No doubt, this article will require a significant amount of your time to understand how and why the materials described here work. This is because everything that will be shown here is initially oriented toward object-oriented programming, but in fact it is based on the principles of structured programming.
In today's article, we will show how to approach solving problems related to structuring different elements and creating simpler and more attractive solutions. Although the content is oriented toward learning and, therefore, does not constitute production code, it is essential to thoroughly understand the concepts and knowledge that will be covered here. In this way, in the future we will be able to follow the codes we will present.
In this article, we explore practical and robust risk management techniques specifically tailored for liquidity-based trading. You will learn how to protect positions during retests, handle false breakouts with confidence, and identify signs of potential level manipulation. By the end, you will have built an adaptive Expert Advisor capable of managing zone flips and executing strategic pending orders with integrated risk control.
The Fibonacci retracement tool is an essential component of price action analysis, providing critical levels for potential market reactions. However, its effectiveness is often limited by the need for continuous human monitoring, which can lead to missed setups. In this part of our series, we introduce a tool that synchronizes and actively monitors manually drawn Fibonacci levels using MQL5, combining discretionary insight with automated oversight.
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.
This article presents an MQL5 indicator that detects and manages liquidity zone flips. It identifies supply and demand zones from higher timeframes using a base–impulse pattern, applies objective breakout and impulse thresholds, and flips zones automatically when structure changes. The result is a dynamic support‑resistance map that reduces manual redraws and gives you clear, actionable context for signals and retests.
Today, we explore another component of ALGLIB, leveraging its mathematical capabilities to develop a Polynomial Regression Channel indicator. By the end of this discussion, you will gain practical insights into indicator development using the MQL5 Standard Library, along with a fully functional, mathematically driven indicator source code.
This article shows how to represent market structure as a graph in MQL5, turning swing highs/lows into nodes with features and linking them by edges. It trains a Graph Neural Network to score potential liquidity zones, exports the model to ONNX, and runs real-time inference in an Expert Advisor. Readers learn how to build the data pipeline, integrate the model, visualize zones on the chart, and use the signals for rule-based execution.
Monitoring manually drawn trendlines requires constant chart observation, which can cause important price interactions to be missed. This article develops a trendline monitoring Expert Advisor that synchronizes manually drawn trendlines with automated monitoring logic in MQL5, generating alerts when price approaches, touches, or breaks a monitored line.
The alignment of higher-timeframe liquidity structures with lower-timeframe reversal patterns can greatly influence both the likelihood and direction of the next price movement. By integrating structural liquidity zones from higher timeframes with precise reversal confirmations on lower timeframes, traders can improve entry timing and overall trade quality. This article demonstrates how to reinforce liquidity-based trading strategies through higher-timeframe structural confirmation—and how to implement this approach effectively using MQL5.
This article applies Depth-First Search to market structure by modeling swing highs and lows as graph nodes and tracking one structural path as deeply as conditions remain valid. When a key swing is broken, the algorithm backtracks and explores an alternative branch. Readers gain a practical framework to formalize structural bias and test whether the current path aligns with targets like liquidity pools or supply and demand zones.
We have developed a system that enforces a daily trade limit to keep you aligned with your trading rules. It monitors all executed trades across the account and automatically intervenes once the defined limit is reached, preventing any further activity. By embedding control directly into the platform, the system ensures discipline is maintained even when market pressure rises.
During sideways price movements, traders face excessive signals from multiple moving average crossovers. Today, we discuss how ALGLIB preprocesses raw price data to produce filtered crossover layers, which can also generate alerts when they occur. Join this discussion to learn how a mathematical library can be leveraged in MQL5 programs.
In this part, we will integrate a real-time correlation matrix into a multi-symbol Expert Advisor to prevent redundant or risk-stacked trades. By dynamically measuring cross-pair relationships, the EA will filter entries that conflict with existing exposure, improving portfolio balance, reducing systemic risk, and enhancing overall trade quality.
