Discussing the article: "Developing Market Entropy Indicator: Trading System Based on Information Theory"
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Check out the new article: Developing Market Entropy Indicator: Trading System Based on Information Theory.
This article explores the development of a Market Entropy Indicator based on principles from Information Theory to measure the uncertainty and information content within financial markets. By applying concepts such as Shannon Entropy to price movements, the indicator quantifies whether the market is structured (trending), transitioning, or chaotic.
Traders and algorithm developers constantly face a critical question: is the market currently structured and suitable for trend-following strategies, or is it in a transitional or chaotic state where conventional indicators generate mostly noise? While most tools focus on price and its derivatives, they rarely quantify the degree of randomness in price action. As a result, many systems apply the same logic across all market conditions, leading to false entries, poor signal quality, and inconsistent performance in unpredictable environments.
To address this, we introduce a framework that formalizes market randomness using Shannon entropy applied to discretized price states (up, down, flat). The Market Entropy Indicator computes a normalized entropy score over a rolling window and maps it into distinct market regimes—TREND, TRANSITION, and CHAOTIC—using clearly defined thresholds. We augment this with fast/slow entropy horizons, entropy momentum, divergence, and compression/decompression detection so the informational state is both measurable and implementable in MQL5 as buffers, regime flags, visual markers, and rule-based signal filters.
Author: Hlomohang John Borotho