Market Entropy for Regime Detection and Trading Signals

Summary

This article presents an MQL5 indicator that applies normalized Shannon entropy to price changes classified as up, down, or flat. A rolling window estimates how evenly those states occur, with low entropy interpreted as more structured movement and high entropy as greater randomness. It labels conditions as trend, transition, or chaotic and adds fast and slow entropy, momentum, divergence, compression and decompression measures, plus markers for shocks and regime shifts.

The proposed signals combine changes in entropy with fast and slow horizon relationships: rising entropy after compression can support a buy setup, while weakening structure and negative momentum can inform a sell setup. The article describes plots, inputs, and implementation components, but supplies no backtest, out-of-sample results, or evidence that its thresholds predict returns. Entropy measures the distribution of discretized moves, so results depend on the price-step setting, sampling window, instrument, and market; the suggested regimes and signal rules require independent validation.

Key ideas

• The indicator estimates uncertainty by counting up, down, and flat price changes in a rolling window.
• Low and high entropy are mapped to trend-like and chaotic regimes, with an intermediate transition state.
• Fast and slow entropy, momentum, and divergence provide additional context for regime changes.
• Compression followed by rising entropy is proposed as a setup cue, while rising disorder can warn of breakdown.
• The article offers implementation logic but no performance validation for its thresholds or signals.

Tags

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