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


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In the previous part, Lamarcus discussed importing Python libraries numpy as np
and pandas as pd. Follow the series with today’s article, which will focus on how to build a heatmap

Now that we have our libraries, let’s get our data.

#setting start and end dates
#importing Walmart and Target using pandas datareader

Before testing our two stocks for cointegration, let’s take a look at their performance over the period. We’ll create a plot of Walmart* and Target*.

#Creating a figure to plot on plt.figure(figsize=(10,8))
#Creating WMT and TGT plots
plt.title(‘Walmart and Target Over 2014-2016’)

In the above plot, we can see a slight correlation at the beginning of 2014. But this doesn’t really give us a clear idea of the relationship between Walmart and Target. To get a definitive idea of the relationship between the two stocks, we’ll create a correlation heat-map.

To begin creating our correlation heatmap, we must first place Walmart* and Target* prices in the same dataframe. Let’s create a new dataframe for our stocks.

#initializing newDF as a pandas dataframe
#adding WMT closing prices as a column to the newDF
#adding TGT closing prices as a column to the newDF

Now that we have created a new dataframe to hold our Walmart and Target stock prices, let’s take a look at it.


We can see that we have the prices of both our stocks in one place. 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.

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