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

###### Posted October 10, 2019 at 11:15 am
Lamarcus Coleman
QuantInsti

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
start=’2014-01-01′
end=’2916-01-01′
#importing Walmart and Target using pandas datareader
wmt=pdr.get_data_yahoo(‘WMT’,start,end)
tgt=pdr.get_data_yahoo(‘TGT’,start,end)

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.plot(wmt[“Close”],label=’Walmart’)
plt.plot(tgt[‘Close’],label=’Target’)
plt.title(‘Walmart and Target Over 2014-2016’)
plt.legend(loc=0)
plt.show()

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
newDF=pd.DataFrame()
#adding WMT closing prices as a column to the newDF
newDF[‘WMT’]=wmt[‘Close’]
#adding TGT closing prices as a column to the newDF
newDF[‘TGT’]=tgt[‘Close’]

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
sns.heatmap(newDF.corr())

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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