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

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

Posted November 12, 2019 at 9:52 am
Lamarcus Coleman
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

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