In this article, we create a fully customizable session-based Opening Range Breakout (ORB) system in MQL5 that lets us set any desired session start time and range duration, automatically calculates the high and low of that opening period, and trades only confirmed breakouts in the direction of the move.
In this article, we continue mastering API and WebRequest in MQL5 by retrieving candlestick data from an external source. We focus on splitting the server response, cleaning the data, and extracting essential elements such as opening time and OHLC values for multiple daily candles, preparing the data for further analysis.
In this session, we will build a sophisticated, multi-signal Expert Advisor using the MQL5 Standard Library. This approach allows us to seamlessly blend built-in signals with our own custom logic, demonstrating how to construct a powerful and flexible trading algorithm. For more, click to read further.
In this article, we implement an adaptive Linear Regression Channel system in MQL5 that automatically calculates the regression line and standard deviation channel over a user-defined period, only activates when the slope exceeds a minimum threshold to confirm a clear trend, and dynamically recreates or extends the channel when the price breaks out by a configurable percentage of channel width.
This article is the first installment in a two-part series designed to impart practical skills and best practices for writing custom indicators in MQL5. Using Heikin Ashi as a working example, the article explores the theory behind Heikin Ashi charts, explains how Heikin Ashi candlesticks are calculated, and demonstrates their application in technical analysis. The centerpiece is a step-by-step guide to developing a fully functional Heikin Ashi indicator from scratch, with clear explanations to help readers understand what to code and why. This foundational knowledge sets the stage for Part Two, where we will build an expert advisor that trades based on Heikin Ashi logic.
In this article, we develop a Change of Character (CHoCH) detection system in MQL5 that identifies swing highs and lows over a user-defined bar length, labels them as HH/LH for highs or LL/HL for lows to determine trend direction, and triggers trades on breaks of these swing points, indicating a potential reversal, and trades the breaks when the structure changes.
Trading without session awareness is like navigating without a compass—you're moving, but not with purpose. Today, we're revolutionizing how traders perceive market timing by transforming ordinary charts into dynamic geographical displays. Using MQL5's powerful visualization capabilities, we'll build a live world map that illuminates active trading sessions in real-time, turning abstract market hours into intuitive visual intelligence. This journey sharpens your trading psychology and reveals professional-grade programming techniques that bridge the gap between complex market structure and practical, actionable insight.
In this article, we create an Inverse Fair Value Gap (IFVG) detection system in MQL5 that identifies bullish/bearish FVGs on recent bars with minimum gap size filtering, tracks their states as normal/mitigated/inverted based on price interactions (mitigation on far-side breaks, retracement on re-entry, inversion on close beyond far side from inside), and ignores overlaps while limiting tracked FVGs.
In this discussion, we introduce a structured, multi-layered defense system designed to pursue aggressive profit targets while minimizing exposure to catastrophic loss. The focus is on blending offensive trading logic with protective safeguards at every level of the trading pipeline. The idea is to engineer an EA that behaves like a “risk-aware predator”—capable of capturing high-value opportunities, but always with layers of insulation that prevent blindness to sudden market stress.
This article introduces the ParaFrac Oscillator and its V2 model as trading tools. It outlines three trading strategies developed using these indicators. Each strategy was tested and optimized to identify their strengths and weaknesses. Comparative analysis highlighted the performance differences between the original and V2 models.
Learn how to build a complete Kagi-based trading Expert Advisor in MQL5, from signal construction to order execution, visual markers, and a three-stage trailing stop. Includes full code, testing results, and a downloadable set file.
This topic explores how to build an Adaptive Smart Money Architecture (ASMA)—an intelligent Expert Advisor that merges Smart Money Concepts (Order Blocks, Break of Structure, Fair Value Gaps) with real-time market sentiment to automatically choose the best trading strategy depending on current market conditions.
