MQL5 Programming Articles

Larry Connors is a renowned trader and author, best known for his work in quantitative trading and strategies like the 2-period RSI (RSI2), which helps identify short-term overbought and oversold market conditions. In this article, we’ll first explain the motivation behind our research, then recreate three of Connors’ most famous strategies in MQL5 and apply them to intraday trading of the S&P 500 index CFD.

Determining market direction can be straightforward, but knowing when to enter can be challenging. As part of the series titled "Price Action Analysis Toolkit Development", I am excited to introduce another tool that provides entry points, take profit levels, and stop loss placements. To achieve this, we have utilized the MQL5 programming language. Let’s delve into each step in this article.

In this article we explore the practical implementation of elastic net regression to minimize overfitting and at the same time automatically separate useful predictors from those that have little prognostic power.

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.

In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.

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.

In the second part of the article, we will continue developing a modified version of the AOS (Atomic Orbital Search) algorithm focusing on specific operators to improve its efficiency and adaptability. After analyzing the fundamentals and mechanics of the algorithm, we will discuss ideas for improving its performance and the ability to analyze complex solution spaces, proposing new approaches to extend its functionality as an optimization tool.

In this article, we take the first step in MQL5 programming, even for complete beginners. We'll show you how to transform familiar candlestick patterns into a fully functional custom indicator. Candlestick patterns are valuable as they reflect real price action and signal market shifts. Instead of manually scanning charts—an approach prone to errors and inefficiencies—we'll discuss how to automate the process with an indicator that identifies and labels patterns for you. Along the way, we’ll explore key concepts like indexing, time series, Average True Range (for accuracy in varying market volatility), and the development of a custom reusable Candlestick Pattern library for use in future projects.

The ATR oscillator is a very popular indicator for acting as a volatility proxy, especially in the forex markets where volume data is scarce. We examine this, on a pattern basis as we have with prior indicators, and share strategies & test reports thanks to the MQL5 wizard library classes and assembly.

Understanding agent behavior is important in many different areas, but most methods focus on just one of the tasks (understanding, noise removal, or prediction), which reduces their effectiveness in real-world scenarios. In this article, we will get acquainted with a model that can adapt to solving various problems.

In this article, we upgrade our Trade Assistant Tool by adding drag-and-drop panel functionality and hover effects to make the interface more intuitive and responsive. We refine the tool to validate real-time order setups, ensuring accurate trade configurations relative to market prices. We also backtest these enhancements to confirm their reliability.

Discover how to develop an Expert Advisor (EA) in MQL5 using multiple indicators like RSI, MA, and Stochastic Oscillator to detect hidden bullish and bearish divergences. Learn to implement effective risk management and automate trades with detailed examples and fully commented source code for educational purposes!

Volatility tends to peak around high-impact news events, creating significant breakout opportunities. In this article, we will outline the implementation process of a calendar-based breakout strategy. We'll cover everything from creating a class to interpret and store calendar data, developing realistic backtests using this data, and finally, implementing execution code for live trading.

In this article, we will analyse the impact of dividend announcements on stock market returns and see how investors can earn more returns than those offered by the market when they expect a company to announce dividends. In doing so, we will also check the validity of the Efficient Market Hypothesis in the context of the Indian Stock Market.

In the previous parts, the Expert Advisor (EA) under development was able to use only a fixed position size for trading. This is acceptable for testing, but is not advisable when trading on a real account. Let's make it possible to trade using variable position sizes.

In this article we will continue the simulator development stage. this time we will see how to effectively create a RANDOM WALK type movement. This type of movement is very intriguing because it forms the basis of everything that happens in the capital market. In addition, we will begin to understand some concepts that are fundamental to those conducting market analysis.

In this article, we will have a look at yet another reinforcement learning approach. It is called goal-conditioned reinforcement learning (GCRL). In this approach, an agent is trained to achieve different goals in specific scenarios.

The last two articles considered the Soft Actor-Critic algorithm, which incorporates entropy regularization into the reward function. This approach balances environmental exploration and model exploitation, but it is only applicable to stochastic models. The current article proposes an alternative approach that is applicable to both stochastic and deterministic models.

We continue the discussion about the use of piecewise linear representation of time series, which was started in the previous article. Today we will see how to combine this method with other approaches to time series analysis to improve the price trend prediction quality.

Discover the secrets of algorithmic alchemy as we guide you through the blend of artistry and precision in decoding financial landscapes. Unearth how Random Forests transform data into predictive prowess, offering a unique perspective on navigating the complex terrain of stock markets. Join us on this journey into the heart of financial wizardry, where we demystify the role of Random Forests in shaping market destiny and unlocking the doors to lucrative opportunities

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.

Uncover the secrets behind these powerful dimensionality reduction techniques as we dissect their applications within the MQL5 trading environment. Delve into the nuances of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), gaining a profound understanding of their impact on strategy development and market analysis.

Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.

Having started developing a multi-currency EA, we have already achieved some results and managed to carry out several code improvement iterations. However, our EA was unable to work with pending orders and resume operation after the terminal restart. Let's add these features.

In this article, I propose to look at the issue of building a trading strategy from a different angle. We will not predict future price movements, but will try to build a trading system based on the analysis of historical data.

Detecting patterns in financial markets is challenging because it involves seeing what's on the chart, something that's difficult to undertake in MQL5 due to image limitations. In this article, we are going to discuss a decent model made in Python that helps us detect patterns present on the chart with minimal effort.

Algorithmic trading system that combines volume analysis with machine learning techniques, specifically LSTM neural networks. Unlike traditional trading approaches that primarily focus on price movements, this system emphasizes volume patterns and their derivatives to predict market movements. The methodology incorporates three main components: volume derivatives analysis (first and second derivatives), LSTM predictions for volume patterns, and traditional technical indicators.

This part focuses on building a flexible, adaptive trading model trained on historical XAUUSD data, preparing it for ONNX export and potential integration into live trading systems.

In this series of articles, we discuss how we can build Expert Advisors capable of autonomously adjusting themselves to dynamic market conditions. In today's article, we will attempt to tune a deep neural network to Deriv's synthetic markets.

This article delves into the application of John Nash's game theory, specifically the Nash Equilibrium, in trading. It discusses how traders can utilize Python scripts and MetaTrader 5 to identify and exploit market inefficiencies using Nash's principles. The article provides a step-by-step guide on implementing these strategies, including the use of Hidden Markov Models (HMM) and statistical analysis, to enhance trading performance.

A large number of the models we have reviewed so far are based on the Transformer architecture. However, they may be inefficient when dealing with long sequences. And in this article, we will get acquainted with an alternative direction of time series forecasting based on state space models.