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K-Means Clustering For Pair Selection In Python - Heatmaps and ADF Tests

K-Means Clustering For Pair Selection In Python – Heatmaps and ADF Tests

In the previous post we created a dataframe to hold our Walmart* and Target* stock prices.

We are now ready to create a correlation heatmap of our stocks. To this, we will use python’s Seaborn library. Recall that we imported Seaborn earlier as sns.

#using seaborn as sns to create a correlation heatmap of WMT and TGT


In the above plot, we called the corr() method on our newDF and passed it into Seaborn’s heatmap object. From this visualization, we can see that our two stocks are not that correlated. Let’s create a final visualization to asses this relationship. We’ll use a scatter plot for this. Bottom of Form

Earlier we used Matplotlibs scatter plot method. So now we’ll introduce Seaborn’s scatter plot method. Note that Seaborn is built on top of Matplotlib and thus matplotlibs functionality can be applied to Seaborn.

#Creating a scatter plot using Seaborn

Seaborn scatter plot

One feature that I like about using Seaborn’s scatter plot is that it provides the Correlation Coefficient and P-Value. From looking at this pearsonr value, we can see that WMT and TGT were not positively correlated over the period. Now that we have a better understanding of our two stocks, let’s check to see if a tradable relationship exists.

We’ll use the Augmented Dickey Fuller Test to determine of our stocks can be traded within a Statistical Arbitrage Strategy. Recall that we imported the adfuller test from the statsmodels.tsa.api package earlier.

To perform the ADF test, we must first create the spread of our stocks. We add this to our existing newDF dataframe.

#adding the spread column to the nemDF dataframe

#instantiating the adfuller test

We have now performed the ADF test on our spread and need to determine whether or not our stocks are cointegrated. Let’s write some logic to determine the results of our test.

#Logic that states if our test statistic is less than
#a specific critical value, then the pair is cointegrated at that
#level, else the pair is not cointegrated

if adf[0] < adf[4]['1%']:

              print('Spread is Cointegrated at 1% Significance Level')

elif adf[0] < adf[4]['5%']:

              print('Spread is Cointegrated at 5% Significance Level')

elif adf[0] < adf[4]['10%']:

              print('Spread is Cointegrated at 10% Significance Level')


              print('Spread is not Cointegrated')

Spread is not Cointegrated

The results of the Augmented Dickey Fuller Test showed that Walmart and Target were not cointegrated. This is determined by a test statistic that is not less than one of the critical values. If you would like to view the actual print out of the ADF test you can do so by keying ADF. In the above example, we use indexing to decipher between the t-statistic and critical values. The statsmodels ADF Test provides you with other useful information such as the p-value. You can learn more about the ADF test here

#printing out the results of the adf test






{'1%': -3.4434175660489905,

'10%': -2.5698395516760275,

'5%': -2.8673031724657454},


In the next post we will look at historic problem of pair selection.



*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.

If you want to learn more about K-Means Clustering for Pair Selection in Python, or to download the code, visit QuantInsti website and the educational offerings at their Executive Programme in Algorithmic Trading (EPAT™).

This article is from QuantInsti and is being posted with QuantInsti’s permission. The views expressed in this article are solely those of the author and/or QuantInsti and IB is not endorsing or recommending any investment or trading discussed in the article. This material is for information only and 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 by IB 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.



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