(date_format) # Convert date back to the required format
return formatted_date in holidays # Check if it’s a holiday
def equity_info(self, symbol):
"""
Extracts the full details of a symbol as see on NSE website
:param symbol:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
# Fetch primary details
ref_url = 'https://www.nseindia.com/get-quotes/equity?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# Fetch Trade Data for symbol ('Trade Information' tab on NSE website)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol + "§ion=trade_info"
trade_data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# Merge Meta data with Trade Information into final dataset
data['tradeData'] = trade_data
return data
def price_info(self, symbol):
"""
Gets all key price related information for a given stock
:param symbol:
:param trade_info:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/get-quotes/equity?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
if not data:
return None
if 'error' in data:
return None
return {
"Symbol": symbol,
"LastTradedPrice": data['priceInfo']['lastPrice'],
"PreviousClose": data['priceInfo']['previousClose'],
"Change": data['priceInfo']['change'],
"PercentChange": data['priceInfo']['pChange'],
"Open": data['priceInfo']['open'],
"Close": data['priceInfo']['close'],
"High": data['priceInfo']['intraDayHighLow']['max'],
"Low": data['priceInfo']['intraDayHighLow']['min'],
"VWAP": data['priceInfo']['vwap'],
"UpperCircuit": data['priceInfo']['upperCP'],
"LowerCircuit": data['priceInfo']['lowerCP'],
}
def futures_data(self, symbol, indices=False):
"""
Returns the list of futures instruments for a given stock and its details
:param symbol:
:param indices: This is to set to true if symbol is an index
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-derivative?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
lst = []
for i in data["stocks"]:
if i["metadata"]["instrumentType"] == ("Index Futures" if indices else "Stock Futures"):
lst.append(i["metadata"])
df = pd.DataFrame(lst)
df = df.set_index("identifier", drop=True)
return df
def get_option_chain(self, symbol, indices=False):
"""
Returns the full option chain table as seen on NSE website for the given stock/index
:param symbol:
:param indices:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
if not indices:
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-equities?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()["records"]
else:
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-indices?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()["records"]
my_df = []
for i in data["data"]:
for k, v in i.items():
if k == "CE" or k == "PE":
info = v
info["instrumentType"] = k
info["timestamp"] = data["timestamp"]
my_df.append(info)
df = pd.DataFrame(my_df)
df = df.set_index("identifier", drop=True)
return df
def get_52week_high_low(self, stock=None):
"""
Get 52 Week High and Low data. If stock is provided, the High/Low data for that
particular stock is returned. If not, the full list is returned
:param stock: Optional
:return:
"""
url = 'https://nsearchives.nseindia.com/content/CM_52_wk_High_low_25012024.csv'
response = requests.get(url, headers=self.headers)
data = StringIO(response.text.replace(
'"Disclaimer - The Data provided in the adjusted 52 week high and adjusted 52 week low columns are adjusted for corporate actions (bonus, splits & rights).For actual (unadjusted) 52 week high & low prices, kindly refer bhavcopy."\n"Effective for 25-Jan-2024"\n',
''))
df = pd.read_csv(data)
if stock is not None:
# Return the full 52 Week High/Low list of stock input
row = df[df['SYMBOL'] == stock]
if row.empty:
return None
return {
"Symbol": stock,
"52 Week High": row["Adjusted 52_Week_High"].values[0],
"52 Week High Date": row["52_Week_High_Date"].values[0],
"52 Week Low": row["Adjusted 52_Week_Low"].values[0],
"52 Week Low Date": row["52_Week_Low_DT"].values[0]
}
# Return the full 52 Week High/Low list of all stocks
return df
def fno_bhav_copy(self, trade_date: str = ""):
"""
Get the NSE FNO bhav copy data as per the traded date
:param trade_date: eg:'20-06-2023'
:return: pandas data frame
"""
bhav_df = pd.DataFrame()
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
url = 'https://nsearchives.nseindia.com/content/fo/BhavCopy_NSE_FO_0_0_0_'
payload = f"{str(trade_date.strftime('%Y%m%d'))}_F_0000.csv.zip"
request_bhav = requests.get(url + payload, headers=self.headers)
bhav_df = pd.DataFrame()
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
url2 = "https://www.nseindia.com/api/reports?archives=" \
"%5B%7B%22name%22%3A%22F%26O%20-%20Bhavcopy(csv)%22%2C%22type%22%3A%22archives%22%2C%22category%22" \
f"%3A%22derivatives%22%2C%22section%22%3A%22equity%22%7D%5D&date={str(trade_date.strftime('%d-%b-%Y'))}" \
f"&type=equity&mode=single"
request_bhav = requests.get(url2 + payload, headers=self.headers)
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
raise FileNotFoundError(f' Data not found, change the date...')
return bhav_df
def bhav_copy_with_delivery(self, trade_date: str):
"""
Get the NSE bhav copy with delivery data as per the traded date
:param trade_date: eg:'20-06-2023'
:return: pandas data frame
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
use_date = trade_date.strftime("%d%m%Y")
url = f'https://nsearchives.nseindia.com/products/content/sec_bhavdata_full_{use_date}.csv'
request_bhav = requests.get(url, headers=self.headers)
if request_bhav.status_code == 200:
bhav_df = pd.read_csv(BytesIO(request_bhav.content))
else:
raise FileNotFoundError(f' Data not found, change the trade_date...')
bhav_df.columns = [name.replace(' ', '') for name in bhav_df.columns]
bhav_df['SERIES'] = bhav_df['SERIES'].str.replace(' ', '')
bhav_df['DATE1'] = bhav_df['DATE1'].str.replace(' ', '')
return bhav_df
def equity_bhav_copy(self, trade_date: str):
"""
Extract Equity Bhav Copy per the traded date provided
:param trade_date:
:return: pandas dataframe
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
# trade_date = datetime.strptime(trade_date, dd_mm_yyyy)
url = 'https://nsearchives.nseindia.com/content/cm/BhavCopy_NSE_CM_0_0_0_'
payload = f"{str(trade_date.strftime('%Y%m%d'))}_F_0000.csv.zip"
request_bhav = requests.get(url + payload, headers=self.headers)
bhav_df = pd.DataFrame()
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
raise FileNotFoundError(f' Data not found, change the trade_date...')
return bhav_df
def bhav_copy_indices(self, trade_date: str):
"""
Get nse bhav copy as per the traded date provided
:param trade_date: eg:'20-06-2023'
:return: pandas dataframe
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
url = f"https://nsearchives.nseindia.com/content/indices/ind_close_all_{str(trade_date.strftime('%d%m%Y').upper())}.csv"
# nse_resp = nse_urlfetch(url)
nse_resp = requests.get(url, headers=self.headers)
if nse_resp.status_code != 200:
raise FileNotFoundError(f" No data available for : {trade_date}")
try:
bhav_df = pd.read_csv(BytesIO(nse_resp.content))
except Exception as e:
raise FileNotFoundError(f' Bhav copy indices not found for : {trade_date} :: NSE error : {e}')
return bhav_df
def fii_dii_activity(self):
"""
FII and DII trading activity of the day in data frame
:return: pd.DataFrame
"""
url = "https://www.nseindia.com/api/fiidiiTradeReact"
# data_json = nse_urlfetch(url).json()
data_json = requests.get(url, headers=self.headers)
data_df = pd.DataFrame(data_json.json())
return data_df
def get_live_option_chain(self, symbol: str, expiry_date: str = None, oi_mode: str = "full", indices=False):
"""
get live nse option chain.