In this article, we build a Liquidity Sweep on Break of Structure (BoS) system in MQL5 that detects swing highs/lows over a user-defined length, labels them as HH/HL/LH/LL to identify BOS (HH in uptrend or LL in downtrend), and spots liquidity sweeps when price wicks beyond the swing but closes back inside on a bullish/bearish candle.
In this article, we will explore practical techniques for trading the Relative Strength Index (RSI) oscillator with market structure. Our focus will be on channel price action patterns, how they are typically traded, and how MQL5 can be leveraged to enhance this process. By the end, you will have a rule-based, automated channel-trading system designed to capture trend continuation opportunities with greater precision and consistency.
In this article, we enhance the AI-powered trading system's modularity by separating UI components into a dedicated include file. The system now automates trade execution based on AI-generated signals, parsing JSON responses for BUY/SELL/NONE with entry/SL/TP, visualizing patterns like engulfing or divergences on charts with arrows, lines, and labels, and optional auto-signal checks on new bars.
For many traders, the gap between knowing a risk rule and following it consistently is where accounts go to die. Emotional overrides, revenge trading, and simple oversight can dismantle even the best strategy. Today, we will transform the MetaTrader 5 platform into an unwavering enforcer of your trading rules by developing a Risk Enforcement Expert Advisor. Join this discussion to find out more.
Market rules are continuously evolving, and many once-reliable principles gradually lose their effectiveness. What worked in the past no longer works consistently over time. Today’s discussion focuses on probability ranges and how they can be used to navigate market irregularities. We will leverage MQL5 to develop an algorithm capable of trading effectively even in the choppiest market conditions. Join this discussion to find out more.
Trading strategies may be challenging to improve because we often don’t fully understand what the strategy is doing wrong. In this discussion, we introduce linear system identification, a branch of control theory. Linear feedback systems can learn from data to identify a system’s errors and guide its behavior toward intended outcomes. While these methods may not provide fully interpretable explanations, they are far more valuable than having no control system at all. Let’s explore linear system identification and observe how it may help us as algorithmic traders to maintain control over our trading applications.
In high-probability support and resistance zones, valid entry confirmation signals are always present once the zone has been correctly identified. In this discussion, we build an intelligent MQL5 program that automatically detects entry conditions within these zones. We leverage well-known candlestick patterns alongside native confirmation indicators to validate trade decisions. Click to read further.
News trading often requires managing multiple positions and symbols within a very short time due to heightened volatility. In today’s discussion, we address the challenges of multi-symbol trading by integrating this feature into our News Headline EA. Join us as we explore how algorithmic trading with MQL5 makes multi-symbol trading more efficient and powerful.
Imagine transforming the traditional EA or indicator input properties into a real-time, on-chart control interface. This discussion builds upon our foundational work in the Market Periods Synchronizer indicator, marking a significant evolution in how we visualize and manage higher-timeframe (HTF) market structures. Here, we turn that concept into a fully interactive utility—a dashboard that brings dynamic control and enhanced multi-period price action visualization directly onto the chart. Join us as we explore how this innovation reshapes the way traders interact with their tools.
In this article, we enhance the AI-powered trading system in MQL5 with user interface improvements, including loading animations for request preparation and thinking phases, as well as timing metrics displayed in responses for better feedback. We add response management tools like regenerate buttons to re-query the AI and export options to save the last response to a file, streamlining interaction.
Master the automation of Larry Williams’ short-term swing patterns using MQL5. In this guide, we develop a fully configurable Expert Advisor (EA) that leverages non-random market structures. We’ll cover how to integrate robust risk management and flexible exit logic, providing a solid foundation for systematic strategy development and backtesting.
We continue to implement the approaches proposed by the authors of the FinCon framework. FinCon is a multi-agent system based on Large Language Models (LLMs). Today, we will implement the necessary modules and conduct comprehensive testing of the model on real historical data.
