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.
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.
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.
Breadth First Search (BFS) uses level-order traversal to model market structure as a directed graph of price swings evolving through time. By analyzing historical bars or sessions layer by layer, BFS prioritizes recent price behavior while still respecting deeper market memory.
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.
We continue exploring hybrid graph sequence models (GSM++), which integrate the advantages of different architectures, providing high analysis accuracy and efficient distribution of computing resources. These models effectively identify hidden patterns, reducing the impact of market noise and improving forecasting quality.
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.
This article develops a market state classification module for MQL5 that interprets price behavior using completed price data. By examining volatility contraction, expansion, and structural consistency, the tool classifies market conditions as compression, transition, expansion, or trend, providing a clear contextual framework for price action analysis.
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.
An in-depth walkthrough of building a Larry Williams inspired volatility breakout Expert Advisor in MQL5, combining swing structure, volatility-based entries, trade day of the week filtering, time filters, and flexible risk management, with a complete implementation and reproducible test setup.
In this article, we build a versatile RSI indicator in MQL5 supporting multiple variants, data sources, and smoothing methods for improved analysis. We add hue shifts for color visuals, dynamic boundaries for overbought/oversold zones, and notifications for trend alerts. It includes multi-timeframe support with interpolation, offering us a customizable RSI tool for diverse strategies.
From ChatGPT to Gemini and many model AI tools for text, image, and video generation. Transformers have rocked the AI-world. But, are they applicable in the financial (trading) space? Let's find out.
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.
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.
This article presents a session-based analytical framework that combines time-defined market sessions with the Candle Pressure Index (CPI) to classify acceptance and rejection behavior at session boundaries using closed-candle data and clearly defined rules.
Hybrid graph sequence models (GSM++) combine the advantages of different architectures to provide high-fidelity data analysis and optimized computational costs. These models adapt effectively to dynamic market data, improving the presentation and processing of financial information.
This article presents the Chow test for detecting structural breaks in pair relationships and the application of the Cumulative Sum of Squares - CUSUM - for structural breaks monitoring and early detection. The article uses the Nvidia/Intel partnership announcement and the US Gov foreign trade tariff announcement as examples of slope inversion and intercept shift, respectively. Python scripts for all the tests are provided.
In this fascinating article, we build our very first trading robot in the simulator and run a strategy testing action that resembles how the MetaTrader 5 strategy tester works, then compare the outcome produced in a custom simulation against our favorite terminal.
This article presents the development of an arbitrage analysis panel in MQL5. How to get fair exchange rates on Forex in different ways? Create an indicator to obtain deviations of market prices from fair exchange rates, as well as to assess the benefits of arbitrage ways of exchanging one currency for another (as in triangular arbitrage).
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.
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.
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.
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.
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, 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.
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.
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 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.
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.
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 this article, we develop a gauge-style RSI indicator in MQL5 that visualizes Relative Strength Index values on a circular scale with a dynamic needle, color-coded ranges for overbought and oversold levels, and customizable legends. We utilize the Canvas class to draw elements like arcs, ticks, and pies, ensuring smooth updates on new RSI data.
