Articles on the MQL5 programming and use of trading robots
Expert Advisors created for the MetaTrader platform perform a variety of functions implemented by their developers. Trading robots can track financial symbols 24 hours a day, copy deals, create and send reports, analyze news and even provide specific custom graphical interface.
The articles describe programming techniques, mathematical ideas for data processing, tips on creating and ordering of trading robots.
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Automating Trading Strategies in MQL5 (Part 26): Building a Pin Bar Averaging System for Multi-Position Trading
In this article, we develop a Pin Bar Averaging system in MQL5 that detects pin bar patterns to initiate trades and employs an averaging strategy for multi-position management, enhanced by trailing stops and breakeven adjustments. We incorporate customizable parameters with a dashboard for real-time monitoring of positions and profits.
Neural Networks in Trading: Parameter-Efficient Transformer with Segmented Attention (Final Part)
In the previous work, we discussed the theoretical aspects of the PSformer framework, which includes two major innovations in the classical Transformer architecture: the Parameter Shared (PS) mechanism and attention to spatio-temporal segments (SegAtt). In this article, we continue the work we started on implementing the proposed approaches using MQL5.
Neural Networks in Trading: A Parameter-Efficient Transformer with Segmented Attention (PSformer)
This article introduces the new PSformer framework, which adapts the architecture of the vanilla Transformer to solving problems related to multivariate time series forecasting. The framework is based on two key innovations: the Parameter Sharing (PS) mechanism and the Segment Attention (SegAtt).
Automating Trading Strategies in MQL5 (Part 25): Trendline Trader with Least Squares Fit and Dynamic Signal Generation
In this article, we develop a trendline trader program that uses least squares fit to detect support and resistance trendlines, generating dynamic buy and sell signals based on price touches and open positions based on generated signals.
Neural Networks in Trading: Enhancing Transformer Efficiency by Reducing Sharpness (Final Part)
SAMformer offers a solution to the key drawbacks of Transformer models in long-term time series forecasting, such as training complexity and poor generalization on small datasets. Its shallow architecture and sharpness-aware optimization help avoid suboptimal local minima. In this article, we will continue to implement approaches using MQL5 and evaluate their practical value.
Python-MetaTrader 5 Strategy Tester (Part 01): Trade Simulator
The MetaTrader 5 module offered in Python provides a convenient way of opening trades in the MetaTrader 5 app using Python, but it has a huge problem, it doesn't have the strategy tester capability present in the MetaTrader 5 app, In this article series, we will build a framework for back testing your trading strategies in Python environments.
MQL5 Wizard Techniques you should know (Part 78): Using Gator Oscillator and the Accumulation/Distribution Oscillator
The Gator Oscillator by Bill Williams and the Accumulation/Distribution Oscillator are another indicator pairing that could be used harmoniously within an MQL5 Expert Advisor. We use the Gator Oscillator for its ability to affirm trends, while the A/D is used to provide confirmation of the trends via checks on volume. We are following up our last article where we introduced 5 signal patterns by introducing another 5 to complete our typical set of 10. As always, we use the MQL5 wizard to build and test out their potential.
MQL5 Trading Tools (Part 7): Informational Dashboard for Multi-Symbol Position and Account Monitoring
In this article, we develop an informational dashboard in MQL5 for monitoring multi-symbol positions and account metrics like balance, equity, and free margin. We implement a sortable grid with real-time updates, CSV export, and a glowing header effect to enhance usability and visual appeal.
Self Optimizing Expert Advisors in MQL5 (Part 10): Matrix Factorization
Factorization is a mathematical process used to gain insights into the attributes of data. When we apply factorization to large sets of market data—organized in rows and columns—we can uncover patterns and characteristics of the market. Factorization is a powerful tool, and this article will show how you can use it within the MetaTrader 5 terminal, through the MQL5 API, to gain more profound insights into your market data.