:param symbol: eg:SBIN/BANKNIFTY
:param expiry_date: '20-06-2023'
:param oi_mode: eg: full/compact
:return: pands dataframe
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/option-chain'
ref = requests.get(ref_url, headers=self.headers)
if not indices:
url = 'https://www.nseindia.com/api/option-chain-equities?symbol=' + symbol
else:
# ref_url = 'https://www.nseindia.com/option-chain'
# ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-indices?symbol=' + symbol
payload = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# payload = get_nse_option_chain(symbol).json()
if expiry_date:
exp_date = pd.to_datetime(expiry_date, format='%d-%m-%Y')
expiry_date = exp_date.strftime('%d-%b-%Y')
if oi_mode == 'compact':
col_names = ['Fetch_Time', 'Symbol', 'Expiry_Date', 'CALLS_OI', 'CALLS_Chng_in_OI', 'CALLS_Volume',
'CALLS_IV',
'CALLS_LTP', 'CALLS_Net_Chng', 'Strike_Price', 'PUTS_OI', 'PUTS_Chng_in_OI', 'PUTS_Volume',
'PUTS_IV', 'PUTS_LTP', 'PUTS_Net_Chng']
else:
col_names = ['Fetch_Time', 'Symbol', 'Expiry_Date', 'CALLS_OI', 'CALLS_Chng_in_OI', 'CALLS_Volume',
'CALLS_IV',
'CALLS_LTP', 'CALLS_Net_Chng', 'CALLS_Bid_Qty', 'CALLS_Bid_Price', 'CALLS_Ask_Price',
'CALLS_Ask_Qty', 'Strike_Price', 'PUTS_Bid_Qty', 'PUTS_Bid_Price', 'PUTS_Ask_Price',
'PUTS_Ask_Qty',
'PUTS_Net_Chng', 'PUTS_LTP', 'PUTS_IV', 'PUTS_Volume', 'PUTS_Chng_in_OI', 'PUTS_OI']
oi_data = pd.DataFrame(columns=col_names)
oi_row = {'Fetch_Time': None, 'Symbol': None, 'Expiry_Date': None, 'CALLS_OI': 0, 'CALLS_Chng_in_OI': 0,
'CALLS_Volume': 0,
'CALLS_IV': 0, 'CALLS_LTP': 0, 'CALLS_Net_Chng': 0, 'CALLS_Bid_Qty': 0, 'CALLS_Bid_Price': 0,
'CALLS_Ask_Price': 0, 'CALLS_Ask_Qty': 0, 'Strike_Price': 0, 'PUTS_OI': 0, 'PUTS_Chng_in_OI': 0,
'PUTS_Volume': 0, 'PUTS_IV': 0, 'PUTS_LTP': 0, 'PUTS_Net_Chng': 0, 'PUTS_Bid_Qty': 0,
'PUTS_Bid_Price': 0, 'PUTS_Ask_Price': 0, 'PUTS_Ask_Qty': 0}
for m in range(len(payload['records']['data'])):
if not expiry_date or (payload['records']['data'][m]['expiryDate'] == expiry_date):
try:
oi_row['Expiry_Date'] = payload['records']['data'][m]['expiryDate']
oi_row['CALLS_OI'] = payload['records']['data'][m]['CE']['openInterest']
oi_row['CALLS_Chng_in_OI'] = payload['records']['data'][m]['CE']['changeinOpenInterest']
oi_row['CALLS_Volume'] = payload['records']['data'][m]['CE']['totalTradedVolume']
oi_row['CALLS_IV'] = payload['records']['data'][m]['CE']['impliedVolatility']
oi_row['CALLS_LTP'] = payload['records']['data'][m]['CE']['lastPrice']
oi_row['CALLS_Net_Chng'] = payload['records']['data'][m]['CE']['change']
if oi_mode == 'full':
oi_row['CALLS_Bid_Qty'] = payload['records']['data'][m]['CE']['bidQty']
oi_row['CALLS_Bid_Price'] = payload['records']['data'][m]['CE']['bidprice']
oi_row['CALLS_Ask_Price'] = payload['records']['data'][m]['CE']['askPrice']
oi_row['CALLS_Ask_Qty'] = payload['records']['data'][m]['CE']['askQty']
except KeyError:
oi_row['CALLS_OI'], oi_row['CALLS_Chng_in_OI'], oi_row['CALLS_Volume'], oi_row['CALLS_IV'], oi_row[
'CALLS_LTP'], oi_row['CALLS_Net_Chng'] = 0, 0, 0, 0, 0, 0
if oi_mode == 'full':
oi_row['CALLS_Bid_Qty'], oi_row['CALLS_Bid_Price'], oi_row['CALLS_Ask_Price'], oi_row[
'CALLS_Ask_Qty'] = 0, 0, 0, 0
pass
oi_row['Strike_Price'] = payload['records']['data'][m]['strikePrice']
try:
oi_row['PUTS_OI'] = payload['records']['data'][m]['PE']['openInterest']
oi_row['PUTS_Chng_in_OI'] = payload['records']['data'][m]['PE']['changeinOpenInterest']
oi_row['PUTS_Volume'] = payload['records']['data'][m]['PE']['totalTradedVolume']
oi_row['PUTS_IV'] = payload['records']['data'][m]['PE']['impliedVolatility']
oi_row['PUTS_LTP'] = payload['records']['data'][m]['PE']['lastPrice']
oi_row['PUTS_Net_Chng'] = payload['records']['data'][m]['PE']['change']
if oi_mode == 'full':
oi_row['PUTS_Bid_Qty'] = payload['records']['data'][m]['PE']['bidQty']
oi_row['PUTS_Bid_Price'] = payload['records']['data'][m]['PE']['bidprice']
oi_row['PUTS_Ask_Price'] = payload['records']['data'][m]['PE']['askPrice']
oi_row['PUTS_Ask_Qty'] = payload['records']['data'][m]['PE']['askQty']
except KeyError:
oi_row['PUTS_OI'], oi_row['PUTS_Chng_in_OI'], oi_row['PUTS_Volume'], oi_row['PUTS_IV'], oi_row[
'PUTS_LTP'], oi_row['PUTS_Net_Chng'] = 0, 0, 0, 0, 0, 0
if oi_mode == 'full':
oi_row['PUTS_Bid_Qty'], oi_row['PUTS_Bid_Price'], oi_row['PUTS_Ask_Price'], oi_row[
'PUTS_Ask_Qty'] = 0, 0, 0, 0
if oi_data.empty:
oi_data = pd.DataFrame([oi_row]).copy()
else:
oi_data = pd.concat([oi_data, pd.DataFrame([oi_row])], ignore_index=True)
oi_data['Symbol'] = symbol
oi_data['Fetch_Time'] = payload['records']['timestamp']
return oi_data
def get_market_depth(self, symbol):
"""
Function to retrieve market depth for a given symbol
:param symbol:
:return: Market Dept as Dict
"""
data = self.equity_info(symbol)
merged_dict = {
'ask': data['tradeData']['marketDeptOrderBook']['ask'],
'bid': data['tradeData']['marketDeptOrderBook']['bid']
}
return merged_dict
def get_index_historic_data(self, index: str, from_date: str = None, to_date: str = None):
"""
get historical index data set for the specific time period.
