What Is K-Means Clustering?
K-Means Clustering is a type of unsupervised machine learning that groups data on the basis of similarities. In unsupervised machine learning, we only provide the model with features and then it “learns” the associations on its own.
K-Means is one technique for finding subgroups within datasets. One difference in K-Means versus that of other clustering methods is that in K-Means, we have a predetermined amount of clusters.
The algorithm begins by randomly assigning each data point to a specific cluster with no one data point being in any two clusters. It then calculates the centroid, or mean of these points.
The object of the algorithm is to reduce the total within-cluster variation. In other words, we want to place each point into a specific cluster, measure the distances from the centroid of that cluster and then take the squared sum of these to get the total within-cluster variation. Our goal is to reduce this value.
The process of assigning data points and calculating the squared distances is continued until there are no more changes in the components of the clusters, or in other words, we have optimally reduced the in-cluster variation.
In the trading world, if you want to know the importance of K-Means, you don’t have to look further than the implementation of statistical arbitrage.
Statistical Arbitrage is one of the most recognizable quantitative trading strategies. Though several variations exist, the basic premise is that despite two securities being random walks, their relationship is not random, thus yielding a trading opportunity. A key concern of implementing any version of statistical arbitrage is the process of pair selection.
In the next installment, Lamarcus will try to implement statistical arbitrage without using K-Means first.
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