From Novice to Expert: Reporting EA — Setting up the work flow
Brokerages often provide trading account reports at regular intervals, based on a predefined schedule. These firms, through their API technologies, have access to your account activity and trading history, allowing them to generate performance reports on your behalf. Similarly, the MetaTrader 5 terminal stores detailed records of your trading activity, which can be leveraged using MQL5 to create fully customized reports and define personalized delivery methods.
MQL5 Wizard Techniques you should know (Part 77): Using Gator Oscillator and the Accumulation/Distribution Oscillator
The Gator Oscillator by Bill Williams and the Accumulation/Distribution Oscillator are another indicator pairing that could be used harmoniously within an MQL5 Expert Advisor. We use the Gator Oscillator for its ability to affirm trends, while the A/D is used to provide confirmation of the trends via checks on volume. In exploring this indicator pairing, as always, we use the MQL5 wizard to build and test out their potential.
From Novice to Expert: Animated News Headline Using MQL5 (VII) — Post Impact Strategy for News Trading
The risk of whipsaw is extremely high during the first minute following a high-impact economic news release. In that brief window, price movements can be erratic and volatile, often triggering both sides of pending orders. Shortly after the release—typically within a minute—the market tends to stabilize, resuming or correcting the prevailing trend with more typical volatility. In this section, we’ll explore an alternative approach to news trading, aiming to assess its effectiveness as a valuable addition to a trader’s toolkit. Continue reading for more insights and details in this discussion.
Price Action Analysis Toolkit Development (Part 33): Candle Range Theory Tool
Upgrade your market reading with the Candle-Range Theory suite for MetaTrader 5, a fully MQL5-native solution that converts raw price bars into real-time volatility intelligence. The lightweight CRangePattern library benchmarks each candle’s true range against an adaptive ATR and classifies it the instant it closes; the CRT Indicator then projects those classifications on your chart as crisp, color-coded rectangles and arrows that reveal tightening consolidations, explosive breakouts, and full-range engulfment the moment they occur.
MQL5 Trading Tools (Part 6): Dynamic Holographic Dashboard with Pulse Animations and Controls
In this article, we create a dynamic holographic dashboard in MQL5 for monitoring symbols and timeframes with RSI, volatility alerts, and sorting options. We add pulse animations, interactive buttons, and holographic effects to make the tool visually engaging and responsive.
Introduction to MQL5 (Part 19): Automating Wolfe Wave Detection
This article shows how to programmatically identify bullish and bearish Wolfe Wave patterns and trade them using MQL5. We’ll explore how to identify Wolfe Wave structures programmatically and execute trades based on them using MQL5. This includes detecting key swing points, validating pattern rules, and preparing the EA to act on the signals it finds.
MQL5 Wizard Techniques you should know (Part 76): Using Patterns of Awesome Oscillator and the Envelope Channels with Supervised Learning
We follow up on our last article, where we introduced the indicator couple of the Awesome-Oscillator and the Envelope Channel, by looking at how this pairing could be enhanced with Supervised Learning. The Awesome-Oscillator and Envelope-Channel are a trend-spotting and support/resistance complimentary mix. Our supervised learning approach is a CNN that engages the Dot Product Kernel with Cross-Time-Attention to size its kernels and channels. As per usual, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Automating Trading Strategies in MQL5 (Part 24): London Session Breakout System with Risk Management and Trailing Stops
In this article, we develop a London Session Breakout System that identifies pre-London range breakouts and places pending orders with customizable trade types and risk settings. We incorporate features like trailing stops, risk-to-reward ratios, maximum drawdown limits, and a control panel for real-time monitoring and management.
Reimagining Classic Strategies (Part 14): Multiple Strategy Analysis
In this article, we continue our exploration of building an ensemble of trading strategies and using the MT5 genetic optimizer to tune the strategy parameters. Today, we analyzed the data in Python, showing our model could better predict which strategy would outperform, achieving higher accuracy than forecasting market returns directly. However, when we tested our application with its statistical models, our performance levels fell dismally. We subsequently discovered that the genetic optimizer unfortunately favored highly correlated strategies, prompting us to revise our method to keep vote weights fixed and focus optimization on indicator settings instead.
