Add a new article
From Novice to Expert: Statistical Validation of Supply and Demand Zones
From Novice to Expert: Statistical Validation of Supply and Demand Zones
Today, we uncover the often overlooked statistical foundation behind supply and demand trading strategies. By combining MQL5 with Python through a Jupyter Notebook workflow, we conduct a structured, data-driven investigation aimed at transforming visual market assumptions into measurable insights. This article covers the complete research process, including data collection, Python-based statistical analysis, algorithm design, testing, and final conclusions. To explore the methodology and findings in detail, read the full article.
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (IV): Trade Management Panel class
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (IV): Trade Management Panel class
This discussion covers the updated TradeManagementPanel in our New_Admin_Panel EA. The update enhances the panel by using built-in classes to offer a user-friendly trade management interface. It includes trading buttons for opening positions and controls for managing existing trades and pending orders. A key feature is the integrated risk management that allows setting stop loss and take profit values directly in the interface. This update improves code organization for large programs and simplifies access to order management tools, which are often complex in the terminal.
Integrating External Applications with MQL5 Community OAuth
Integrating External Applications with MQL5 Community OAuth
Learn how to add “Sign in with MQL5” to your Android app using the OAuth 2.0 authorization code flow. The guide covers app registration, endpoints, redirect URI, Custom Tabs, deep-link handling, and a PHP backend that exchanges the code for an access token over HTTPS. You will authenticate real MQL5 users and access profile data such as rank and reputation.
Blood inheritance optimization (BIO)
Blood inheritance optimization (BIO)
I present to you my new population optimization algorithm - Blood Inheritance Optimization (BIO), inspired by the human blood group inheritance system. In this algorithm, each solution has its own "blood type" that determines the way it evolves. Just as in nature where a child's blood type is inherited according to specific rules, in BIO new solutions acquire their characteristics through a system of inheritance and mutations.
Market Simulation (Part 06): Transferring Information from MetaTrader 5 to Excel
Market Simulation (Part 06): Transferring Information from MetaTrader 5 to Excel
Many people, especially non=programmers, find it very difficult to transfer information between MetaTrader 5 and other programs. One such program is Excel. Many use Excel as a way to manage and maintain their risk control. It is an excellent program and easy to learn, even for those who are not VBA programmers. Here we will look at how to establish a connection between MetaTrader 5 and Excel (a very simple method).
Integrating MQL5 with Data Processing Packages (Part 7): Building Multi-Agent Environments for Cross-Symbol Collaboration
Integrating MQL5 with Data Processing Packages (Part 7): Building Multi-Agent Environments for Cross-Symbol Collaboration
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.
Algorithmic Trading Strategies: AI and Its Road to Golden Pinnacles
Algorithmic Trading Strategies: AI and Its Road to Golden Pinnacles
This article demonstrates an approach to creating trading strategies for gold using machine learning. Considering the proposed approach to the analysis and forecasting of time series from different angles, it is possible to determine its advantages and disadvantages in comparison with other ways of creating trading systems which are based solely on the analysis and forecasting of financial time series.
Market Simulation (Part 12): Sockets (VI)
Market Simulation (Part 12): Sockets (VI)
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.
Market Simulation (Part 13): Sockets (VII)
Market Simulation (Part 13): Sockets (VII)
When we develop something in xlwings or any other package that allows reading and writing directly to Excel, we must note that all programs, functions, or procedures execute and then complete their task. They do not remain in a loop, no matter how hard we try to do things differently.
Market Simulation (Part 15): Sockets (IX)
Market Simulation (Part 15): Sockets (IX)
In this article, we will discuss one of the possible solutions to what we have been trying to demonstrate—namely, how to allow an Excel user to perform an action in MetaTrader 5 without sending orders or opening or closing positions. The idea is that the user employs Excel to conduct fundamental analysis of a particular symbol. And by using only Excel, they can instruct an expert advisor running in MetaTrader 5 to open or close a specific position.
Market Simulation: (Part 11): Sockets (V)
Market Simulation: (Part 11): Sockets (V)
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.
Using the MQL5 Economic Calendar for News Filtering (Part 1): Implementing Pre- and Post-News Windows in MQL5
Using the MQL5 Economic Calendar for News Filtering (Part 1): Implementing Pre- and Post-News Windows in MQL5
We build a calendar‑driven news filter entirely in MQL5, avoiding web requests and external DLLs. Part 1 covers loading and caching events, mapping them to symbols by currency, filtering by impact level, defining pre/post windows, and blocking new trades during active news, with optional pre‑news position closure. The result is a configurable, prop‑firm‑friendly control that reduces false pauses and protects entries during volatility.
MetaTrader 5 Machine Learning Blueprint (Part 6): Engineering a Production-Grade Caching System
MetaTrader 5 Machine Learning Blueprint (Part 6): Engineering a Production-Grade Caching System
Tired of watching progress bars instead of testing trading strategies? Traditional caching fails financial ML, leaving you with lost computations and frustrating restarts. We've engineered a sophisticated caching architecture that understands the unique challenges of financial data—temporal dependencies, complex data structures, and the constant threat of look-ahead bias. Our three-layer system delivers dramatic speed improvements while automatically invalidating stale results and preventing costly data leaks. Stop waiting for computations and start iterating at the pace the markets demand.
The MQL5 Standard Library Explorer (Part 3): Expert Standard Deviation Channel
The MQL5 Standard Library Explorer (Part 3): Expert Standard Deviation Channel
In this discussion, we will develop an Expert Advisor using the CTrade and CChartObjectStdDevChannel classes, while applying several filters to enhance profitability. This stage puts our previous discussion into practical application. Additionally, I’ll introduce another simple approach to help you better understand the MQL5 Standard Library and its underlying codebase. Join the discussion to explore these concepts in action.
