Stock Clusters Using K-Means Algorithm in Python

For this post, I will be creating a script to download pricing data for the S&P 500 stocks, calculate their historic returns and volatility and then proceed to use the K-Means clustering algorithm to divide the stocks into distinct groups based upon said returns and volatilities.

So why would we want to do this you ask? Well dividing stocks into groups with “similar characteristics” can help in portfolio construction to ensure we choose a universe of stocks with sufficient diversification between them.

The concept behind K-Means clustering is explained here far more succinctly than I ever could, so please visit that link for more details on the concept and algorithm

I’ll deal instead with the actual Python code needed to carry out the necessary data collection, manipulation and analysis.

First things first, we need to collect the data – lets run our imports and create a simple data download script that scrapes the web to collect the tickers for all the individual stocks within the S&P 500.

This gets up something resembling the following:

We can now start to analyse the data and begin our K-Means investigation…

Our first decision is to choose how many clusters do we actually want to separate the data into. Rather than make some arbitrary decision we can use an “Elbow Curve” to highlight the relationship between how many clusters we choose, and the Sum of Squared Errors (SSE) resulting from using that number of clusters.

We then plot this relationship to help us identify the optimal number of clusters to use – we would prefer a lower number of clusters, but also would prefer the SSE to be lower – so this trade off needs to be taken into account.

Lets run the code for our Elbow Curve plot.

The resulting plot with the above data is as follows:

So we can sort of see that once the number of clusters reaches 5 (on the bottom axis), the reduction in the SSE begins to slow down for each increase in cluster number. This would lead me to believe that the optimal number of clusters for this exercise lies around the 5 mark – so let’s use 5.

This gives us the output:

Ok, so it looks like we have an outlier in the data which is skewing the results and making it difficult to actually see what is going on for all the other stocks. Let’s take the easy route and just delete the outlier from our data set and run this again.

Returns BHF
Volatility BHF
dtype: object

Ok so let’s drop the stock ‘BHF and recreate the necessary data arrays.

So now running the following piece of code:

gets us a much clearer visual representation of the clusters as follows:

Finally to get the details of which stock is actually in which cluster we can run the following line of code to carry out a list comprehension to create a list of tuples in the (Stock Name, Cluster Number) format:

This will print out something resembling the below (I havn’t included all the results for brevity)

SO there you have it, we now have a list of each of the stocks in the S&P 500, along with which one of 5 clusters they belong to with the clusters being defined by their return and volatility characteristics. We also have a visual representation of the clusters in chart format.

If anyone has any questions or comments, as always feel free to leave them below.


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Written by s666