Discussing the article: "From Novice to Expert: Demystifying Hidden Fibonacci Retracement Levels"

[MetaQuotes]

Check out the new article: From Novice to Expert: Demystifying Hidden Fibonacci Retracement Levels.

In this article, we explore a data-driven approach to discovering and validating non-standard Fibonacci retracement levels that markets may respect. We present a complete workflow tailored for implementation in MQL5, beginning with data collection and bar or swing detection, and extending through clustering, statistical hypothesis testing, backtesting, and integration into an MetaTrader 5 Fibonacci tool. The goal is to create a reproducible pipeline that transforms anecdotal observations into statistically defensible trading signals.

Fibonacci retracement levels are widely used but, sometimes price reacts to intermediate or repeated non-standard ratios. Our question is, can we use systematic, data-driven methods to discover such levels, test whether they occur more often than once, and, if robust, add them as first-class levels in our trading tools and strategies?

Classical Fibonacci ratios such as 23.6%, 38.2%, 50%, 61.8%, and 78.6% are derived from the Fibonacci sequence and the golden ratio. While widely accepted, traders often notice that markets sometimes respect intermediate or alternative retracement levels not included in this traditional set. This suggests the standard framework may not fully capture market behavior.

Visual pattern recognition is prone to confirmation bias: we remember the times when price reacted at a suspected level but forget the misses. Without systematic testing, these hidden levels remain speculative. Still, anecdotal evidence gives us a starting point to test these ideas in a structured way and bring more accuracy to the theory traders rely on. The challenge is to distinguish genuine structural tendencies from randomness and noise.

Author: Clemence Benjamin