apply the index name as per the nse india site
:param index: 'NIFTY 50'/'NIFTY BANK'
:param from_date: '17-03-2022' ('dd-mm-YYYY')
:param to_date: '17-06-2023' ('dd-mm-YYYY')
:return: pandas.DataFrame
:raise ValueError if the parameter input is not proper
"""
index_data_columns = ['TIMESTAMP', 'INDEX_NAME', 'OPEN_INDEX_VAL', 'HIGH_INDEX_VAL', 'CLOSE_INDEX_VAL',
'LOW_INDEX_VAL', 'TRADED_QTY', 'TURN_OVER']
# Check for valid dates and period inputs
if not from_date or not to_date:
raise ValueError(' Please provide the valid parameters')
try:
from_dt = datetime.strptime(from_date, "%d-%m-%Y")
to_dt = datetime.strptime(to_date, "%d-%m-%Y")
time_delta = (to_dt - from_dt).days
if time_delta < 1:
raise ValueError(f'to_date should greater than from_date ')
except Exception as e:
print(e)
raise ValueError(f'either or both from_date = {from_date} || to_date = {to_date} are not valid value')
nse_df = pd.DataFrame(columns=index_data_columns)
from_date = datetime.strptime(from_date, "%d-%m-%Y")
to_date = datetime.strptime(to_date, "%d-%m-%Y")
load_days = (to_date - from_date).days
while load_days > 0:
if load_days > 365:
end_date = (from_date + timedelta(364)).strftime("%d-%m-%Y")
start_date = from_date.strftime("%d-%m-%Y")
else:
end_date = to_date.strftime("%d-%m-%Y")
start_date = from_date.strftime("%d-%m-%Y")
data_df = self.get_index_data(index=index, from_date=start_date, to_date=end_date)
from_date = from_date + timedelta(365)
load_days = (to_date - from_date).days
if nse_df.empty:
nse_df = data_df
else:
nse_df = pd.concat([nse_df, data_df], ignore_index=True)
return nse_df
def get_index_data(self, index: str, from_date: str, to_date: str):
index_data_columns = ['TIMESTAMP', 'INDEX_NAME', 'OPEN_INDEX_VAL', 'HIGH_INDEX_VAL', 'CLOSE_INDEX_VAL',
'LOW_INDEX_VAL', 'TRADED_QTY', 'TURN_OVER']
index = index.replace(' ', '%20').upper()
ref_url = 'https://www.nseindia.com/reports-indices-historical-index-data'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/historical/indicesHistory?indexType={index}&from={from_date}&to={to_date}"
try:
data_json = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
data_close_df = pd.DataFrame(data_json['data']['indexCloseOnlineRecords']).drop(
columns=['_id', "EOD_TIMESTAMP"])
data_turnover_df = pd.DataFrame(data_json['data']['indexTurnoverRecords']).drop(columns=['_id',
'HIT_INDEX_NAME_UPPER'])
data_df = pd.merge(data_close_df, data_turnover_df, on='TIMESTAMP', how='inner')
except Exception as e:
raise " Resource not available"
data_df.drop(columns='TIMESTAMP', inplace=True)
unwanted_str_list = ['FH_', 'EOD_', 'HIT_']
new_col = data_df.columns
for unwanted in unwanted_str_list:
new_col = [name.replace(f'{unwanted}', '') for name in new_col]
data_df.columns = new_col
return data_df[index_data_columns]
def get_equity_full_list(self, list_only=False):
"""
get list of all equity available to trade in NSE
:param list_only: Optional. If you only need the symbols in a list, set this to true.
Otherwise, the full table is downloaded as a dataframe
:return: pandas data frame
"""
url = "https://archives.nseindia.com/content/equities/EQUITY_L.csv"
nse_resp = self.session.get(url, headers=self.headers, cookies=self.cookies)
if nse_resp.status_code != 200:
raise FileNotFoundError(f" No data equity list available")
try:
data_df = pd.read_csv(BytesIO(nse_resp.content))
except Exception as e:
raise FileNotFoundError(f' Equity List not found :: NSE error : {e}')
data_df = data_df[['SYMBOL', 'NAME OF COMPANY', ' SERIES', ' DATE OF LISTING', ' FACE VALUE']]
if list_only:
symbol_list = data_df['SYMBOL'].tolist()
return symbol_list
return data_df
def get_fno_full_list(self, list_only=False):
"""
get a dataframe of all listed derivative list with the recent lot size to trade
:param list_only: Optional. If you only need the symbols in a list, set this to true.
Otherwise, the full table is downloaded as a dataframe
:return: pandas data frame
"""
ref_url = 'https://www.nseindia.com/products-services/equity-derivatives-list-underlyings-information'
ref = requests.get(ref_url, headers=self.headers)
url = "https://www.nseindia.com/api/underlying-information"
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if response.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = response.json()
data_df = pd.DataFrame(data_dict['data']['UnderlyingList'])
if list_only:
symbol_list = data_df['symbol'].tolist()
return symbol_list
return data_df
def get_gainers_losers(self):
gain_loss_dict = {}
ref_url = 'https://www.nseindia.com/market-data/top-gainers-losers'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-variations?index=gainers'
data_obj = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if data_obj.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = data_obj.json()
# Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTY']['data'])
nifty_gainer = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['BANKNIFTY']['data'])
banknifty_gainer = data_df['symbol'].to_list()
# Nifty Next 50 Gainer
data_df = pd.DataFrame(data_dict['NIFTYNEXT50']['data'])
next50_gainer = data_df['symbol'].to_list()
# All Securities Gainer
data_df = pd.DataFrame(data_dict['allSec']['data'])
allsec_gainer = data_df['symbol'].to_list()
# FNO Securities Gainer
data_df = pd.DataFrame(data_dict['FOSec']['data'])
fno_gainer = data_df['symbol'].to_list()
url = 'https://www.nseindia.com/api/live-analysis-variations?index=loosers'
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if data_obj.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = data_obj.json()
# Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTY']['data'])
nifty_loser = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['BANKNIFTY']['data'])
banknifty_loser = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTYNEXT50']['data'])
next50_loser = data_df['symbol'].to_list()
# All Securities Gainer
data_df = pd.DataFrame(data_dict['allSec']['data'])
allsec_loser = data_df['symbol'].to_list()
# FNO Securities Gainer
data_df = pd.DataFrame(data_dict['FOSec']['data'])
fno_loser = data_df['symbol'].to_list()
gain_dict = {
'Nifty Gainer': nifty_gainer,
'Bank Nifty Gainer': banknifty_gainer,
'Nifty Next 50 Gainer': next50_gainer,
'All Securities Gainer': allsec_gainer,
'FNO Gainer': fno_gainer
}
loss_dict = {
'Nifty Loser': nifty_loser,
'Bank Nifty Loser': banknifty_loser,
'Nifty Next 50 Loser': next50_loser,
'All Securities Loser': allsec_loser,
'FNO Loser': fno_loser
}
return gain_dict, loss_dict
def get_corporate_action(self, from_date_str: str = None, to_date_str: str = None, filter: str = None):
# Fetch Corporate Action data from NSE
if from_date_str is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
if to_date_str is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
try:
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-actions'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/corporates-corporateActions?index=equities&from_date={from_date_str}&to_date={to_date_str}"
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
corp_action = pd.DataFrame(data_obj.json())
if filter is not None:
corp_action = corp_action[corp_action['subject'].str.contains(filter, case=False, na=False)]
return corp_action
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_corporate_announcement(self, from_date_str: str = None, to_date_str: str = None):
# Fetch Corporate Announcements data from NSE
if from_date_str is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
if to_date_str is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
try:
ref_url = ('https://www.nseindia.com/companies-listing/corporate-filings-announcements')
ref = requests.get(ref_url, headers=self.headers)
url = f'https://www.nseindia.com/api/corporate-announcements?index=equities&from_date={from_date_str}&to_date={to_date_str}'
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
corp_announcement = pd.DataFrame(data_obj.json())
return corp_announcement
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_index_pe_ratio(self):
# try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'pe']]
df = df[df['pe'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['pe'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Profit Earning Ratio']
return df
else:
return None
# except:
# print("Error fetching Corporate Action Data. Check your input")
# return None
def get_index_pb_ratio(self):
try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'pb']]
df = df[df['pb'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['pb'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Price Book Ratio']
return df
else:
return None
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_index_div_yield(self):
