Spread is not Cointegrated
To view the complete print out of the ADF2 test, we can call adf2.
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.
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
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.
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