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

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

Posted November 19, 2019 at 9:57 am
Lamarcus Coleman
QuantInsti

See the prior installments in this series here. Part I, Part II, Part III, Part IVPart V and and Part VI.

Spread is not Cointegrated

To view the complete print out of the ADF2 test, we can call adf2.

adf2
(-1.9620694402101162,
0.30344784824995258,
1,
502,
{‘1%': -3.4434437319767452,
‘10%': -2.5698456884811351,
‘5%': -2.8673146875484368},
1305.4559226426163)

How about we take a breather here and review what we have learned so far. In this section, we began our journey toward understanding the efficacy of K-Means for pair selection and Statistical Arbitrage by attempting to develop a Statistical Arbitrage strategy in a world with no K-Means.

We learned that in a Statistical Arbitrage trading world without K-Means, we are left to our own devices for solving the historic problem of pair selection. We've learned that despite two stocks being related on a fundamental level, this doesn't necessarily insinuate that they will provide a tradable relationship.

Understanding K-Means

Before we start implementing the K-means clustering algorithm for statistical arbitrage, let's take a look at how K-Means works.

We will begin by importing our usual data analysis and manipulation libraries. Sci-kit learn offers built-in datasets that you can play with to get familiar with various algorithms. You can take a look at some of the datasets provided by sklearn here.

To gain an understanding of how K-Means works, we're going to create our own toy data and visualize the clusters. Then we will use sklearn's K-Means algorithm to assess its ability to identify the clusters that we created. Let’s get started!

#importing necessary libraries
#data analysis and manipulation libraries
import numpy as np
import pandas as pd
#visualization libraries
import matplotlib.pyplot as plt
import seaborn as sns
#machine learning libraries
#the below line is far making fake data far illustration purposes
from sklearn.datasets import make_blobs

Stay tuned -for the next installment in this series. Lamarcus will create the data to begin the analysis.

Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.

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