In this part of the Price Action Analysis Toolkit Development series, we develop an MQL5 indicator that automatically detects rising and falling wedge patterns in real time. The system confirms pivot structures, validates boundary convergence mathematically, prevents overlapping formations, and monitors breakout and failure conditions with precise visual feedback. Built using a clean object-oriented architecture, this implementation converts subjective wedge recognition into a structured, state-aware analytical component designed to strengthen disciplined price action analysis.
Let's discuss how we can make our Expert Advisors speech‑capable using text‑to‑speech technology, partnering Python and MQL5. After reading this article, you will walk away with a working example of an EA that speaks dynamic market information. You will master the application of TTS, the WebRequest function, and learn how Python libraries integrate with the MQL5 language to create a truly voice‑aware trading tool.
This article presents an adaptive parallel channel detection and breakout system in MQL5. It explains how swing points are identified, channels are constructed and dynamically recalculated, and breakouts are confirmed and visualized with persistent signals. The framework integrates trendline geometry, ATR-based filtering, and retest validation to provide reliable, real-time price action analysis for professional charting and trading decisions.
Built on lower-timeframe market structure, and then orchestrated on the higher-timeframe, this indicator detects swing extremes where price becomes statistically vulnerable to reversal. It visualizes overextension and pullback zones, offering early insight into mean-reversion behavior.
This article discusses the application of a breakeven mechanism in automated strategies using the MQL5 language. We will start with a simple explanation of what the breakeven mode is, how it is implemented, and its possible variations. Next, this functionality will be integrated into the Order Blocks expert advisor, which we created in our last article on risk management. To evaluate its effectiveness, we will run two backtests under specific conditions: one using the breakeven mechanism and the other without it.
The article extends a liquidity-based strategy with a simple trend constraint: trade liquidity zones only in the direction of the EMA(50). It explains filtering rules, presents a reusable TrendFilter.mqh class and EA integration in MQL5, and compares baseline versus filtered tests. Readers gain a clear directional bias, reduced overtrading in countertrend phases, and ready-to-use source files.
In this discussion we will Automate Swing Extremes and the Pullback Indicator, which transforms raw lower-timeframe (LTF) price action into a structured map of market intent, precisely identifying swing highs, swing lows, and corrective phases in real time. By programmatically tracking microstructure shifts, it anticipates potential reversals before they fully unfold—turning noise into actionable insight.
In the latest installment of this series, we move beyond individual machine learning techniques to address the "Research Chaos" that plagues many quantitative traders. This article focuses on the transition from ad-hoc notebook experiments to a principled, production-grade pipeline that ensures reproducibility, traceability, and efficiency.
Discipline becomes reliable when it is produced by system design, not willpower. Using MQL5, the article implements real-time constraints—trade-frequency caps and daily equity-based stops—that monitor behavior and trigger actions on breach. Readers gain a practical template for governance layers that stabilize execution under market pressure.
We translate the EMA‑50 retest idea into a behavior‑driven Expert Advisor for intraday trading. The study formalizes trend bias, EMA interaction (pierce and close), reaction confirmation, and optional filters, then implements them in MQL5 with modular functions and resource‑safe handles. Visual testing in the Strategy Tester verifies signal correctness. The result is a clear template for coding discretionary bounces.
In this article, we will look at how to solve certain problems and issues that arise when using Python code within other programs. More specifically, we will demonstrate a common issue encountered when using Excel in conjunction with MetaTrader 5, although we will be using Python to facilitate this interaction. However, this implementation has a minor drawback. It does not occur in all cases, but only in certain specific situations. When it does happen, it is necessary to understand the cause. In today’s article, we will begin explaining how to resolve this issue.
In this article, we will implement the risk management system developed in previous publications and add the Order Blocks indicator described in other articles. In addition, we will run a backtest so we can compare results with the risk management system enabled and evaluate the impact of dynamic risk.
This article turns Market Memory Zones from a chart-only concept into a complete MQL5 Expert Advisor. It automates Displacement, Structure Transition (CHoCH), and Liquidity Sweep zones using ATR- and candle-structure filters, applies lower-timeframe confirmation, and enforces risk-based position sizing with dynamic SL and structure-based TP. You will get the code architecture for detection, entries, trade management, and visualization, plus a brief backtest review.
