Articles on the MQL5 programming and use of trading robots

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.

The article describes an MQL5 EA that automates Wyckoff accumulation and distribution via a finite state machine. It confirms spring to SOS and upthrust to SOW before placing LPS or LPSY entries, using relative tick volume as the confirmation metric. Readers get the state model, detection criteria, code organization, and MetaTrader 5 testing procedure.

In this article, we prepare our MQL5 trading system for strategy testing by embedding economic calendar data as a resource for non-live analysis. We implement event loading and filtering for time, currency, and impact, then validate it in the Strategy Tester. This enables effective backtesting of news-driven strategies.

Generating new indicators from existing ones offers a powerful way to enhance trading analysis. By defining a mathematical function that integrates the outputs of existing indicators, traders can create hybrid indicators that consolidate multiple signals into a single, efficient tool. This article introduces a new indicator built from three oscillators using a modified version of the Pearson correlation function, which we call the Pseudo Pearson Correlation (PPC). The PPC indicator aims to quantify the dynamic relationship between oscillators and apply it within a practical trading strategy.

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.

In financial markets, the laws of retracement remain among the most undeniable forces. It is a rule of thumb that price will always retrace—whether in large moves or even within the smallest tick patterns, which often appear as a zigzag. However, the retracement pattern itself is never fixed; it remains uncertain and subject to anticipation. This uncertainty explains why traders rely on multiple Fibonacci levels, each carrying a certain probability of influence. In this discussion, we introduce a refined strategy that applies Fibonacci techniques to address the challenges of trading shortly after major economic event announcements. By combining retracement principles with event-driven market behavior, we aim to uncover more reliable entry and exit opportunities. Join to explore the full discussion and see how Fibonacci can be adapted to post-event trading.

Stop-loss and take-profit placement is usually the least-measured decision in a trading system. This Expert Advisor reads your closed history, replays M1 price between each entry and exit to measure Maximum Adverse and Favorable Excursion per trade, and splits winners from losers. From the distributions and trade efficiency it derives data-driven stop and target levels - measured from your own account, not a rule of thumb. Analysis only; it does not trade.

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.

The Angle of Attack is an often-quoted metric whose steepness is understood to strongly correlate with the strength of a prevailing trend. We look at how it is commonly used and understood and examine if there are changes that could be introduced in how it's measured for the benefit of a trade system that puts it in use.

In this series of articles, we revisit classic strategies to see if we can improve them using AI. In today's article, we will examine the popular strategy of multiple time-frame analysis to judge if the strategy would be enhanced with AI.

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.

This article presents CPyramidEngine, a reusable MQL5 class that adds disciplined pyramiding to any Expert Advisor with about six lines of integration. The engine enforces three constraints: strictly decreasing lot sizes, a single unified stop that advances after each add-on, and broker-level validation of every modification. It explains common failure modes in naive implementations and shows how to keep total account risk quantifiable and controlled as positions are added.

One of the key problems within reinforcement learning is environmental exploration. Previously, we have already seen the research method based on Intrinsic Curiosity. Today I propose to look at another algorithm: Exploration via Disagreement.

In the previous article, we introduced the multi-agent self-adaptive framework MASA, which combines reinforcement learning approaches and self-adaptive strategies, providing a harmonious balance between profitability and risk in turbulent market conditions. We have built the functionality of individual agents within this framework. In this article, we will continue the work we started, bringing it to its logical conclusion.

The article describes a variant of options emulation through an underlying asset implemented in the MQL5 programming language. The pros and cons of the chosen approach are compared with real exchange options using the example of the FORTS futures market of the MOEX Moscow exchange and the Bybit crypto exchange.

Bayesian inference is the adoption of Bayes Theorem to update probability hypothesis as new information is made available. This intuitively leans to adaptation in time series analysis, and so we have a look at how we could use this in building custom classes not just for the signal but also money-management and trailing-stops.

We will breakdown the main MQL5 code into specified code snippets to illustrate the integration of Telegram and WhatsApp for receiving signal notifications from the Trend Constraint indicator we are creating in this article series. This will help traders, both novices and experienced developers, grasp the concept easily. First, we will cover the setup of MetaTrader 5 for notifications and its significance to the user. This will help developers in advance to take notes to further apply in their systems.

The number of strategies that can be integrated into an Expert Advisor is virtually limitless. However, each additional strategy increases the complexity of the algorithm. By incorporating multiple strategies, an Expert Advisor can better adapt to varying market conditions, potentially enhancing its profitability. Today, we will explore how to implement MQL5 for one of the prominent strategies developed by Richard Donchian, as we continue to enhance the functionality of our Trend Constraint Expert.

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.

The problem of reinforcement learning lies in the need to define a reward function. It can be complex or difficult to formalize. To address this problem, activity-based and environment-based approaches are being explored to learn skills without an explicit reward function.

In the previous article, we implemented the Soft Actor-Critic algorithm, but were unable to train a profitable model. Here we will optimize the previously created model to obtain the desired results.

The previously developed risk manager contained only basic functionality. Let's try to consider possible ways of its development, allowing us to improve trading results without interfering with the logic of trading strategies.

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.

We have already made some progress in developing a multi-currency EA with several strategies working in parallel. Considering the accumulated experience, let's review the architecture of our solution and try to improve it before we go too far ahead.

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 previous article, we introduced the DDPG method, which allows training models in a continuous action space. However, like other Q-learning methods, DDPG is prone to overestimating Q-function values. This problem often results in training an agent with a suboptimal strategy. In this article, we will look at some approaches to overcome the mentioned issue.

We invite you to explore the next stage in implementing the SCNN framework, which combines flexibility and interpretability, allowing structural components of a time series to be identified precisely. The article provides a detailed explanation of the mechanisms of adaptive normalization and attention, which ensure the model's resilience to changing market conditions.