try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'dy']]
df = df[df['dy'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['dy'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Div Yield']
return df
else:
return None
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_advance_decline(self):
try:
ref_url = 'https://www.nseindia.com/market-data/live-market-indices'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'advances', 'declines', 'unchanged']]
df.dropna(inplace=True)
df.columns = ['Index', 'Advances', 'Declines', 'Unchanged']
return df
else:
return None
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_equity_stocks_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-equities'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-most-active-securities?index=volume'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_equity_stocks_by_value(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-equities'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-most-active-securities?index=value'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_index_calls(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=calls-index-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTIDX']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_index_puts(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=puts-index-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTIDX']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_stock_calls(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=calls-stocks-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTSTK']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_stock_puts(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=puts-stocks-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTSTK']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_contracts_by_oi(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=oi'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=contracts'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_futures_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=futures'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_options_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=options&limit=20'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_insider_trading(self, from_date: str = None, to_date: str = None):
try:
if from_date is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
else:
from_date_str = from_date
if to_date is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
else:
to_date_str = to_date
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-insider-trading'
ref = requests.get(ref_url, headers=self.headers)
url= f'https://www.nseindia.com/api/corporates-pit?index=equities&from_date={from_date_str}&to_date={to_date_str}'
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json()
df = pd.DataFrame(data['data'])
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_upcoming_results_calendar(self):
# Extracts the events calendar from NSE - Filters only the upcoming Financial results related events
try:
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-event-calendar'
ref = requests.get(ref_url, headers=self.headers)
url= f'https://www.nseindia.com/api/event-calendar?'
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json()
df = pd.DataFrame(data)
events = df[df['purpose'].str.contains('Results', case=False, na=False)]
return events
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_etf_list(self):
try:
ref_url = 'https://www.nseindia.com/market-data/exchange-traded-funds-etf'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/etf'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching ETF list. Check your input")
return None
"""
* NSE UTILITY *
Description: This utility is Python Library to get publicly available data on new NSE india website
Disclaimer : This utility is meant for educational purposes only. Downloading data from NSE
website requires explicit approval from the exchange. Hence, the usage of this utility is for
limited purposes only under proper/explicit approvals.
Requirements : Following packages are to be installed (using pip) prior to using this utility
- pandas
- python 3.8 and above
"""
import requests
import pandas as pd
from datetime import datetime, timedelta
from io import StringIO, BytesIO
import zipfile
class NseUtils:
equity_market_list = ['NIFTY 50', 'NIFTY NEXT 50', 'NIFTY MIDCAP 50', 'NIFTY MIDCAP 100',
'NIFTY MIDCAP 150', 'NIFTY SMALLCAP 50', 'NIFTY SMALLCAP 100', 'NIFTY SMALLCAP 250',
'NIFTY MIDSMALLCAP 400', 'NIFTY 100', 'NIFTY 200', 'NIFTY AUTO',
'NIFTY BANK', 'NIFTY ENERGY', 'NIFTY FINANCIAL SERVICES', 'NIFTY FINANCIAL SERVICES 25/50',
'NIFTY FMCG',
'NIFTY IT', 'NIFTY MEDIA', 'NIFTY METAL', 'NIFTY PHARMA', 'NIFTY PSU BANK', 'NIFTY REALTY',
'NIFTY PRIVATE BANK', 'Securities in F&O', 'Permitted to Trade',
'NIFTY DIVIDEND OPPORTUNITIES 50',
'NIFTY50 VALUE 20', 'NIFTY100 QUALITY 30', 'NIFTY50 EQUAL WEIGHT', 'NIFTY100 EQUAL WEIGHT',
'NIFTY100 LOW VOLATILITY 30', 'NIFTY ALPHA 50', 'NIFTY200 QUALITY 30',
'NIFTY ALPHA LOW-VOLATILITY 30',
'NIFTY200 MOMENTUM 30', 'NIFTY COMMODITIES', 'NIFTY INDIA CONSUMPTION', 'NIFTY CPSE',
'NIFTY INFRASTRUCTURE',
'NIFTY MNC', 'NIFTY GROWTH SECTORS 15', 'NIFTY PSE', 'NIFTY SERVICES SECTOR',
'NIFTY100 LIQUID 15',
'NIFTY MIDCAP LIQUID 15']
pre_market_list = ['NIFTY 50', 'Nifty Bank', 'Emerge', 'Securities in F&O', 'Others', 'All']
def __init__(self):
self.headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.83 Safari/537.36',
'Upgrade-Insecure-Requests': "1",
"DNT": "1",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*,q=0.8",
'Accept-Language': 'en-US,en;q=0.9',
# 'Accept-Encoding': 'gzip, deflate, br, zstd',
'Accept-Encoding': 'gzip, deflate',
'Connection': 'keep-alive'
}
self.session = requests.Session()
self.session.get("http://nseindia.com", headers=self.headers)
self.cookies = self.session.cookies.get_dict()
def pre_market_info(self, category='All'):
pre_market_xref = {"NIFTY 50": "NIFTY", "Nifty Bank": "BANKNIFTY", "Emerge": "SME", "Securities in F&O": "FO",
"Others": "OTHERS", "All": "ALL"}
ref_url = 'https://www.nseindia.com/market-data/pre-open-market-cm-and-emerge-market'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/market-data-pre-open?key={pre_market_xref[category]}"
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
processed_data = []
data = response.json()['data']
for i in data:
processed_data.append(i["metadata"])
df = pd.DataFrame(processed_data)
df = df.set_index("symbol", drop=True)
return df
def get_index_details(self, category, list_only=False):
category = category.upper().replace('&', '%26').replace(' ', '%20')
ref_url = "https://www.nseindia.com/market-data/live-equity-market?symbol={category}"
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/equity-stockIndices?index={category}"
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
df = pd.DataFrame(data['data'])
df = df.drop(["meta"], axis=1)
df = df.set_index("symbol", drop=True)
if list_only:
symbol_list = sorted(df.index[1:].tolist())
return symbol_list
else:
return df
def clearing_holidays(self, list_only=False):
"""
Returns the list of NSE clearing holidays
:param list_only: If case of list_only, the holidays are returned as a simple list. Else
full details are provided in a dataframe
:return:
"""
data = self.session.get(f'https://www.nseindia.com/api/holiday-master?type={'Clearing'.lower()}',
headers=self.headers).json()
df = pd.DataFrame(list(data.values())[0])
if list_only:
holiday_list = df['tradingDate'].tolist()
return holiday_list
return df
def trading_holidays(self, list_only=False):
"""
Returns the list of NSE trading holidays
:param list_only: If case of list_only, the holidays are returned as a simple list. Else
full details are provided in a dataframe
:return:
"""
data = self.session.get(f'https://www.nseindia.com/api/holiday-master?type={'Trading'.lower()}',
headers=self.headers).json()
df = pd.DataFrame(list(data.values())[0])
if list_only:
holiday_list = df['tradingDate'].tolist()
return holiday_list
else:
return df
def is_nse_trading_holiday(self, date_str=None):
"""
Return True if the date supplied in a NSE trading holiday, else False
:param date_str: Optional. If no date provided, current data will be assumed
:return:
"""
holidays = self.trading_holidays(list_only=True) # Fetch Trading holiday list from NSE
date_format = "%d-%b-%Y" # Define the expected date format
# If date_str is provided, validate and parse it
if date_str:
try:
date_obj = datetime.strptime(date_str, date_format) # Convert string to datetime
except ValueError:
print("Error: Invalid date format! Please use 'DD-MMM-YYYY' (e.g., '19-Feb-2025').")
