Using Volume and Volatility Clusters to Identify Reversal Candidates

Summary

The article proposes identifying possible trend reversals through “yellow” clusters formed from volume and price volatility, their rates of change, and a visualization of market data. It describes normalizing inputs to a 3–9 scale, combining volatility and momentum into a cluster score, and interpreting lower and higher score ranges as developing or mature clusters. It also suggests using the signal against a short-term trend and comparing behavior across timeframes.

The article reports that 97% of detected clusters occurred close to pivot points, while 40% of reversals had a cluster; it also gives claimed accuracy and backtest results for EURUSD. These figures are presented without enough methodological detail to assess labeling, signal timing, costs, or out-of-sample robustness. The reported 100% profitable trades and unusually smooth equity curve warrant particular scrutiny. The piece is best treated as an exploratory hypothesis and implementation sketch, not evidence that the method reliably predicts reversals.

Key ideas

• The proposed cluster score combines price and volume volatility with the absolute rates of change in both series.
• The article maps normalized values to low, developing, and mature cluster ranges.
• It suggests interpreting a detected cluster as a possible reversal against the prevailing trend.
• The reported cluster statistics and backtest lack enough methodological detail to establish predictive performance.
• The author proposes further testing across instruments and time periods.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.