Algorithmic Trading Articles for Trading systems

Risk Management (Part 5): Integrating the Risk Management System into an Expert Advisor

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.

Python-MetaTrader 5 Strategy Tester (Part 05): Multi-Symbols and Timeframes Strategy Tester

This article presents a MetaTrader 5–compatible backtesting workflow that scales across symbols and timeframes. We use HistoryManager to parallelize data collection, synchronize bars and ticks from all timeframes, and run symbol‑isolated OnTick handlers in threads. You will learn how modelling modes affect speed/accuracy, when to rely on terminal data, how to reduce I/O with event‑driven updates, and how to assemble a complete multicurrency trading robot.

Analyzing Overbought and Oversold Trends Via Chaos Theory Approaches

We determine the overbought and oversold condition of the market according to chaos theory: integrating the principles of chaos theory, fractal geometry and neural networks to forecast financial markets. The study demonstrates the use of the Lyapunov exponent as a measure of market randomness and the dynamic adaptation of trading signals. The methodology includes an algorithm for generating fractal noise, hyperbolic tangent activation, and moment optimization.

Integrating Computer Vision into Trading in MQL5 (Part 1): Creating Basic Functions

The EURUSD forecasting system with the use of computer vision and deep learning. Learn how convolutional neural networks can recognize complex price patterns in the foreign exchange market and predict exchange rate movements with up to 54% accuracy. The article shares the methodology for creating an algorithm that uses artificial intelligence technologies for visual analysis of charts instead of traditional technical indicators. The author demonstrates the process of transforming price data into "images", their processing by a neural network, and a unique opportunity to peer into the "consciousness" of AI through activation maps and attention heatmaps. Practical Python code using the MetaTrader 5 library allows readers to reproduce the system and apply it in their own trading.

Larry Williams Market Secrets (Part 8): Combining Volatility, Structure and Time Filters

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.

Forex Arbitrage Trading: Relationship Assessment Panel

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).

Neural Networks in Trading: Two-Dimensional Connection Space Models (Final Part)

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.

Larry Williams Market Secrets (Part 3): Proving Non-Random Market Behavior with MQL5

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.

Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model (Final Part)

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.

Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model

A multi-task learning framework based on ResNeXt optimizes the analysis of financial data, taking into account its high dimensionality, nonlinearity, and time dependencies. The use of group convolution and specialized heads allows the model to effectively extract key features from the input data.

Neural Networks in Trading: Hierarchical Dual-Tower Transformer (Final Part)

We continue to build the Hidformer hierarchical dual-tower transformer model designed for analyzing and forecasting complex multivariate time series. In this article, we will bring the work we started earlier to its logical conclusion — we will test the model on real historical data.

Risk Management (Part 2): Implementing Lot Calculation in a Graphical Interface

In this article, we will look at how to improve and more effectively apply the concepts presented in the previous article using the powerful MQL5 graphical control libraries. We'll go step by step through the process of creating a fully functional GUI. I'll be explaining the ideas behind it, as well as the purpose and operation of each method used. Additionally, at the end of the article, we will test the panel we created to ensure it functions correctly and meets its stated goals.

Building a Trading System (Part 5): Managing Gains Through Structured Trade Exits

For many traders, it's a familiar pain point: watching a trade come within a whisker of your profit target, only to reverse and hit your stop-loss. Or worse, seeing a trailing stop close you out at breakeven before the market surges toward your original target. This article focuses on using multiple entries at different Reward-to-Risk Ratios to systematically secure gains and reduce overall risk exposure.

Risk Management (Part 1): Fundamentals for Building a Risk Management Class

In this article, we'll cover the basics of risk management in trading and learn how to create your first functions for calculating the appropriate lot size for a trade, as well as a stop-loss. Additionally, we will go into detail about how these features work, explaining each step. Our goal is to provide a clear understanding of how to apply these concepts in automated trading. Finally, we will put everything into practice by creating a simple script with an include file.

Introduction to MQL5 (Part 23): Automating Opening Range Breakout Strategy

This article explores how to build an Opening Range Breakout (ORB) Expert Advisor in MQL5. It explains how the EA identifies breakouts from the market’s initial range and opens trades accordingly. You’ll also learn how to control the number of positions opened and set a specific cutoff time to stop trading automatically.

Price Action Analysis Toolkit Development (Part 45): Creating a Dynamic Level-Analysis Panel in MQL5

In this article, we explore a powerful MQL5 tool that let's you test any price level you desire with just one click. Simply enter your chosen level and press analyze, the EA instantly scans historical data, highlights every touch and breakout on the chart, and displays statistics in a clean, organized dashboard. You'll see exactly how often price respected or broke through your level, and whether it behaved more like support or resistance. Continue reading to explore the detailed procedure.