return None
else:
date_obj = datetime.today() # Use today's date if no input is given
formatted_date = date_obj.strftime(date_format) # Convert date back to the required format
return formatted_date in holidays # Check if it’s a holiday
def is_nse_clearing_holiday(self, date_str=None):
"""
Return True if the date supplied in a NSE clearing holiday, else False
:param date_str: Optional. If no date provided, current data will be assumed
:return:
"""
holidays = self.clearing_holidays(list_only=True) # Fetch Trading holiday list from NSE
date_format = "%d-%b-%Y" # Define the expected date format
# If date_str is provided, validate and parse it
if date_str:
try:
date_obj = datetime.strptime(date_str, date_format) # Convert string to datetime
except ValueError:
print("Error: Invalid date format! Please use 'DD-MMM-YYYY' (e.g., '19-Feb-2025').")
return None
else:
date_obj = datetime.today() # Use today's date if no input is given
formatted_date = date_obj.strftime(date_format) # Convert date back to the required format
return formatted_date in holidays # Check if it’s a holiday
def equity_info(self, symbol):
"""
Extracts the full details of a symbol as see on NSE website
:param symbol:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
# Fetch primary details
ref_url = 'https://www.nseindia.com/get-quotes/equity?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# Fetch Trade Data for symbol ('Trade Information' tab on NSE website)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol + "§ion=trade_info"
trade_data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# Merge Meta data with Trade Information into final dataset
data['tradeData'] = trade_data
return data
def price_info(self, symbol):
"""
Gets all key price related information for a given stock
:param symbol:
:param trade_info:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/get-quotes/equity?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-equity?symbol=' + symbol
data = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
if not data:
return None
if 'error' in data:
return None
return {
"Symbol": symbol,
"LastTradedPrice": data['priceInfo']['lastPrice'],
"PreviousClose": data['priceInfo']['previousClose'],
"Change": data['priceInfo']['change'],
"PercentChange": data['priceInfo']['pChange'],
"Open": data['priceInfo']['open'],
"Close": data['priceInfo']['close'],
"High": data['priceInfo']['intraDayHighLow']['max'],
"Low": data['priceInfo']['intraDayHighLow']['min'],
"VWAP": data['priceInfo']['vwap'],
"UpperCircuit": data['priceInfo']['upperCP'],
"LowerCircuit": data['priceInfo']['lowerCP'],
}
def futures_data(self, symbol, indices=False):
"""
Returns the list of futures instruments for a given stock and its details
:param symbol:
:param indices: This is to set to true if symbol is an index
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/quote-derivative?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
lst = []
for i in data["stocks"]:
if i["metadata"]["instrumentType"] == ("Index Futures" if indices else "Stock Futures"):
lst.append(i["metadata"])
df = pd.DataFrame(lst)
df = df.set_index("identifier", drop=True)
return df
def get_option_chain(self, symbol, indices=False):
"""
Returns the full option chain table as seen on NSE website for the given stock/index
:param symbol:
:param indices:
:return:
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
if not indices:
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-equities?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()["records"]
else:
ref_url = 'https://www.nseindia.com/get-quotes/derivatives?symbol=' + symbol
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-indices?symbol=' + symbol
data = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()["records"]
my_df = []
for i in data["data"]:
for k, v in i.items():
if k == "CE" or k == "PE":
info = v
info["instrumentType"] = k
info["timestamp"] = data["timestamp"]
my_df.append(info)
df = pd.DataFrame(my_df)
df = df.set_index("identifier", drop=True)
return df
def get_52week_high_low(self, stock=None):
"""
Get 52 Week High and Low data. If stock is provided, the High/Low data for that
particular stock is returned. If not, the full list is returned
:param stock: Optional
:return:
"""
url = 'https://nsearchives.nseindia.com/content/CM_52_wk_High_low_25012024.csv'
response = requests.get(url, headers=self.headers)
data = StringIO(response.text.replace(
'"Disclaimer - The Data provided in the adjusted 52 week high and adjusted 52 week low columns are adjusted for corporate actions (bonus, splits & rights).For actual (unadjusted) 52 week high & low prices, kindly refer bhavcopy."\n"Effective for 25-Jan-2024"\n',
''))
df = pd.read_csv(data)
if stock is not None:
# Return the full 52 Week High/Low list of stock input
row = df[df['SYMBOL'] == stock]
if row.empty:
return None
return {
"Symbol": stock,
"52 Week High": row["Adjusted 52_Week_High"].values[0],
"52 Week High Date": row["52_Week_High_Date"].values[0],
"52 Week Low": row["Adjusted 52_Week_Low"].values[0],
"52 Week Low Date": row["52_Week_Low_DT"].values[0]
}
# Return the full 52 Week High/Low list of all stocks
return df
def fno_bhav_copy(self, trade_date: str = ""):
"""
Get the NSE FNO bhav copy data as per the traded date
:param trade_date: eg:'20-06-2023'
:return: pandas data frame
"""
bhav_df = pd.DataFrame()
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
url = 'https://nsearchives.nseindia.com/content/fo/BhavCopy_NSE_FO_0_0_0_'
payload = f"{str(trade_date.strftime('%Y%m%d'))}_F_0000.csv.zip"
request_bhav = requests.get(url + payload, headers=self.headers)
bhav_df = pd.DataFrame()
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
url2 = "https://www.nseindia.com/api/reports?archives=" \
"%5B%7B%22name%22%3A%22F%26O%20-%20Bhavcopy(csv)%22%2C%22type%22%3A%22archives%22%2C%22category%22" \
f"%3A%22derivatives%22%2C%22section%22%3A%22equity%22%7D%5D&date={str(trade_date.strftime('%d-%b-%Y'))}" \
f"&type=equity&mode=single"
request_bhav = requests.get(url2 + payload, headers=self.headers)
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
raise FileNotFoundError(f' Data not found, change the date...')
return bhav_df
def bhav_copy_with_delivery(self, trade_date: str):
"""
Get the NSE bhav copy with delivery data as per the traded date
:param trade_date: eg:'20-06-2023'
:return: pandas data frame
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
use_date = trade_date.strftime("%d%m%Y")
url = f'https://nsearchives.nseindia.com/products/content/sec_bhavdata_full_{use_date}.csv'
request_bhav = requests.get(url, headers=self.headers)
if request_bhav.status_code == 200:
bhav_df = pd.read_csv(BytesIO(request_bhav.content))
else:
raise FileNotFoundError(f' Data not found, change the trade_date...')
