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.
We are making a remote professional risk manager for Forex in Python, deploying it on the server step by step. In the course of the article, we will understand how to programmatically manage Forex risks, and how not to waste a Forex deposit any more.
In this article, we will continue to connect the new strategy to the created auto optimization system. Let's look at what changes need to be made to the optimization project creation EA, as well as the second and third stage EAs.
In this article we introduce Python-MetaTrader5-like ways of handling trading operations such as opening, closing, and modifying orders in the simulator. To ensure the simulation behaves like MetaTrader 5, a strict validation layer for trade requests is implemented, taking into account symbol trading parameters and typical brokerage restrictions.
In this article, we enhance the Smart WaveTrend Crossover indicator in MQL5 by integrating canvas-based drawing for fog gradient overlays, signal boxes that detect breakouts, and customizable buy/sell bubbles or triangles for visual alerts. We incorporate risk management features with dynamic take-profit and stop-loss levels calculated via candle multipliers or percentages, displayed through lines and a table, alongside options for trend filtering and box extensions.
This article presents the design and MetaTrader 5 implementation of the Candle Pressure Index (CPI)—a CLV-based overlay that visualizes intra-Bar buying and selling pressure directly on price charts. The discussion focuses on candle structure, pressure classification, visualization mechanics, and a non-repainting, transition-based alert system designed for consistent behavior across timeframes and instruments.
Today's article is a continuation of the previous one. We will look at the implementation of an Expert Advisor, focusing mainly on how the server code is executed. The code given in the previous article is not enough to make everything work as expected, so we need to dig a little deeper into it. Therefore, it is necessary to read both articles to better understand what will happen.
We continue to explore the innovative Chimera framework – a two-dimensional state-space model that uses neural network technologies to analyze multidimensional time series. This method provides high forecasting accuracy with low computational cost.
This article explores the development of an ensemble algorithmic trading strategy for the EURUSD market that combines the Bollinger Bands and the Relative Strength Indicator (RSI). Initial rule-based strategies produced high-quality signals but suffered from low trade frequency and limited profitability. Multiple iterations of the strategy were evaluated, revealing flaws in our understanding of the market, increased noise, and degraded performance. By appropriately employing statistical learning algorithms, shifting the modeling target to technical indicators, applying proper scaling, and combining machine learning forecasts with classical trading rules, the final strategy achieved significantly improved profitability and trade frequency while maintaining acceptable signal quality.
In this article, we build a correlation matrix dashboard in MQL5 to compute asset relationships using Pearson, Spearman, and Kendall methods over a set timeframe and bars. The system offers standard mode with color thresholds and p-value stars, plus heatmap mode with gradient visuals for correlation strengths. It includes an interactive UI with timeframe selectors, mode toggles, and a dynamic legend for efficient analysis of symbol interdependencies.
In this article, we will examine the movements of synthetic currencies using Python and MQL5 and explore how feasible Forex arbitrage is today. We will also consider ready-made Python code for analyzing synthetic currencies and share more details on what synthetic currencies are in Forex.
This is a specialized trend-following EA that makes both short and long-term analyses, trading decisions, and executions based on the overall trend and its strength. This article will explore in detail an EA that is specifically designed for traders who are patient, disciplined, and focused enough to only execute trades and hold their positions only when trading with strength and in the trend direction without changing their bias frequently, especially against the trend, until take-profit targets are hit.
This article explores a method that combines Heikin‑Ashi smoothing with EMA20 High and Low boundaries and an EMA50 trend filter to improve trade clarity and timing. It demonstrates how these tools can help traders identify genuine momentum, filter out noise, and better navigate volatile or trending markets.
This article demonstrates how to automate Larry Williams’ volatility breakout strategy in MQL5 using a practical, step-by-step approach. You will learn how to calculate daily range expansions, derive buy and sell levels, manage risk with range-based stops and reward-based targets, and structure a professional Expert Advisor for MetaTrader 5. Designed for traders and developers looking to transform Larry Williams’ market concepts into a fully testable and deployable automated trading system.
In this article, we introduce functions similar to those provided by the Python-MetaTrader 5 module, providing a simulator with a familiar interface and a custom way of handling bars and ticks internally.
Given everything that has been shown so far, I think we can now start implementing some kind of application to run some symbol directly on the chart. However, first we need to talk about a concept that can be rather confusing for beginners. Namely, it's the fact that applications developed in MQL5 and intended for display on a chart are not created in the same way as we have seen so far. In this article, we'll begin to understand this a little better.
Today we will take a look at my first arbitrage robot — a liquidity provider (if you can call it that) for synthetic assets. Currently, this bot is successfully operating as a module in a large machine learning system, but I pulled up an old Forex arbitrage robot from the cloud, so let's take a look at it and think about what we can do with it today.