Overcoming Accessibility Challenges in MQL5 Trading Tools (Part II): Enabling EA Voice Using a Python Text-to-Speech Engine
Overcoming Accessibility Challenges in MQL5 Trading Tools (Part II): Enabling EA Voice Using a Python Text-to-Speech Engine
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.
Package-based approach with KnitPkg for MQL5 development
Package-based approach with KnitPkg for MQL5 development
For maximum reliability and productivity in MetaTrader products built with MQL, this article advocates a development approach based on reusable “packages” managed by KnitPkg, a project manager for MQL5/MQL4. A package can be used as a building block for other packages or as the foundation for final artifacts that run directly on the MetaTrader platform, such as EAs, indicators, and more.
Graph Theory: Traversal Depth-First Search (DFS) Applied in Trading
Graph Theory: Traversal Depth-First Search (DFS) Applied in Trading
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.
Risk Management (Part 3): Building the Main Class for Risk Management
Risk Management (Part 3): Building the Main Class for Risk Management
In this article, we will begin creating a core risk management class that will be key to controlling risks in the system. We will focus on building the foundations, defining the basic structures, variables and functions. In addition, we will implement the necessary methods for setting maximum profit and loss values, thereby laying the foundation for risk management.
From Novice to Expert: Time Filtered Trading
From Novice to Expert: Time Filtered Trading
Just because ticks are constantly flowing in doesn’t mean every moment is an opportunity to trade. Today, we take an in-depth study into the art of timing—focusing on developing a time isolation algorithm to help traders identify and trade within their most favorable market windows. Cultivating this discipline allows retail traders to synchronize more closely with institutional timing, where precision and patience often define success. Join this discussion as we explore the science of timing and selective trading through the analytical capabilities of MQL5.
Integrating MQL5 with Data Processing Packages (Part 8): Using Graph Neural Networks for Liquidity Zone Recognition
Integrating MQL5 with Data Processing Packages (Part 8): Using Graph Neural Networks for Liquidity Zone Recognition
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.
The MQL5 Standard Library Explorer (Part 10): Polynomial Regression Channel
The MQL5 Standard Library Explorer (Part 10): Polynomial Regression Channel
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.
Neuro-Structural Trading Engine — NSTE (Part I): How to Build a Prop-Firm-Safe Multi-Account System
Neuro-Structural Trading Engine — NSTE (Part I): How to Build a Prop-Firm-Safe Multi-Account System
This article lays the system architecture for a multi‑account algorithmic trading setup that operates cryptocurrency CFDs on MetaTrader 5 while respecting prop‑firm constraints. It defines three core principles—fixed dollar risk, one script per account, and centralized configuration—then details the Python–MQL5 split, the 60‑second processing loop, and JSON-based signaling. Readers get practical lot‑size computation, safety checks, and position management patterns for reliable deployment.
MetaTrader 5 Machine Learning Blueprint (Part 8): Bayesian Hyperparameter Optimization with Purged Cross-Validation and Trial Pruning
MetaTrader 5 Machine Learning Blueprint (Part 8): Bayesian Hyperparameter Optimization with Purged Cross-Validation and Trial Pruning
GridSearchCV and RandomizedSearchCV share a fundamental limitation in financial ML: each trial is independent, so search quality does not improve with additional compute. This article integrates Optuna — using the Tree-structured Parzen Estimator — with PurgedKFold cross-validation, HyperbandPruner early stopping, and a dual-weight convention that separates training weights from evaluation weights. The result is a five-component system: an objective function with fold-level pruning, a suggestion layer that optimizes the weighting scheme jointly with model hyperparameters, a financially-calibrated pruner, a resumable SQLite-backed orchestrator, and a converter to scikit-learn cv_results_ format. The article also establishes the boundary — drawn from Timothy Masters — between statistical objectives where directed search is beneficial and financial objectives where it is harmful.
From Novice to Expert: Forex Market Periods
From Novice to Expert: Forex Market Periods
Every market period has a beginning and an end, each closing with a price that defines its sentiment—much like any candlestick session. Understanding these reference points allows us to gauge the prevailing market mood, revealing whether bullish or bearish forces are in control. In this discussion, we take an important step forward by developing a new feature within the Market Periods Synchronizer—one that visualizes Forex market sessions to support more informed trading decisions. This tool can be especially powerful for identifying, in real time, which side—bulls or bears—dominates the session. Let’s explore this concept and uncover the insights it offers.
From Novice to Expert: Detecting Liquidity Zone Flips Using MQL5
From Novice to Expert: Detecting Liquidity Zone Flips Using MQL5
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.
Coral Reefs Optimization (CRO)
Coral Reefs Optimization (CRO)
The article presents a comprehensive analysis of the Coral Reef Optimization (CRO) algorithm, a metaheuristic method inspired by the biological processes of coral reef formation and development. The algorithm models key aspects of coral evolution: broadcast spawning, brooding, larval settlement, asexual reproduction, and competition for limited reef space. Particular attention is paid to the improved version of the algorithm.
From Novice to Expert: Adaptive Risk Management for Liquidity Strategies
From Novice to Expert: Adaptive Risk Management for Liquidity Strategies
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.
Can DOOM Run in MetaTrader 5: DLLs, Rendering, and MQL5 Input?
Can DOOM Run in MetaTrader 5: DLLs, Rendering, and MQL5 Input?
This article demonstrates how to run DOOM inside MetaTrader 5 by integrating a native Windows DLL with an MQL5 Expert Advisor. We cover building the DLL, real-time framebuffer rendering via ResourceCreate, keyboard input with a key-up workaround using GetAsyncKeyState, and running the game loop on a background thread. The techniques are directly applicable to custom visualization, external data bridges, and robust MQL5–native code integration.