bhav_df.columns = [name.replace(' ', '') for name in bhav_df.columns]
bhav_df['SERIES'] = bhav_df['SERIES'].str.replace(' ', '')
bhav_df['DATE1'] = bhav_df['DATE1'].str.replace(' ', '')
return bhav_df
def equity_bhav_copy(self, trade_date: str):
"""
Extract Equity Bhav Copy per the traded date provided
:param trade_date:
:return: pandas dataframe
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
# trade_date = datetime.strptime(trade_date, dd_mm_yyyy)
url = 'https://nsearchives.nseindia.com/content/cm/BhavCopy_NSE_CM_0_0_0_'
payload = f"{str(trade_date.strftime('%Y%m%d'))}_F_0000.csv.zip"
request_bhav = requests.get(url + payload, headers=self.headers)
bhav_df = pd.DataFrame()
if request_bhav.status_code == 200:
zip_bhav = zipfile.ZipFile(BytesIO(request_bhav.content), 'r')
for file_name in zip_bhav.filelist:
if file_name:
bhav_df = pd.read_csv(zip_bhav.open(file_name))
elif request_bhav.status_code == 403:
raise FileNotFoundError(f' Data not found, change the trade_date...')
return bhav_df
def bhav_copy_indices(self, trade_date: str):
"""
Get nse bhav copy as per the traded date provided
:param trade_date: eg:'20-06-2023'
:return: pandas dataframe
"""
trade_date = datetime.strptime(trade_date, "%d-%m-%Y")
url = f"https://nsearchives.nseindia.com/content/indices/ind_close_all_{str(trade_date.strftime('%d%m%Y').upper())}.csv"
# nse_resp = nse_urlfetch(url)
nse_resp = requests.get(url, headers=self.headers)
if nse_resp.status_code != 200:
raise FileNotFoundError(f" No data available for : {trade_date}")
try:
bhav_df = pd.read_csv(BytesIO(nse_resp.content))
except Exception as e:
raise FileNotFoundError(f' Bhav copy indices not found for : {trade_date} :: NSE error : {e}')
return bhav_df
def fii_dii_activity(self):
"""
FII and DII trading activity of the day in data frame
:return: pd.DataFrame
"""
url = "https://www.nseindia.com/api/fiidiiTradeReact"
# data_json = nse_urlfetch(url).json()
data_json = requests.get(url, headers=self.headers)
data_df = pd.DataFrame(data_json.json())
return data_df
def get_live_option_chain(self, symbol: str, expiry_date: str = None, oi_mode: str = "full", indices=False):
"""
get live nse option chain.
:param symbol: eg:SBIN/BANKNIFTY
:param expiry_date: '20-06-2023'
:param oi_mode: eg: full/compact
:return: pands dataframe
"""
symbol = symbol.replace(' ', '%20').replace('&', '%26')
ref_url = 'https://www.nseindia.com/option-chain'
ref = requests.get(ref_url, headers=self.headers)
if not indices:
url = 'https://www.nseindia.com/api/option-chain-equities?symbol=' + symbol
else:
# ref_url = 'https://www.nseindia.com/option-chain'
# ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/option-chain-indices?symbol=' + symbol
payload = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
# payload = get_nse_option_chain(symbol).json()
if expiry_date:
exp_date = pd.to_datetime(expiry_date, format='%d-%m-%Y')
expiry_date = exp_date.strftime('%d-%b-%Y')
if oi_mode == 'compact':
col_names = ['Fetch_Time', 'Symbol', 'Expiry_Date', 'CALLS_OI', 'CALLS_Chng_in_OI', 'CALLS_Volume',
'CALLS_IV',
'CALLS_LTP', 'CALLS_Net_Chng', 'Strike_Price', 'PUTS_OI', 'PUTS_Chng_in_OI', 'PUTS_Volume',
'PUTS_IV', 'PUTS_LTP', 'PUTS_Net_Chng']
else:
col_names = ['Fetch_Time', 'Symbol', 'Expiry_Date', 'CALLS_OI', 'CALLS_Chng_in_OI', 'CALLS_Volume',
'CALLS_IV',
'CALLS_LTP', 'CALLS_Net_Chng', 'CALLS_Bid_Qty', 'CALLS_Bid_Price', 'CALLS_Ask_Price',
'CALLS_Ask_Qty', 'Strike_Price', 'PUTS_Bid_Qty', 'PUTS_Bid_Price', 'PUTS_Ask_Price',
'PUTS_Ask_Qty',
'PUTS_Net_Chng', 'PUTS_LTP', 'PUTS_IV', 'PUTS_Volume', 'PUTS_Chng_in_OI', 'PUTS_OI']
oi_data = pd.DataFrame(columns=col_names)
oi_row = {'Fetch_Time': None, 'Symbol': None, 'Expiry_Date': None, 'CALLS_OI': 0, 'CALLS_Chng_in_OI': 0,
'CALLS_Volume': 0,
'CALLS_IV': 0, 'CALLS_LTP': 0, 'CALLS_Net_Chng': 0, 'CALLS_Bid_Qty': 0, 'CALLS_Bid_Price': 0,
'CALLS_Ask_Price': 0, 'CALLS_Ask_Qty': 0, 'Strike_Price': 0, 'PUTS_OI': 0, 'PUTS_Chng_in_OI': 0,
'PUTS_Volume': 0, 'PUTS_IV': 0, 'PUTS_LTP': 0, 'PUTS_Net_Chng': 0, 'PUTS_Bid_Qty': 0,
'PUTS_Bid_Price': 0, 'PUTS_Ask_Price': 0, 'PUTS_Ask_Qty': 0}
for m in range(len(payload['records']['data'])):
if not expiry_date or (payload['records']['data'][m]['expiryDate'] == expiry_date):
try:
oi_row['Expiry_Date'] = payload['records']['data'][m]['expiryDate']
oi_row['CALLS_OI'] = payload['records']['data'][m]['CE']['openInterest']
oi_row['CALLS_Chng_in_OI'] = payload['records']['data'][m]['CE']['changeinOpenInterest']
oi_row['CALLS_Volume'] = payload['records']['data'][m]['CE']['totalTradedVolume']
oi_row['CALLS_IV'] = payload['records']['data'][m]['CE']['impliedVolatility']
oi_row['CALLS_LTP'] = payload['records']['data'][m]['CE']['lastPrice']
oi_row['CALLS_Net_Chng'] = payload['records']['data'][m]['CE']['change']
if oi_mode == 'full':
oi_row['CALLS_Bid_Qty'] = payload['records']['data'][m]['CE']['bidQty']
oi_row['CALLS_Bid_Price'] = payload['records']['data'][m]['CE']['bidprice']
oi_row['CALLS_Ask_Price'] = payload['records']['data'][m]['CE']['askPrice']
oi_row['CALLS_Ask_Qty'] = payload['records']['data'][m]['CE']['askQty']
except KeyError:
oi_row['CALLS_OI'], oi_row['CALLS_Chng_in_OI'], oi_row['CALLS_Volume'], oi_row['CALLS_IV'], oi_row[
'CALLS_LTP'], oi_row['CALLS_Net_Chng'] = 0, 0, 0, 0, 0, 0
if oi_mode == 'full':
oi_row['CALLS_Bid_Qty'], oi_row['CALLS_Bid_Price'], oi_row['CALLS_Ask_Price'], oi_row[
'CALLS_Ask_Qty'] = 0, 0, 0, 0
pass
oi_row['Strike_Price'] = payload['records']['data'][m]['strikePrice']
try:
oi_row['PUTS_OI'] = payload['records']['data'][m]['PE']['openInterest']
