nse-live-stocks 1.1

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nse_live_stocks

Python library for extracting realtime stock data from National Stock Exchange (India) .

Introduction.

nselivestocks library is to collect the real time stock information just by giving the nse symbol. This library data accuracy is depends upon 'www.nseindia.com'.

Features

Nse stock live price

Nse stock information

Download historical Data (from v0.8)

Package link

https://pypi.org/project/nse-live-stocks/

Command to install

pip install nse-live-stocks

---

i) Get live price of any nse stock

from nse_live_stocks import Nse

stock = Nse()

result = stock.get_current_price('TCS')

print(result)

Output:

{'error': False, 'nse_symbol': 'TCS', 'current_value': '4393.65', 'date': '26-Jul-2024 09:07:13'}

---

ii) Download the Historical data from nseindia.

name: CM-UDiFF Common Bhavcopy Final (zip) | cm bhav csv (zip)

Type: daily-reports

Category: capital-market

Sector: equities

Inputs:

1. Date - you want to download the historical data. format is '%m-%d-%Y'

2. Fullpath - Full path to download the historical data file

Example in my case path is '/Users/akhilkodati/Music/myworks/'

default path is Downloads folder in Home directory.

from nse_live_stocks import Nse

stock = Nse()

To Download historical file in Default directory.

result = stock.download_nse_historical_data_csv('06-26-2024')

Output:

{'error': False, 'requesteddate': '06-26-2024', 'message': 'File Downloaded to /Users/akhilkodati/Downloads/'}

To Download historical file in custom directory

result = stock.download_nse_historical_data_csv('06-26-2024','/Users/akhilkodati/Music/myworks/')

print(result)

Output:

{'error': False, 'requesteddate': '06-26-2024', 'message': 'File Downloaded to /Users/akhilkodati/Music/myworks/'}

Tested from 01-01-2016.(You can download historical data from 01-01-2016) If requesteddate before 08/July/2024 it downloads the cm bhav csv(zip) file else it downloads the CM-UDiFF Common Bhavcopy Final (zip)

---

iii) Get Complete information of any nse stock

from nse_live_stocks import Nse

stock = Nse()

result = stock.get_stock_info('ABB')

print(result)

Output:

{'info': {'symbol': 'ABB', 'companyName': 'ABB India Limited', 'industry': 'ELECTRICAL EQUIPMENT', 'activeSeries': ['EQ'], 'debtSeries': [], 'isFNOSec': True, 'isCASec': False, 'isSLBSec': True, 'isDebtSec': False, 'isSuspended': False, 'tempSuspendedSeries': [], 'isETFSec': False, 'isDelisted': False, 'isin': 'INE117A01022', 'isMunicipalBond': False, 'isTop10': False, 'identifier': 'ABBEQN'}, 'metadata': {'series': 'EQ', 'symbol': 'ABB', 'isin': 'INE117A01022', 'status': 'Listed', 'listingDate': '08-Feb-1995', 'industry': 'Heavy Electrical Equipment', 'lastUpdateTime': '26-Jul-2024 16:00:00', 'pdSectorPe': 110.46, 'pdSymbolPe': 110.46, 'pdSectorInd': 'NIFTY NEXT 50', 'pdSectorIndAll': ['NIFTY NEXT 50', 'NIFTY TOTAL MARKET', 'NIFTY MNC', 'NIFTY 200', 'NIFTY100 QUALITY 30', 'NIFTY ALPHA 50', 'NIFTY100 EQUAL WEIGHT', 'NIFTY100 ESG SECTOR LEADERS', 'NIFTY LARGEMIDCAP 250', 'NIFTY500 MULTICAP 50:25:25', 'NIFTY200 MOMENTUM 30', 'NIFTY500 LARGEMIDSMALL EQUAL-CAP WEIGHTED', 'NIFTY200 ALPHA 30', 'NIFTY500 EQUAL WEIGHT', 'NIFTY100 ESG', 'NIFTY500 MOMENTUM 50', 'NIFTY INDIA MANUFACTURING', 'NIFTY 500', 'NIFTY 100']}, 'securityInfo': {'boardStatus': 'Main', 'tradingStatus': 'Active', 'tradingSegment': 'Normal Market', 'sessionNo': '-', 'slb': 'Yes', 'classOfShare': 'Equity', 'derivatives': 'Yes', 'surveillance': {'surv': None, 'desc': None}, 'faceValue': 2, 'issuedSize': 211908375}, 'sddDetails': {'SDDAuditor': '-', 'SDDStatus': '-'}, 'priceInfo': {'lastPrice': 7849, 'change': 225.19999999999982, 'pChange': 2.95390750019675, 'previousClose': 7623.8, 'open': 7698.95, 'close': 7851.25, 'vwap': 7789.44, 'lowerCP': '6861.45', 'upperCP': '8386.15', 'pPriceBand': 'No Band', 'basePrice': 7623.8, 'intraDayHighLow': {'min': 7649.35, 'max': 7870, 'value': 7849}, 'weekHighLow': {'min': 3850, 'minDate': '26-Oct-2023', 'max': 9149.95, 'maxDate': '18-Jun-2024', 'value': 7849}, 'iNavValue': None, 'checkINAV': False, 'tickSize': 0.05}, 'industryInfo': {'macro': 'Industrials', 'sector': 'Capital Goods', 'industry': 'Electrical Equipment', 'basicIndustry': 'Heavy Electrical Equipment'}, 'preOpenMarket': {'preopen': [{'price': 7471.35, 'buyQty': 0, 'sellQty': 1}, {'price': 7599, 'buyQty': 0, 'sellQty': 1}, {'price': 7608, 'buyQty': 0, 'sellQty': 5}, {'price': 7620, 'buyQty': 0, 'sellQty': 35}, {'price': 7698.95, 'buyQty': 0, 'sellQty': 0, 'iep': True}, {'price': 7800.4, 'buyQty': 1, 'sellQty': 0}, {'price': 7800.9, 'buyQty': 2, 'sellQty': 0}, {'price': 7852.5, 'buyQty': 26, 'sellQty': 0}, {'price': 7918.6, 'buyQty': 58, 'sellQty': 0}], 'ato': {'buy': 71, 'sell': 55}, 'IEP': 7698.95, 'totalTradedVolume': 264, 'finalPrice': 7698.95, 'finalQuantity': 264, 'lastUpdateTime': '26-Jul-2024 09:07:11', 'totalBuyQuantity': 2977, 'totalSellQuantity': 5441, 'atoBuyQty': 71, 'atoSellQty': 55, 'Change': 75.14999999999964, 'perChange': 0.9857289015976237, 'prevClose': 7623.8}}

Thank you.

Developed by Akhil kodati

If you like my work please follow me on https://github.com/Akhilkodati