Reusing Invalidated Orderblocks As Mitigation Blocks (SMC)

In this article, we explore how previously invalidated orderblocks can be reused as mitigation blocks within Smart Money Concepts (SMC). These zones reveal where institutional traders re-enter the market after a failed orderblock, providing high-probability areas for trade continuation in the dominant trend.

How to earn money by fulfilling traders' orders in the Freelance service

MQL5 Freelance is an online service where developers are paid to create trading applications for traders customers. The service has been successfully operating since 2010, with over 100,000 projects completed to date, totaling $7 million in value. As we can see, a substantial amount of money is involved here.

Price Action Analysis Toolkit Development (Part 44): Building a VWMA Crossover Signal EA in MQL5

This article introduces a VWMA crossover signal tool for MetaTrader 5, designed to help traders identify potential bullish and bearish reversals by combining price action with trading volume. The EA generates clear buy and sell signals directly on the chart, features an informative panel, and allows for full user customization, making it a practical addition to your trading strategy.

Building AI-Powered Trading Systems in MQL5 (Part 4): Overcoming Multiline Input, Ensuring Chat Persistence, and Generating Signals

In this article, we enhance the ChatGPT-integrated program in MQL5 overcoming multiline input limitations with improved text rendering, introducing a sidebar for navigating persistent chat storage using AES256 encryption and ZIP compression, and generating initial trade signals through chart data integration.

Neural Networks in Trading: Models Using Wavelet Transform and Multi-Task Attention (Final Part)

In the previous article, we explored the theoretical foundations and began implementing the approaches of the Multitask-Stockformer framework, which combines the wavelet transform and the Self-Attention multitask model. We continue to implement the algorithms of this framework and evaluate their effectiveness on real historical data.

Neural Networks in Trading: Models Using Wavelet Transform and Multi-Task Attention

We invite you to explore a framework that combines wavelet transforms and a multi-task self-attention model, aimed at improving the responsiveness and accuracy of forecasting in volatile market conditions. The wavelet transform allows asset returns to be decomposed into high and low frequencies, carefully capturing long-term market trends and short-term fluctuations.

Market Simulation (Part 02): Cross Orders (II)

Unlike what was done in the previous article, here we will test the selection option using an Expert Advisor. Although this is not a final solution yet, it will be enough for now. With the help of this article, you will be able to understand how to implement one of the possible solutions.

From Novice to Expert: Demystifying Hidden Fibonacci Retracement Levels

In this article, we explore a data-driven approach to discovering and validating non-standard Fibonacci retracement levels that markets may respect. We present a complete workflow tailored for implementation in MQL5, beginning with data collection and bar or swing detection, and extending through clustering, statistical hypothesis testing, backtesting, and integration into an MetaTrader 5 Fibonacci tool. The goal is to create a reproducible pipeline that transforms anecdotal observations into statistically defensible trading signals.

MetaTrader 5 Machine Learning Blueprint (Part 3): Trend-Scanning Labeling Method

We have built a robust feature engineering pipeline using proper tick-based bars to eliminate data leakage and solved the critical problem of labeling with meta-labeled triple-barrier signals. This installment covers the advanced labeling technique, trend-scanning, for adaptive horizons. After covering the theory, an example shows how trend-scanning labels can be used with meta-labeling to improve on the classic moving average crossover strategy.

Using JSON Data API in your MQL projects

Imagine that you can use data that is not found in MetaTrader, you only get data from indicators by price analysis and technical analysis. Now imagine that you can access data that will take your trading power steps higher. You can multiply the power of the MetaTrader software if you mix the output of other software, macro analysis methods, and ultra-advanced tools through the API data. In this article, we will teach you how to use APIs and introduce useful and valuable API data services.

Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (Final Part)

We continue our examination of the StockFormer hybrid trading system, which combines predictive coding and reinforcement learning algorithms for financial time series analysis. The system is based on three Transformer branches with a Diversified Multi-Head Attention (DMH-Attn) mechanism that enables the capturing of complex patterns and interdependencies between assets. Previously, we got acquainted with the theoretical aspects of the framework and implemented the DMH-Attn mechanisms. Today, we will talk about the model architecture and training.

Automating Trading Strategies in MQL5 (Part 36): Supply and Demand Trading with Retest and Impulse Model

In this article, we create a supply and demand trading system in MQL5 that identifies supply and demand zones through consolidation ranges, validates them with impulsive moves, and trades retests with trend confirmation and customizable risk parameters. The system visualizes zones with dynamic labels and colors, supporting trailing stops for risk management.

Price Action Analysis Toolkit Development (Part 43): Candlestick Probability and Breakouts

Enhance your market analysis with the MQL5-native Candlestick Probability EA, a lightweight tool that transforms raw price bars into real-time, instrument-specific probability insights. It classifies Pinbars, Engulfing, and Doji patterns at bar close, uses ATR-aware filtering, and optional breakout confirmation. The EA calculates raw and volume-weighted follow-through percentages, helping you understand each pattern's typical outcome on specific symbols and timeframes. On-chart markers, a compact dashboard, and interactive toggles allow easy validation and focus. Export detailed CSV logs for offline testing. Use it to develop probability profiles, optimize strategies, and turn pattern recognition into a measurable edge.