oi_row['PUTS_Chng_in_OI'] = payload['records']['data'][m]['PE']['changeinOpenInterest']
oi_row['PUTS_Volume'] = payload['records']['data'][m]['PE']['totalTradedVolume']
oi_row['PUTS_IV'] = payload['records']['data'][m]['PE']['impliedVolatility']
oi_row['PUTS_LTP'] = payload['records']['data'][m]['PE']['lastPrice']
oi_row['PUTS_Net_Chng'] = payload['records']['data'][m]['PE']['change']
if oi_mode == 'full':
oi_row['PUTS_Bid_Qty'] = payload['records']['data'][m]['PE']['bidQty']
oi_row['PUTS_Bid_Price'] = payload['records']['data'][m]['PE']['bidprice']
oi_row['PUTS_Ask_Price'] = payload['records']['data'][m]['PE']['askPrice']
oi_row['PUTS_Ask_Qty'] = payload['records']['data'][m]['PE']['askQty']
except KeyError:
oi_row['PUTS_OI'], oi_row['PUTS_Chng_in_OI'], oi_row['PUTS_Volume'], oi_row['PUTS_IV'], oi_row[
'PUTS_LTP'], oi_row['PUTS_Net_Chng'] = 0, 0, 0, 0, 0, 0
if oi_mode == 'full':
oi_row['PUTS_Bid_Qty'], oi_row['PUTS_Bid_Price'], oi_row['PUTS_Ask_Price'], oi_row[
'PUTS_Ask_Qty'] = 0, 0, 0, 0
if oi_data.empty:
oi_data = pd.DataFrame([oi_row]).copy()
else:
oi_data = pd.concat([oi_data, pd.DataFrame([oi_row])], ignore_index=True)
oi_data['Symbol'] = symbol
oi_data['Fetch_Time'] = payload['records']['timestamp']
return oi_data
def get_market_depth(self, symbol):
"""
Function to retrieve market depth for a given symbol
:param symbol:
:return: Market Dept as Dict
"""
data = self.equity_info(symbol)
merged_dict = {
'ask': data['tradeData']['marketDeptOrderBook']['ask'],
'bid': data['tradeData']['marketDeptOrderBook']['bid']
}
return merged_dict
def get_index_historic_data(self, index: str, from_date: str = None, to_date: str = None):
"""
get historical index data set for the specific time period.
apply the index name as per the nse india site
:param index: 'NIFTY 50'/'NIFTY BANK'
:param from_date: '17-03-2022' ('dd-mm-YYYY')
:param to_date: '17-06-2023' ('dd-mm-YYYY')
:return: pandas.DataFrame
:raise ValueError if the parameter input is not proper
"""
index_data_columns = ['TIMESTAMP', 'INDEX_NAME', 'OPEN_INDEX_VAL', 'HIGH_INDEX_VAL', 'CLOSE_INDEX_VAL',
'LOW_INDEX_VAL', 'TRADED_QTY', 'TURN_OVER']
# Check for valid dates and period inputs
if not from_date or not to_date:
raise ValueError(' Please provide the valid parameters')
try:
from_dt = datetime.strptime(from_date, "%d-%m-%Y")
to_dt = datetime.strptime(to_date, "%d-%m-%Y")
time_delta = (to_dt - from_dt).days
if time_delta < 1:
raise ValueError(f'to_date should greater than from_date ')
except Exception as e:
print(e)
raise ValueError(f'either or both from_date = {from_date} || to_date = {to_date} are not valid value')
nse_df = pd.DataFrame(columns=index_data_columns)
from_date = datetime.strptime(from_date, "%d-%m-%Y")
to_date = datetime.strptime(to_date, "%d-%m-%Y")
load_days = (to_date - from_date).days
while load_days > 0:
if load_days > 365:
end_date = (from_date + timedelta(364)).strftime("%d-%m-%Y")
start_date = from_date.strftime("%d-%m-%Y")
else:
end_date = to_date.strftime("%d-%m-%Y")
start_date = from_date.strftime("%d-%m-%Y")
data_df = self.get_index_data(index=index, from_date=start_date, to_date=end_date)
from_date = from_date + timedelta(365)
load_days = (to_date - from_date).days
if nse_df.empty:
nse_df = data_df
else:
nse_df = pd.concat([nse_df, data_df], ignore_index=True)
return nse_df
def get_index_data(self, index: str, from_date: str, to_date: str):
index_data_columns = ['TIMESTAMP', 'INDEX_NAME', 'OPEN_INDEX_VAL', 'HIGH_INDEX_VAL', 'CLOSE_INDEX_VAL',
'LOW_INDEX_VAL', 'TRADED_QTY', 'TURN_OVER']
index = index.replace(' ', '%20').upper()
ref_url = 'https://www.nseindia.com/reports-indices-historical-index-data'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/historical/indicesHistory?indexType={index}&from={from_date}&to={to_date}"
try:
data_json = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict()).json()
data_close_df = pd.DataFrame(data_json['data']['indexCloseOnlineRecords']).drop(
columns=['_id', "EOD_TIMESTAMP"])
data_turnover_df = pd.DataFrame(data_json['data']['indexTurnoverRecords']).drop(columns=['_id',
'HIT_INDEX_NAME_UPPER'])
data_df = pd.merge(data_close_df, data_turnover_df, on='TIMESTAMP', how='inner')
except Exception as e:
raise " Resource not available"
data_df.drop(columns='TIMESTAMP', inplace=True)
unwanted_str_list = ['FH_', 'EOD_', 'HIT_']
new_col = data_df.columns
for unwanted in unwanted_str_list:
new_col = [name.replace(f'{unwanted}', '') for name in new_col]
data_df.columns = new_col
return data_df[index_data_columns]
def get_equity_full_list(self, list_only=False):
"""
get list of all equity available to trade in NSE
:param list_only: Optional. If you only need the symbols in a list, set this to true.
Otherwise, the full table is downloaded as a dataframe
:return: pandas data frame
"""
url = "https://archives.nseindia.com/content/equities/EQUITY_L.csv"
nse_resp = self.session.get(url, headers=self.headers, cookies=self.cookies)
if nse_resp.status_code != 200:
raise FileNotFoundError(f" No data equity list available")
try:
data_df = pd.read_csv(BytesIO(nse_resp.content))
except Exception as e:
raise FileNotFoundError(f' Equity List not found :: NSE error : {e}')
data_df = data_df[['SYMBOL', 'NAME OF COMPANY', ' SERIES', ' DATE OF LISTING', ' FACE VALUE']]
if list_only:
symbol_list = data_df['SYMBOL'].tolist()
return symbol_list
return data_df
def get_fno_full_list(self, list_only=False):
"""
get a dataframe of all listed derivative list with the recent lot size to trade
:param list_only: Optional. If you only need the symbols in a list, set this to true.
