Learn Algorithmic Trading

K-nearest neighbors

K-nearest neighbors (or KNN) is a supervised method. Like the prior methods we saw in this chapter, the goal is to find a function predicting an output, y, from an unseen observation, x. Unlike a lot of other methods (such as linear regression), this method doesn't use any specific assumption about the distribution of the data (it is referred to as a non-parametric classifier).

The KNN algorithm is based on comparing a new observation to the K most similar instances. It can be defined as a distance metric between two data points. One of the most used frequently methods is the Euclidean distance. The following is the derivative:

d(x,y)=(x1−y1)^2+(x2−y2)^2+…+(xn−yn)^2

When we review the documentation of the Python function, ...