This article shows how to build rounded speech bubbles in MQL5 by combining a rounded rectangle with a pointer triangle and controlling orientation (up, down, left, right). It details geometry precomputation, supersampled filling, rounded apex arcs, and segmented borders with an extension ratio for seamless joins. Readers get configurable code for size, radii, colors, opacity, and thickness, ready for alerts or tooltips in trading interfaces.
We present a slanted trendline breakout tool that relies on three‑swing validation to generate objective, price‑action signals. The system automates swing detection, trendline construction, and breakout confirmation using crossing logic to reduce noise and standardize execution. The article explains the strategy rules, shows the MQL5 implementation, and reviews testing results; the tool is intended for analysis and signal confirmation, not automated trading.
This is Part 4 of our series on risk management in MQL5, where we continue exploring advanced methods for protecting and optimizing trading strategies. Having laid important foundations in earlier articles, we will now focus on completing all remaining methods postponed in Part 3, including functions for checking whether specific profit or loss levels have been reached. In addition, we will introduce new key events that enable more accurate and flexible risk management.
In this article, we will explore how to create so-called structural code, where the entire context and methods for manipulating variables and information are placed within a structure to create a suitable context for implementing any code. Therefore, we will examine the necessity of using a private section of the code to separate what is public from what is not, thereby adhering to the rule of encapsulation and preserving the context for which the data structure was created.
In this article, we will explore how to approach the implementation of a common structural code base. The goal is to reduce the programming workload and leverage the full potential of the programming language itself—in this case, MQL5.
In this article, we will explore how to overload structural code. I know it can be quite challenging to understand at first, especially if you're seeing it for the first time. It is very important that you grasp these concepts and understand them well before attempting to delve into more complex and elaborate topics.
We are beginning to implement the connection between Excel and MetaTrader 5, but first we need to understand some key points. This way, you won't have to rack your brains trying to figure out why something works or doesn't. And before you frown at the prospect of integrating Python and Excel, let's see how we can (to some extent) control MetaTrader 5 through Excel using xlwings. What we demonstrate here will primarily focus on educational objectives. However, don't think that we can only do what will be covered here.
Many programmers might assume we should abandon using Excel and move directly to Python, using some packages that allow Python to generate an Excel file for later analysis of results. However, as mentioned in the previous article, although this solution is the simplest for many programmers, it will not be accepted by some users. And in this particular case, the user is always right. As programmers, we must find a way to make everything work.
In this article, we will explore what structured code is. Many people confuse structured code with organized code, but there is a difference between these two concepts. This is exactly what will be discussed in this article. Despite the apparent complexity you may feel when first encountering this type of code writing, I have tried to approach the topic as simply as possible. However, this article is just the first step toward something greater.
The article extends our copula toolkit with mixed copulas implemented natively in MQL5. We construct Clayton–Frank–Gumbel and Clayton–Student–t–Gumbel mixtures, estimate them via EM, and enable sparsity control through SCAD with cross‑validation. Provided scripts tune hyperparameters, compare mixtures using information criteria, and save trained models. Practitioners can apply these components to capture asymmetric tail dependence and embed the selected model in indicators or Expert Advisors.
We present a rule-based approach to trendlines that avoids indicator pivots and uses ordered swings derived from raw prices. The article walks through swing detection, size qualification via ATR or fixed thresholds, and validation of ascending and descending structures, then implements these rules in MQL5 with non-repainting drawing and selective output. You get a clear, repeatable way to track structural support and resistance that holds up across market conditions.
The article presents a complete Python–MQL5 integration for multi‑agent trading: MT5 data ingestion, indicator computation, per‑agent decisions, and a weighted consensus that outputs a single action. Signals are stored to JSON, served by Flask, and consumed by an MQL5 Expert Advisor for execution with position sizing and ATR‑derived SL/TP. Flask routes provide safe lifecycle control and status monitoring.
In this article, we will explore how to easily create and implement an operational approach for coloring candles. This concept is highly valued by traders. When implementing such things, care must be taken to ensure that the bars or candles retain their original appearance and do not hinder reading candle by candle.
