This website uses cookies to collect usage information in order to offer a better browsing experience. By browsing this site or by clicking on the "ACCEPT COOKIES" button you accept our Cookie Policy.

K-Means Clustering Algorithm For Pair Selection In Python – Part VI

QuantInsti

Contributor:
QuantInsti
Visit: QuantInsti

See the prior installments in this series here. Part I, Part II, Part III, Part IV and Part V.

Now that we’ve gotten our data, let’s add these stocks to our newDF and create their spread.

#adding dltr and dg to our newDF dataframe
newDF[‘DLTR’]=dltr[‘Close’]
newDF[‘DG’]=dg[‘Close’]
#creating the dltr and dg spread as a column in our newDF dataframe
newDF[‘Spread_2’]=newDF[‘DLTR’]-newDF[‘DG’]

We’ve now added the DLTR and DG stocks as well as their spread to our newDF dataframe. Let’s take a quick look at our dataframe.

newDF.head()

Now that we have Spread_2 or the spread of DLTR and DG, we can create ADF2 or a second ADF test for these two stocks.

#Creating another adfuller instance
adf2=adfuller(newDF[‘Spread_2’])

We’ve just run the ADF test on our DLTR and DG spread. We can now repeat our earlier logic to determine if the spread yields a tradable relationship.

if adf2[0] < adf2[4][‘1%’]:
print(‘Spread is Cointegrated at 1% Significance Level’)
elif adf2[0] < adf2[4][‘5%’]:
print(‘Spread is Cointegrated at 5% Significance Level’)
elif adf2[0] < adf2[4][‘10%’]:
print(‘Spread is Cointegrated at 10% Significance Level’)
else:
print(‘Spread is not Cointegrated’)

Stay tuned -for the next installment in this series. Lamarcus will demonstrate how to

Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.

Disclaimer: All investments and trading in the stock market involve risk. Any decisions to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.

Disclosure: Interactive Brokers

Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from QuantInsti and is being posted with permission from QuantInsti. The views expressed in this material are solely those of the author and/or QuantInsti and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

trading top