Building AI-Powered Trading Systems in MQL5 (Part 3): Upgrading to a Scrollable Single Chat-Oriented UI

In this article, we upgrade the ChatGPT-integrated program in MQL5 to a scrollable single chat-oriented UI, enhancing conversation history display with timestamps and dynamic scrolling. The system builds on JSON parsing to manage multi-turn messages, supporting customizable scrollbar modes and hover effects for improved user interaction.

Price movement discretization methods in Python

We will look at price discretization methods using Python + MQL5. In this article, I will share my practical experience developing a Python library that implements a wide range of approaches to bar formation — from classic Volume and Range bars to more exotic methods like Renko and Kagi. We will consider three-line breakout candles and range bars analyzing their statistics and trying to define how else the prices can be represented discretely.

MQL5 Wizard Techniques you should know (Part 69): Using Patterns of SAR and the RVI

The Parabolic-SAR (SAR) and the Relative Vigour Index (RVI) are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered in the past, is also complementary since SAR defines the trend while RVI checks momentum. As usual, we use the MQL5 wizard to build and test any potential this indicator pairing may have.

Price Action Analysis Toolkit Development (Part 30): Commodity Channel Index (CCI), Zero Line EA

Automating price action analysis is the way forward. In this article, we utilize the Dual CCI indicator, the Zero Line Crossover strategy, EMA, and price action to develop a tool that generates trade signals and sets stop-loss (SL) and take-profit (TP) levels using ATR. Please read this article to learn how we approach the development of the CCI Zero Line EA.

Building a Professional Trading System with Heikin Ashi (Part 2): Developing an EA

This article explains how to develop a professional Heikin Ashi-based Expert Advisor (EA) in MQL5. You will learn how to set up input parameters, enumerations, indicators, global variables, and implement the core trading logic. You will also be able to run a backtest on gold to validate your work.

Automating Trading Strategies in MQL5 (Part 35): Creating a Breaker Block Trading System

In this article, we create a Breaker Block Trading System in MQL5 that identifies consolidation ranges, detects breakouts, and validates breaker blocks with swing points to trade retests with defined risk parameters. The system visualizes order and breaker blocks with dynamic labels and arrows, supporting automated trading and trailing stops.

MQL5 Trading Tools (Part 9): Developing a First Run User Setup Wizard for Expert Advisors with Scrollable Guide

In this article, we develop an MQL5 First Run User Setup Wizard for Expert Advisors, featuring a scrollable guide with an interactive dashboard, dynamic text formatting, and visual controls like buttons and a checkbox allowing users to navigate instructions and configure trading parameters efficiently. Users of the program get to have insight of what the program is all about and what to do on the first run, more like an orientation model.

From Novice to Expert: Backend Operations Monitor using MQL5

Using a ready-made solution in trading without concerning yourself with the internal workings of the system may sound comforting, but this is not always the case for developers. Eventually, an upgrade, misperformance, or unexpected error will arise, and it becomes essential to trace exactly where the issue originates to diagnose and resolve it quickly. Today’s discussion focuses on uncovering what normally happens behind the scenes of a trading Expert Advisor, and on developing a custom dedicated class for displaying and logging backend processes using MQL5. This gives both developers and traders the ability to quickly locate errors, monitor behavior, and access diagnostic information specific to each EA.

Price Action Analysis Toolkit Development (Part 42): Interactive Chart Testing with Button Logic and Statistical Levels

In a world where speed and precision matter, analysis tools need to be as smart as the markets we trade. This article presents an EA built on button logic—an interactive system that instantly transforms raw price data into meaningful statistical levels. With a single click, it calculates and displays mean, deviation, percentiles, and more, turning advanced analytics into clear on-chart signals. It highlights the zones where price is most likely to bounce, retrace, or break, making analysis both faster and more practical.

Data Science and ML (Part 44): Forex OHLC Time series Forecasting using Vector Autoregression (VAR)

Explore how Vector Autoregression (VAR) models can forecast Forex OHLC (Open, High, Low, and Close) time series data. This article covers VAR implementation, model training, and real-time forecasting in MetaTrader 5, helping traders analyze interdependent currency movements and improve their trading strategies.

Automating Trading Strategies in MQL5 (Part 34): Trendline Breakout System with R-Squared Goodness of Fit

In this article, we develop a Trendline Breakout System in MQL5 that identifies support and resistance trendlines using swing points, validated by R-squared goodness of fit and angle constraints, to automate breakout trades. Our plan is to detect swing highs and lows within a specified lookback period, construct trendlines with a minimum number of touch points, and validate them using R-squared metrics and angle constraints to ensure reliability.