MQL5 Trading Tools (Part 5): Creating a Rolling Ticker Tape for Real-Time Symbol Monitoring
In this article, we develop a rolling ticker tape in MQL5 for real-time monitoring of multiple symbols, displaying bid prices, spreads, and daily percentage changes with scrolling effects. We implement customizable fonts, colors, and scroll speeds to highlight price movements and trends effectively.
Neural Networks in Trading: Enhancing Transformer Efficiency by Reducing Sharpness (SAMformer)
Training Transformer models requires large amounts of data and is often difficult since the models are not good at generalizing to small datasets. The SAMformer framework helps solve this problem by avoiding poor local minima. This improves the efficiency of models even on limited training datasets.
From Novice to Expert: Animated News Headline Using MQL5 (VI) — Pending Order Strategy for News Trading
In this article, we shift focus toward integrating news-driven order execution logic—enabling the EA to act, not just inform. Join us as we explore how to implement automated trade execution in MQL5 and extend the News Headline EA into a fully responsive trading system. Expert Advisors offer significant advantages for algorithmic developers thanks to the wide range of features they support. So far, we’ve focused on building a news and calendar events presentation tool, complete with integrated AI insights lanes and technical indicator insights.
Self Optimizing Expert Advisors in MQL5 (Part 9): Double Moving Average Crossover
This article outlines the design of a double moving average crossover strategy that uses signals from a higher timeframe (D1) to guide entries on a lower timeframe (M15), with stop-loss levels calculated from an intermediate risk timeframe (H4). It introduces system constants, custom enumerations, and logic for trend-following and mean-reverting modes, while emphasizing modularity and future optimization using a genetic algorithm. The approach allows for flexible entry and exit conditions, aiming to reduce signal lag and improve trade timing by aligning lower-timeframe entries with higher-timeframe trends.
MQL5 Wizard Techniques you should know (Part 75): Using Awesome Oscillator and the Envelopes
The Awesome Oscillator by Bill Williams and the Envelopes Channel are a pairing that could be used complimentarily within an MQL5 Expert Advisor. We use the Awesome Oscillator for its ability to spot trends, while the envelopes channel is incorporated to define our support/resistance levels. In exploring this indicator pairing, we use the MQL5 wizard to build and test any potential these two may possess.
Implementing Practical Modules from Other Languages in MQL5 (Part 02): Building the REQUESTS Library, Inspired by Python
In this article, we implement a module similar to requests offered in Python to make it easier to send and receive web requests in MetaTrader 5 using MQL5.
MQL5 Trading Tools (Part 4): Improving the Multi-Timeframe Scanner Dashboard with Dynamic Positioning and Toggle Features
In this article, we upgrade the MQL5 Multi-Timeframe Scanner Dashboard with movable and toggle features. We enable dragging the dashboard and a minimize/maximize option for better screen use. We implement and test these enhancements for improved trading flexibility.
Cycles and trading
This article is about using cycles in trading. We will consider building a trading strategy based on cyclical models.
Neural Networks in Trading: Optimizing the Transformer for Time Series Forecasting (LSEAttention)
The LSEAttention framework offers improvements to the Transformer architecture. It was designed specifically for long-term multivariate time series forecasting. The approaches proposed by the authors of the method can be applied to solve problems of entropy collapse and learning instability, which are often encountered with vanilla Transformer.
Introduction to MQL5 (Part 18): Introduction to Wolfe Wave Pattern
This article explains the Wolfe Wave pattern in detail, covering both the bearish and bullish variations. It also breaks down the step-by-step logic used to identify valid buy and sell setups based on this advanced chart pattern.
Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (3) — Weighted Voting Policy
This article explores how determining the optimal number of strategies in an ensemble can be a complex task that is easier to solve through the use of the MetaTrader 5 genetic optimizer. The MQL5 Cloud is also employed as a key resource for accelerating backtesting and optimization. All in all, our discussion here sets the stage for developing statistical models to evaluate and improve trading strategies based on our initial ensemble results.
