Market Intent

Market Intent

The base code for Market Intent was originally published by Hlomohang John Borotho in the MQL5 article “Decoding Market Intent: Reading Structure, Liquidity, and Price Behavior.”

The original concept and source code have since undergone substantial development, refinement, and enhancement to improve their functionality, usability, and practical application. The resulting version presented here has been further developed with the goal of providing a more accessible and ready-to-use implementation for the MQL5 community.

Driven by a commitment to advancing promising ideas and transforming them into more practical and capable trading tools, this enhanced implementation is being made available free of charge, with the intention of continuing its development and introducing further improvements over time.

We sincerely acknowledge and express our appreciation to Hlomohang John Borotho for the original concept, research, and code that provided the foundation for this development. His contribution to the MQL5 community is greatly appreciated.

For a deeper understanding of the underlying Market Intent concept, methodology, logic, and implementation, users are encouraged to refer to the original article:

“Decoding Market Intent: Reading Structure, Liquidity, and Price Behavior”

The original article provides the detailed theoretical background and implementation guidance necessary to better understand the technology and principles behind this Market Intent code.

In brief :

The engine is built as a pipeline, and each stage answers exactly one question. Stage one asks where the market is in structural terms. Stage two asks where orders are likely clustered. Stage three asks what price actually did when it got there. Stage four asks what all of that implies. Stage five asks what we should do about it. Data flows one way, and no stage is allowed to reach back and rewrite an earlier one. This is deliberate. A great many discretionary systems fail because the trader decides on a direction first and then interprets structure to support it. A pipeline cannot do that.

ontext is handled by running stage one on four timeframes at once. A macro timeframe sets the broad bias, a primary timeframe carries the structure we trade with, a setup timeframe hosts the liquidity work, and an entry timeframe supplies confirmation. The defaults are H4, H1, M15, and M5, and all four are inputs. This is what stops the system from taking every pattern it finds. The same M15 sweep means one thing when H4 and H1 agree with it and something quite different when they do not, and the score reflects that difference automatically rather than through a handwritten exception.

The output is deliberately compressed. Rather than fifty objects, we show the handful that carry information, and we back them with a dashboard that states the case in words. The dashboard reports each timeframe's trend and whether liquidity has been swept (and on which side). It also shows the last displacement strength, FVG state, last structural break, component gauges, the total score, the decision state, and the current trade plan with its reward-to-risk ratio.

Please understand that if automated trading is enabled, you bear full responsibility for all associated terms and conditions; I am merely providing the advanced indicator developed as previously described, and by proceeding, you acknowledge and accept this.