In this article, you will learn how to create an interactive control panel in MetaTrader 5. We cover the basics of adding input fields, action buttons, and labels to display text. Using a project-based approach, you will see how to set up a panel where users can type messages and eventually display server responses from an API.
In this article, we develop a custom indicator in MQL5 called Smart WaveTrend Crossover, utilizing dual WaveTrend oscillators—one for generating crossover signals and another for trend filtering—with customizable parameters for channel, average, and moving average lengths. The indicator plots colored candles based on the trend direction, displays buy and sell arrow signals on crossovers, and includes options to enable trend confirmation and adjust visual elements like colors and offsets.
How does the market observe Fibonacci-based relationships? This sequence, where each subsequent number is equal to the sum of the two previous ones (1, 1, 2, 3, 5, 8, 13, 21...), not only describes the growth of the rabbit population. We will consider the Pythagorean hypothesis that everything in the world is subject to certain relationships of numbers...
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.
Build a practical MetaTrader 5 “Sigma Score” indicator from scratch and learn what it really measures: The z-score of log returns (how many standard deviations the latest move is from the recent average). The article walks through every code block in OnInit(), OnCalculate(), and OnDeinit(), then shows how to interpret thresholds (e.g., ±2) and apply the Sigma Score as a simple “market stress meter” for mean-reversion and momentum trading.
A new bioinspired optimization metaheuristic, NOA (Neuroboids Optimization Algorithm), combines the principles of collective intelligence and neural networks. Unlike conventional methods, the algorithm uses a population of self-learning "neuroboids", each with its own neural network that adapts its search strategy in real time. The article reveals the architecture of the algorithm, the mechanisms of self-learning of agents, and the prospects for applying this hybrid approach to complex optimization problems.
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.
In this article, we present an MQL5 library for modeling volatility, designed to function similarly to Python's arch package. The library currently supports the specification of common conditional mean (HAR, AR, Constant Mean, Zero Mean) and conditional volatility (Constant Variance, ARCH, GARCH) models.
This article demonstrates how to integrate the Google Generative AI API with MetaTrader 5 using MQL5. You will learn how to structure API requests, handle server responses, extract AI-generated content, manage rate limits, and save the results to a text file for easy access.
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.
Explore whether financial markets are truly random by recreating Larry Williams’ market behavior experiments using MQL5. This article demonstrates how simple price-action tests can reveal statistical market biases using a custom Expert Advisor.
How about creating something practical using sockets? In today's article, we'll start creating a mini-chat. Let's look together at how this is done - it will be very interesting. Please note that the code provided here is for educational purposes only. It should not be used for commercial purposes or in ready-made applications, as it does not provide data transfer security and the content transmitted over the socket can be accessed.
Successful Restaurateur Algorithm (SRA) is an innovative optimization method inspired by restaurant business management principles. Unlike traditional approaches, SRA does not discard weak solutions, but improves them by combining with elements of successful ones. The algorithm shows competitive results and offers a fresh perspective on balancing exploration and exploitation in optimization problems.
We continue exploring a multi-task learning framework based on ResNeXt, which is characterized by modularity, high computational efficiency, and the ability to identify stable patterns in data. Using a single encoder and specialized "heads" reduces the risk of model overfitting and improves the quality of forecasts.
This article shows how to simplify complex MQL5 file operations by building a Python-style interface for effortless reading and writing. It explains how to recreate Python’s intuitive file-handling patterns through custom functions and classes. The result is a cleaner, more reliable approach to MQL5 file I/O.
In this article, we enhance the gauge-based indicator in MQL5 to support multiple oscillators, allowing user selection through an enumeration for single or combined displays. We introduce sector and round gauge styles via derived classes from a base gauge framework, improving case rendering with arcs, lines, and polygons for a more refined visual appearance.
In this article, we will attempt to predict the market with a decent model for time series forecasting named DeepAR. A model that is a combination of deep neural networks and autoregressive properties found in models like ARIMA and Vector Autoregressive (VAR).
This article proposes another original approach to creating trading systems based on machine learning, using clustering and trade labeling for mean reversion strategies.
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.
The BOA method is inspired by the classic game of billiards and simulates the search for optimal solutions as a game with balls trying to fall into pockets representing the best results. In this article, we will consider the basics of BOA, its mathematical model, and its efficiency in solving various optimization problems.
Learn how to automate Larry Williams market structure concepts in MQL5 by building a complete Expert Advisor that reads swing points, generates trade signals, manages risk, and applies a dynamic trailing stop strategy.
In the article, we will make the table column widths adjustable using the mouse cursor, sort the table by column data, and add a new class to simplify the creation of tables based on any data sets.
