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NSEpy 0.3

Python Library to get publicly available data on NSE website ie. stock quotes, historical data, live indices

Release Notes

  • Added support for derivative data. (Probably only API for indian derivative and India VIX data as Yahoo API has no support for derivatives)
  • Unified and simplified API for all (Equity, Index, Derivative, Volatility Indexes-INDIAVIX)
  • Compatible and Tested with Python 2.7 and 3.4

##Libraries Required

  • requests
  • beautifulsoup
  • numpy
  • scipy
  • pandas

For Windows systems you can install Anaconda, this will cover many dependancies (You'll have to install requests and beautifulsoup additionally though)

##Installation $pip install nsepy ##Usage

Get the price history of stocks and NSE indices directly in pandas dataframe-

from nsepy import get_history, get_index_pe_history
from datetime import date
"""
	get_history argument list
			symbol (str): Symbol for stock (SBIN, RELIANCE etc.), index (NIFTY, BANKNIFTY etc) or any security (Index names "NIFTY 50", "INDIAVIX" etc.
			start (datetime.date): start date 
            end (datetime.date): end date
            index (boolean): False by default, True if its an index, index futures or options and also for INDIAVIX
            futures (boolean): False by default, True for index and stock futures only (should not be set to True with option_type specified)
            expiry_date (datetime.date): Expiry date for derivatives, Compulsory parameter for futures and options
            option_type (str): It takes "CE", "PE", "CA", "PA" for European and American calls and puts
            strike_price (int): Strike price, Compulsory parameter for options
            series (str): Defaults to "EQ", but can be "BE" etc (refer NSE website for details)
"""

#Stock history
sbin = get_history(symbol='SBIN',
                    start=date(2015,1,1), 
                    end=date(2015,1,10))
sbin[[ 'VWAP', 'Turnover']].plot(secondary_y='Turnover')

"""	Index price history
	symbol can take these values (These indexes have derivatives as well)
	"NIFTY" or "NIFTY 50",
    "BANKNIFTY" or "NIFTY BANK",
    "NIFTYINFRA" or "NIFTY INFRA",
    "NIFTYIT" or "NIFTY IT",
    "NIFTYMID50" or "NIFTY MIDCAP 50",
    "NIFTYPSE" or "NIFTY PSE"
	In addition to these there are many indices
	For full list refer- http://www.nseindia.com/products/content/equities/indices/historical_index_data.htm
"""
nifty = get_history(symbol="NIFTY", 
                    start=date(2015,1,1), 
                    end=date(2015,1,10),
					index=True)
nifty[['Close', 'Turnover']].plot(secondary_y='Turnover')

#Futures and Options historical data
nifty_fut = get_history(symbol="NIFTY", 
						start=date(2015,1,1), 
						end=date(2015,1,10),
						index=True,
						futures=True, expiry_date=date(2015,1,29))
						
stock_opt = get_history(symbol="SBIN",
						start=date(2015,1,1), 
						end=date(2015,1,10),
						option_type="CE",
						strike_price=300,
						expiry_date=date(2015,1,29))

#Index P/E ratio history
nifty_pe = get_index_pe_history(symbol="NIFTY",
								start=date(2015,1,1), 
								end=date(2015,1,10))

Sample contents of the one of the dataframe (I'm using Anaconda and Spyder)-

In[6]: nifty_fut
Out[6]: 
           Symbol      Expiry     Open    High      Low    Close     Last  
Date                                                                        
2015-01-01  NIFTY  2015-01-29  8320.00  8356.0  8295.20  8343.80  8347.05   
2015-01-02  NIFTY  2015-01-29  8352.25  8470.9  8352.25  8458.40  8468.00   
2015-01-05  NIFTY  2015-01-29  8452.35  8492.0  8406.00  8422.85  8423.85   
2015-01-06  NIFTY  2015-01-29  8422.00  8422.0  8000.00  8157.90  8150.30   
2015-01-07  NIFTY  2015-01-29  8150.00  8184.0  8096.00  8141.85  8154.00   
2015-01-08  NIFTY  2015-01-29  8209.00  8274.9  8193.10  8257.25  8255.00   
2015-01-09  NIFTY  2015-01-29  8306.35  8334.0  8205.00  8315.50  8311.60   

            Settle Price  Number of Contracts      Turnover  Open Interest  
Date                                                                         
2015-01-01       8343.80               152053  3.165350e+10       21140550   
2015-01-02       8458.40               384440  8.105711e+10       21427925   
2015-01-05       8422.85               362889  7.661895e+10       20698500   
2015-01-06       8157.90               807830  1.663583e+11       19157775   
2015-01-07       8141.85               513814  1.046381e+11       18716025   
2015-01-08       8257.25               409705  8.433153e+10       17798500   
2015-01-09       8315.50               596384  1.234251e+11       17111350   

            Change in OI  Underlying  
Date                                  
2015-01-01        -28675     8284.00  
2015-01-02        287375     8395.45  
2015-01-05       -729425     8378.40  
2015-01-06      -1540725     8127.35  
2015-01-07       -441750     8102.10  
2015-01-08       -917525     8234.60  
2015-01-09       -687150     8284.50  

Below functions still work but these will be depricated soon

from nsepy.archives import get_price_history
from nsepy import indices
from datetime import date
#Stock price history
sbin = get_price_history(stock = 'SBIN',
                        start = date(2015,1,1), 
                        end = date(2015,1,10))
sbin[[ 'VWAP', 'Turnover']].plot(secondary_y='Turnover')
#Index price history
nifty = indices.archives.get_price_history(index = "NIFTY 50", 
                                            start = date(2015,9,1), 
                                            end = date(2015,9,24))
nifty[['Close', 'Turnover']].plot(secondary_y='Turnover')
#Index P/E ratio history
nifty_pe = indices.archives.get_pe_history(index = "NIFTY 50", 
                                            start = date(2015,9,1), 
                                            end = date(2015,9,24))
nifty_pe['Index'] = nifty['Close']
nifty_pe[['Index', 'P/E']].plot(secondary_y='P/E')

To do-

  • Support for live data

Visit my blog to explore other projects

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Python Library to get publicly available data on NSE website ie. stock quotes, historical data, live indices

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