MQL5 Wizard Techniques you should know (Part 74): Using Patterns of Ichimoku and the ADX-Wilder with Supervised Learning
We follow up on our last article, where we introduced the indicator pair of the Ichimoku and the ADX, by looking at how this duo could be improved with Supervised Learning. Ichimoku and ADX are a support/resistance plus trend complimentary pairing. Our supervised learning approach uses a neural network that engages the Deep Spectral Mixture Kernel to fine tune the forecasts of this indicator pairing. As per usual, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Automating Trading Strategies in MQL5 (Part 23): Zone Recovery with Trailing and Basket Logic
In this article, we enhance our Zone Recovery System by introducing trailing stops and multi-basket trading capabilities. We explore how the improved architecture uses dynamic trailing stops to lock in profits and a basket management system to handle multiple trade signals efficiently. Through implementation and backtesting, we demonstrate a more robust trading system tailored for adaptive market performance.
Neural Networks in Trading: Hyperbolic Latent Diffusion Model (Final Part)
The use of anisotropic diffusion processes for encoding the initial data in a hyperbolic latent space, as proposed in the HypDIff framework, assists in preserving the topological features of the current market situation and improves the quality of its analysis. In the previous article, we started implementing the proposed approaches using MQL5. Today we will continue the work we started and will bring it to its logical conclusion.
Neural Networks in Trading: Hyperbolic Latent Diffusion Model (HypDiff)
The article considers methods of encoding initial data in hyperbolic latent space through anisotropic diffusion processes. This helps to more accurately preserve the topological characteristics of the current market situation and improves the quality of its analysis.
Automating Trading Strategies in MQL5 (Part 22): Creating a Zone Recovery System for Envelopes Trend Trading
In this article, we develop a Zone Recovery System integrated with an Envelopes trend-trading strategy in MQL5. We outline the architecture for using RSI and Envelopes indicators to trigger trades and manage recovery zones to mitigate losses. Through implementation and backtesting, we show how to build an effective automated trading system for dynamic markets
MQL5 Wizard Techniques you should know (Part 73): Using Patterns of Ichimoku and the ADX-Wilder
The Ichimoku-Kinko-Hyo Indicator and the ADX-Wilder oscillator are a pairing that could be used in complimentarily within an MQL5 Expert Advisor. The Ichimoku is multi-faceted, however for this article, we are relying on it primarily for its ability to define support and resistance levels. Meanwhile, we also use the ADX to define our trend. As usual, we use the MQL5 wizard to build and test any potential these two may possess.
Developing a multi-currency Expert Advisor (Part 20): Putting in order the conveyor of automatic project optimization stages (I)
We have already created quite a few components that help arrange auto optimization. During the creation, we followed the traditional cyclical structure: from creating minimal working code to refactoring and obtaining improved code. It is time to start clearing up our database, which is also a key component in the system we are creating.
MQL5 Wizard Techniques you should know (Part 72): Using Patterns of MACD and the OBV with Supervised Learning
We follow up on our last article, where we introduced the indicator pair of the MACD and the OBV, by looking at how this pairing could be enhanced with Machine Learning. MACD and OBV are a trend and volume complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Automating Trading Strategies in MQL5 (Part 21): Enhancing Neural Network Trading with Adaptive Learning Rates
In this article, we enhance a neural network trading strategy in MQL5 with an adaptive learning rate to boost accuracy. We design and implement this mechanism, then test its performance. The article concludes with optimization insights for algorithmic trading.
Data Science and ML (Part 45): Forex Time series forecasting using PROPHET by Facebook Model
The Prophet model, developed by Facebook, is a robust time series forecasting tool designed to capture trends, seasonality, and holiday effects with minimal manual tuning. It has been widely adopted for demand forecasting and business planning. In this article, we explore the effectiveness of Prophet in forecasting volatility in forex instruments, showcasing how it can be applied beyond traditional business use cases.
Automating Trading Strategies in MQL5 (Part 20): Multi-Symbol Strategy Using CCI and AO
In this article, we create a multi-symbol trading strategy using CCI and AO indicators to detect trend reversals. We cover its design, MQL5 implementation, and backtesting process. The article concludes with tips for performance improvement.