Here is a simple example to compute Cointegration between two stock pairs using python libraries like NSEpy, Pandas, statmodels, matplotlib
Cointegration is used in Statistical Arbitrage to find the best Pair of Stocks (Pair Trading) to go long in one stock and short(Competitive peers) in another to generate returns. Statistical Arbitrage(StatArb) is all about mean reversion, looking for deviation in the spreads and expecting mean reversion from the spread.
NSEpy – fetches historical data from nseindia.com
Pandas – Python library to handle time series data
Statmodels – Python library to handle statistical operations like cointegration
Matplotlib – Python library to handle 2D chart plotting
NSEpy is an open-source Python library for collecting historical data from the National Stock Exchange (NSE) of India. The library provides easy-to-use functions for accessing data related to stocks, indices, and derivatives traded on the NSE.
The library utilizes web scraping techniques to retrieve data from the NSE website and provide data in a pandas DataFrame format, which can be easily manipulated and analyzed using Python. The data can be retrieved for various time intervals, ranging from daily to monthly, yearly, or customized intervals.
Importing the Libraries, Fetching Data from NSE and Computing Cointegration
import numpy as np
import pandas as pd
import statsmodels
from statsmodels.tsa.stattools import coint
import matplotlib.pyplot as plt
import nsepy
from datetime import date
S1 = nsepy.get_history(symbol = 'SBIN',
start = date(2015,1,1),
end = date(2015,10,10))
S2 = nsepy.get_history(symbol = 'ICICIBANK',
start = date(2015,1,1),
end = date(2015,10,10))
result = coint(S1[['Close']], S2[['Close']])
score = result[0]
pvalue = result[1]Plot State Bank of India Dataframe
S1[['Close']].plot()
Plot ICICI Bank Dataframe
S2[['Close']].plot()
Calculate the p-value
score, pvalue, _ = coint(S1[['Close']], S2[['Close']])
pvalueOutput
0.0052518039905594Calculate the Spread
diff_series= S2[['Close']] - S1[['Close']]
diff_series.plot()
Calculate and Plot the Z-score with +/- 2 SD levels
def zscore(series):
return (series - series.mean()) / np.std(series)
zscore(diff_series).plot()
#plt.axhline(zscore(diff_series).mean(), color='black')
plt.axhline(1.0, color='red', linestyle='--')
plt.axhline(-1.0, color='green', linestyle='--')Simple Strategy using Co-Integration
Go “Long” the spread whenever the z-score is below -1.0
Go “Short” the spread when the z-score is above 1.0
Exit positions when the z-score approaches zero
Since we originally defined the “spread” as S1-S2, “Long” the spread would mean “Buy 1 share of S1, and Sell Short 1 share of S2” (and vice versa if you were going “Short” the spread)
Great post Rajandran
Is there any specific version of python that would you recommend. Else may i know the python version that you are trying this on.
Regards,
Surya
We are using Python 2.7
hello Rajandran
i want your support will u please give your email.id or phone no
do send a mail to [email protected]
Happy to see my work in action, Thanks for using the library!!
Do let me know if any feedbacks!!
Please do try https://github.com/swapniljariwala/zerodha as well and let me know feedback
I am using nsepy but it is not working to fetch indices data to manipulate for our reqirement.
what i have to do.
What error you are facing?
now due to chane in indices symbol free downloaders(eod nse) can not download indices. data
how to download or extract some main indices data from indices snapshot csv data file quickly.
i need nifty, ,banknifty, nse it, nifty junior, midcap50, nse 100 etc
I’ve updated the API, The updated API will solve your problem
https://github.com/swapniljariwala/nsepy
Simple few lines of code and you are done
from nsepy import get_history, get_index_pe_history
from datetime import date
#Index price history
nifty = get_history(symbol=”NIFTY”,
start=date(2015,1,1),
end=date(2015,1,10),
index=True)
nifty[[‘Close’, ‘Turnover’]].plot(secondary_y=’Turnover’)
Hello all,
Please check out the latest version of NSEpy, I’ve added the support for
Derivative data. (Probably only API for indian derivative and India VIX data as Yahoo API has no support for derivatives), Index data
Unified and simplified API for all (Equity, Index, Derivative, Volatility Indexes-INDIAVIX)
Compatible and Tested with Python 2.7 and 3.4
Examples on git-hub page-
https://github.com/swapniljariwala/nsepy
Please report any bugs or issues on –
https://github.com/swapniljariwala/nsepy/issues
Sir, I beginner in Algo Trading trying to use my quant knowlegde. I am using NSEpy though getting error like “ValueError: negative dimensions are not allowed” and “AttributeError: ‘ThreadReturns’ object has no attribute ‘result’. Could you please help me. I would thankful to you. My code is mentioned below:
#importing modules
import pandas as pd
import numpy as np
import statsmodels
from statsmodels.tsa.stattools import coint
import matplotlib.pyplot as plt
import datetime as date
import nsepy
from nsepy.archives import get_price_history
import seaborn
#Defining Cointegrated pairs formula
def find_cointegrated_pairs(stockdata):
n = len(stockdata.minor_axis)
score_matrix = np.zeros((n, n))
pvalue_matrix = np.ones((n, n))
keys = stockdata.keys
pairs = []
for i in range(n):
for j in range(i+1, n):
S1 = stockdata.minor_xs(stockdata.minor_axis[i])
S2 = stockdata.minor_xs(stockdata.minor_axis[j])
result = coint(S1, S2)
score = result[0]
pvalue = result[1]
score_matrix[i, j] = score
pvalue_matrix[i, j] = pvalue
if pvalue = 0.99))
print (pairs)
I am getting same error, did you find the solution ?
if pvalue < 0.05:
pairs.append((stockdata.minor_axis[i], stockdata.minor_axis[j]))
return score_matrix, pvalue_matrix, pairs
#time period of testing
histdata = {}
startdate = date.datetime(2016,5,20)
enddate = date.datetime.today()
#Dowloading NSE Symbols
url = "https://www.nseindia.com/content/indices/ind_nifty50list.csv"
csvfromurl = req.get(url).content
csvfordf = pd.read_csv(strio.StringIO(csvfromurl.decode('utf-8')))
nifty50df = pd.DataFrame(csvfordf)
#fetching data
for eachscrip in nifty50df[‘Symbol’]:
try:
stockdata = get_price_history(stock = eachscrip, start = startdate, end = enddate)
except (ValueError,IOError) as err:
print(err)
#Arranging pairs combination
stockdata.minor_axis = map(lambda x: x.Symbol, stockdata.minor_axis)
Getting above two errors when fetching data
Is there anyone who is still using NSEpy ?
I m newbie with python but willing to learn.
Hi
How do i get live data using nsepy?
Hey hi. Can you help with adjusting historical prices with splits and bonuses
how to get live data using nsepy