Discussing the article: "Data Science and ML (Part 40): Using Fibonacci Retracements in Machine Learning data"
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Check out the new article: Data Science and ML (Part 40): Using Fibonacci Retracements in Machine Learning data.
Fibonacci retracements are a popular tool in technical analysis, helping traders identify potential reversal zones. In this article, we’ll explore how these retracement levels can be transformed into target variables for machine learning models to help them understand the market better using this powerful tool.
Fibonacci numbers can be traced back to the ancient mathematician Leonardo of Pisa, also known as Fibonacci.
In his book named "Liber Abaci", published in 1202, Fibonacci introduced the sequence of numbers now known as the Fibonacci sequence. The sequence that starts with 0 and 1, and each subsequent number in the series is the sum of the two preceding numbers.
This sequence is powerful as it appears in many natural phenomena, including the growth patterns of plants and animals.
In biology, while not perfect, the logarithmic spiral seen in some animal and insect shells approximates the Fibonacci numbers.
Fibonacci-like growth assumption can also be spotted in the rabbit population and bee family trees.
Fibonacci numbers can also be spotted in the DNA makeup of some mammals and human beings.
These numbers are universal, as they have been spotted just about everywhere. Below are some of the common terms you'll come across when working with Fibonacci numbers.
Author: Omega J Msigwa