Otherwise, the full table is downloaded as a dataframe
:return: pandas data frame
"""
ref_url = 'https://www.nseindia.com/products-services/equity-derivatives-list-underlyings-information'
ref = requests.get(ref_url, headers=self.headers)
url = "https://www.nseindia.com/api/underlying-information"
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if response.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = response.json()
data_df = pd.DataFrame(data_dict['data']['UnderlyingList'])
if list_only:
symbol_list = data_df['symbol'].tolist()
return symbol_list
return data_df
def get_gainers_losers(self):
gain_loss_dict = {}
ref_url = 'https://www.nseindia.com/market-data/top-gainers-losers'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-variations?index=gainers'
data_obj = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if data_obj.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = data_obj.json()
# Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTY']['data'])
nifty_gainer = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['BANKNIFTY']['data'])
banknifty_gainer = data_df['symbol'].to_list()
# Nifty Next 50 Gainer
data_df = pd.DataFrame(data_dict['NIFTYNEXT50']['data'])
next50_gainer = data_df['symbol'].to_list()
# All Securities Gainer
data_df = pd.DataFrame(data_dict['allSec']['data'])
allsec_gainer = data_df['symbol'].to_list()
# FNO Securities Gainer
data_df = pd.DataFrame(data_dict['FOSec']['data'])
fno_gainer = data_df['symbol'].to_list()
url = 'https://www.nseindia.com/api/live-analysis-variations?index=loosers'
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
if data_obj.status_code != 200:
raise ("Resource not available for fno_equity_list")
data_dict = data_obj.json()
# Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTY']['data'])
nifty_loser = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['BANKNIFTY']['data'])
banknifty_loser = data_df['symbol'].to_list()
# Bank Nifty Gainer
data_df = pd.DataFrame(data_dict['NIFTYNEXT50']['data'])
next50_loser = data_df['symbol'].to_list()
# All Securities Gainer
data_df = pd.DataFrame(data_dict['allSec']['data'])
allsec_loser = data_df['symbol'].to_list()
# FNO Securities Gainer
data_df = pd.DataFrame(data_dict['FOSec']['data'])
fno_loser = data_df['symbol'].to_list()
gain_dict = {
'Nifty Gainer': nifty_gainer,
'Bank Nifty Gainer': banknifty_gainer,
'Nifty Next 50 Gainer': next50_gainer,
'All Securities Gainer': allsec_gainer,
'FNO Gainer': fno_gainer
}
loss_dict = {
'Nifty Loser': nifty_loser,
'Bank Nifty Loser': banknifty_loser,
'Nifty Next 50 Loser': next50_loser,
'All Securities Loser': allsec_loser,
'FNO Loser': fno_loser
}
return gain_dict, loss_dict
def get_corporate_action(self, from_date_str: str = None, to_date_str: str = None, filter: str = None):
# Fetch Corporate Action data from NSE
if from_date_str is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
if to_date_str is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
try:
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-actions'
ref = requests.get(ref_url, headers=self.headers)
url = f"https://www.nseindia.com/api/corporates-corporateActions?index=equities&from_date={from_date_str}&to_date={to_date_str}"
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
corp_action = pd.DataFrame(data_obj.json())
if filter is not None:
corp_action = corp_action[corp_action['subject'].str.contains(filter, case=False, na=False)]
return corp_action
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_corporate_announcement(self, from_date_str: str = None, to_date_str: str = None):
# Fetch Corporate Announcements data from NSE
if from_date_str is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
if to_date_str is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
try:
ref_url = ('https://www.nseindia.com/companies-listing/corporate-filings-announcements')
ref = requests.get(ref_url, headers=self.headers)
url = f'https://www.nseindia.com/api/corporate-announcements?index=equities&from_date={from_date_str}&to_date={to_date_str}'
data_obj = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
corp_announcement = pd.DataFrame(data_obj.json())
return corp_announcement
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_index_pe_ratio(self):
# try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'pe']]
df = df[df['pe'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['pe'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Profit Earning Ratio']
return df
else:
return None
# except:
# print("Error fetching Corporate Action Data. Check your input")
# return None
def get_index_pb_ratio(self):
try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'pb']]
df = df[df['pb'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['pb'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Price Book Ratio']
return df
else:
return None
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_index_div_yield(self):
try:
ref_url = 'https://www.nseindia.com/market-data/index-performances'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'key', 'dy']]
df = df[df['dy'].str.strip() != ''] # Removes rows where PE is an empty string
df = df[df['dy'].str.strip() != 'None'] # Removes rows where PE is none
df.columns = ['Index', 'Type', 'Div Yield']
return df
else:
return None
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_advance_decline(self):
try:
ref_url = 'https://www.nseindia.com/market-data/live-market-indices'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/allIndices'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.json_normalize(data['data']) # Extract the main data list
# Retain only PE Ratio
if not df.empty:
df = df[['indexSymbol', 'advances', 'declines', 'unchanged']]
df.dropna(inplace=True)
df.columns = ['Index', 'Advances', 'Declines', 'Unchanged']
return df
else:
return None
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_equity_stocks_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-equities'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-most-active-securities?index=volume'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_equity_stocks_by_value(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-equities'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/live-analysis-most-active-securities?index=value'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_index_calls(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=calls-index-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTIDX']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_index_puts(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=puts-index-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTIDX']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_stock_calls(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=calls-stocks-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTSTK']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_stock_puts(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=puts-stocks-vol'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['OPTSTK']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_contracts_by_oi(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=oi'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=contracts'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_futures_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=futures'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def most_active_options_contracts_by_volume(self):
try:
ref_url = 'https://www.nseindia.com/market-data/most-active-contracts'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/snapshot-derivatives-equity?index=options&limit=20'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['volume']['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_insider_trading(self, from_date: str = None, to_date: str = None):
try:
if from_date is None:
from_date = datetime.now() - timedelta(days=30)
from_date_str = from_date.strftime("%d-%m-%Y")
to_date_str = datetime.now().strftime("%d-%m-%Y")
else:
from_date_str = from_date
if to_date is None:
to_date_str = datetime.now().strftime("%d-%m-%Y")
else:
to_date_str = to_date
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-insider-trading'
ref = requests.get(ref_url, headers=self.headers)
url= f'https://www.nseindia.com/api/corporates-pit?index=equities&from_date={from_date_str}&to_date={to_date_str}'
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json()
df = pd.DataFrame(data['data'])
return df
except Exception as e:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_upcoming_results_calendar(self):
# Extracts the events calendar from NSE - Filters only the upcoming Financial results related events
try:
ref_url = 'https://www.nseindia.com/companies-listing/corporate-filings-event-calendar'
ref = requests.get(ref_url, headers=self.headers)
url= f'https://www.nseindia.com/api/event-calendar?'
response = requests.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json()
df = pd.DataFrame(data)
events = df[df['purpose'].str.contains('Results', case=False, na=False)]
return events
except:
print("Error fetching Corporate Action Data. Check your input")
return None
def get_etf_list(self):
try:
ref_url = 'https://www.nseindia.com/market-data/exchange-traded-funds-etf'
ref = requests.get(ref_url, headers=self.headers)
url = 'https://www.nseindia.com/api/etf'
response = self.session.get(url, headers=self.headers, cookies=ref.cookies.get_dict())
data = response.json() # Convert response to JSON
# Convert JSON data to a DataFrame
df = pd.DataFrame(data['data']) # Extract the main data list
if df.empty:
return None
else:
return df
except Exception as e:
print("Error fetching ETF list. Check your input")
return None
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Vivek is an algorithmic trader, Python programmer, and a passionate advocate of the F.I.R.E. (Financial Independence, Retire Early) movement. He achieved his financial independence at the age of 45 and is dedicated to helping others embark on their own journeys toward financial freedom.