In this series of articles, we look at the challenges faced by algorithmic traders when deploying machine-learning-powered trading strategies. Some challenges within our community remain unseen because they demand deeper technical understanding. Today’s discussion acts as a springboard toward examining the blind spots of cross-validation in machine learning. Although often treated as routine, this step can easily produce misleading or suboptimal results if handled carelessly. This article briefly revisits the essentials of time series cross-validation to prepare us for more in-depth insight into its hidden blind spots.
The MacroHFT framework for high-frequency cryptocurrency trading uses context-aware reinforcement learning and memory to adapt to dynamic market conditions. At the end of this article, we will test the implemented approaches on real historical data to assess their effectiveness.
This article demonstrates how to design and implement a Larry Williams volatility breakout Expert Advisor in MQL5, covering swing-range measurement, entry-level projection, risk-based position sizing, and backtesting on real market data.
This article introduces the basic concepts behind HMAC-SHA256 and API signatures in MQL5, explaining how messages and secret keys are combined to securely authenticate requests. It lays the foundation for signing API calls without exposing sensitive data.
In this article, we develop a canvas-based price dashboard in MQL5 using the CCanvas class to create interactive panels for visualizing recent price graphs and account statistics, with support for background images, fog effects, and gradient fills. The system includes draggable and resizable features via mouse event handling, theme toggling between dark and light modes with dynamic color adjustments, and minimize/maximize controls for efficient chart space management.
An empirical study of Larry Williams’ Trade Day of the Week concept, showing how time-based market bias can be measured, tested, and applied using MQL5. This article presents a practical framework for analyzing win rates and performance across trading days to improve short-term trading systems.
In this discussion, we follow up on the previously developed multi-signal Expert Advisor with the objective of exploring and applying available optimization methods. The aim is to determine whether the trading performance of the EA can be meaningfully improved through systematic optimization based on historical data.
In this article, we show how to send authenticated requests to the Binance API using MQL5 to retrieve your account balance for all assets. Learn how to use your API key, server time, and signature to securely access account data, and how to save the response to a file for future use.
In this article, we will explore the innovative Chimera framework: a two-dimensional state-space model that uses neural networks to analyze multivariate time series. This method offers high accuracy with low computational cost, outperforming traditional approaches and Transformer architectures.
In this article, we enhance the canvas-based price dashboard in MQL5 by adding a pixel-perfect scrollable text panel for usage guides, overcoming native scrolling limitations through custom antialiasing and a rounded scrollbar design with hover-expand functionality. The text panel supports themed backgrounds with opacity, dynamic line wrapping for content like instructions and contacts, and interactive navigation via up/down buttons, slider dragging, and mouse wheel scrolling within the body area.
In this part, we will focus on designing an intelligent execution layer that continuously monitors and evaluates real-time spread conditions across multiple symbols. The EA dynamically adapts its symbol selection by enabling or disabling trading based on spread efficiency rather than fixed rules. This approach allows high-frequency multi-pair systems to prioritize cost-effective symbols.
In this article, we develop a Nick Rypock Trailing Reverse (NRTR) trading system in MQL5 that uses channel indicators for reversal signals, enabling trend-following entries with hedging support for buys and sells. We incorporate risk management features like auto lot sizing based on equity or balance, fixed or dynamic stop-loss and take-profit levels using ATR multipliers, and position limits.
Wave analysis is one of the methods used in technical analysis. This article focuses on two less conventional wave patterns: triangular and sawtooth waves. These formations underpin a number of technical indicators designed for market price analysis.
In this article, we enhance the MQL5 canvas dashboard with advanced visual effects, including blur gradients for fog overlays, shadow rendering for headers, and antialiased drawing for smoother lines and curves. We add smooth mouse wheel scrolling to the text panel that does not interfere with the chart zoom scale, technically an upgrade.
The extent of liquidity zones and the magnitude of the breakout range are key variables that substantially affect the probability of a retest occurring. In this discussion, we outline the complete process for developing an indicator that incorporates these ratios.
An empirical study of Larry Williams' short-term trading patterns, showing how classic setups can be automated in MQL5, tested on real market data, and evaluated for consistency, profitability, and practical trading value.
