# Compute Cointegration using NsePy, Pandas Library

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']])
pvalue``````

Output

``0.0052518039905594``

``````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)

Sample IPython Notebook to compute Cointegration below :

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## 20 Replies to “Compute Cointegration using NsePy, Pandas Library”

1. Surya says:

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

2. venkateshwarreddy says:

hello Rajandran

3. Swapnil says:

Happy to see my work in action, Thanks for using the library!!

Do let me know if any feedbacks!!

4. Rajeev says:

I am using nsepy but it is not working to fetch indices data to manipulate for our reqirement.
what i have to do.

5. kalyan says:

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

1. Swapnil says:

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’)

6. Swapnil says:

Hello all,
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

7. Himanshu Arora says:

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)

1. Kshitiz Sharma says:

I am getting same error, did you find the solution ?

8. Himanshu Arora says:

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()

url = "https://www.nseindia.com/content/indices/ind_nifty50list.csv&quot;
csvfromurl = req.get(url).content
nifty50df = pd.DataFrame(csvfordf)

9. Himanshu Arora says:

#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)

10. Himanshu Arora says:

Getting above two errors when fetching data

11. Darsh says:

Is there anyone who is still using NSEpy ?

I m newbie with python but willing to learn.

12. Akshay says:

Hi

How do i get live data using nsepy?

13. Zeeshan says:

Hey hi. Can you help with adjusting historical prices with splits and bonuses

14. hari says:

how to get live data using nsepy