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Compute Cointegration using NsePy, Pandas Library

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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 best Pair of Stocks (Pair Trading) to go long in one stock and short(Competitive peers) 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

We will be using get_history NSEpy function to fetch the index data from nseindia. However to fetch stock data you need to use get_price_history. Exploring the NSEpy library would give you a broader idea about how to replicate the same for stocks. But the problem with NSEIndia data is that stock data is not adjusted to split/bonus. Will handle that in a different post about how to process the data for split/bonus before analyzing the time series data.

 
Sample IPython Notebook to compute Cointegration below :

References

Quantopian Lecture on Pair Trading
Python Library to get publicly available data on NSE website – NSEpy

Related Readings and Observations

About Rajandran

Rajandran is a Full time trader and founder of Marketcalls & Co-Founder of Traderscafe, trades mostly using discretionary Trading Concepts like Market Profile, Trading sentimental analysis, building timing models, algorithmic trading models. Instructs professional traders, full time traders & aspiring full time traders. Rajandran attended college in the Chennai where he earned a BE in Electronics and Communications. Rajandran has a broad understanding of trading softwares like Amibroker, Ninjatrader, Esignal, Metastock, Motivewave, Market Analyst(Optuma),Metatrader,Tradingivew,Python and understands individual needs of traders and investors utilizing a wide range of methodologies.

Comments

  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

    Reply

  6. kalyan says

    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

    Reply

    • 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’)

      Reply

  7. Swapnil says

    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

    Reply

  8. 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)

    Reply

  9. 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()

    #Dowloading NSE Symbols
    url = "https://www.nseindia.com/content/indices/ind_nifty50list.csv&quot;
    csvfromurl = req.get(url).content
    csvfordf = pd.read_csv(strio.StringIO(csvfromurl.decode('utf-8')))
    nifty50df = pd.DataFrame(csvfordf)

    Reply

  10. 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)

    Reply

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