Articles on MetaTrader 5 integration using MQL5

We continue our last article on DDPG with MA and stochastic indicators by examining other key Reinforcement Learning classes crucial for implementing DDPG. Though we are mostly coding in python, the final product, of a trained network will be exported to as an ONNX to MQL5 where we integrate it as a resource in a wizard assembled Expert Advisor.

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.

The article introduces the dialectical algorithm (DA), a new global optimization method inspired by the philosophical concept of dialectics. The algorithm exploits a unique division of the population into speculative and practical thinkers. Testing shows impressive performance of up to 98% on low-dimensional problems and overall efficiency of 57.95%. The article explains these metrics and presents a detailed description of the algorithm and the results of experiments on different types of functions.

One major challenge is managing multiple chart windows of the same pair running the same program with different features. Let's discuss how to consolidate several integrations into one main program. Additionally, we will share insights on configuring the program to print to a journal and commenting on the successful signal broadcast on the chart interface. Find more information in this article as we progress the article series.

Exploring advanced techniques to integrate MQL5 with powerful data processing tools, this part focuses on efficient handling of big data to enhance trading analysis and decision-making.

In this article, we explore practical and robust risk management techniques specifically tailored for liquidity-based trading. You will learn how to protect positions during retests, handle false breakouts with confidence, and identify signs of potential level manipulation. By the end, you will have built an adaptive Expert Advisor capable of managing zone flips and executing strategic pending orders with integrated risk control.

In this article, we explore practical and robust risk management techniques specifically tailored for liquidity-based trading. You will learn how to protect positions during retests, handle false breakouts with confidence, and identify signs of potential level manipulation. By the end, you will have built an adaptive Expert Advisor capable of managing zone flips and executing strategic pending orders with integrated risk control.

In this article, we introduce functions similar to those provided by the Python-MetaTrader 5 module, providing a simulator with a familiar interface and a custom way of handling bars and ticks internally.

Moving Average and Stochastic Oscillator are very common indicators whose collective patterns we explored in the prior article, via a supervised learning network, to see which “patterns-would-stick”. We take our analyses from that article, a step further by considering the effects' reinforcement learning, when used with this trained network, would have on performance. Readers should note our testing is over a very limited time window. Nonetheless, we continue to harness the minimal coding requirements afforded by the MQL5 wizard in showcasing this.

In this discussion, we will carefully extend the existing Dialog library to incorporate theme management logic. Furthermore, we will integrate methods for theme switching into the CDialog, CEdit, and CButton classes utilized in our Admin Panel project. Continue reading for more insightful perspectives.

The article explores the bird swarm-based algorithm (BSA) inspired by the collective flocking interactions of birds in nature. The different search strategies of individuals in BSA, including switching between flight, vigilance and foraging behavior, make this algorithm multifaceted. It uses the principles of bird flocking, communication, adaptability, leading and following to efficiently find optimal solutions.

The article explores the bird swarm-based algorithm (BSA) inspired by the collective flocking interactions of birds in nature. The different search strategies of individuals in BSA, including switching between flight, vigilance and foraging behavior, make this algorithm multifaceted. It uses the principles of bird flocking, communication, adaptability, leading and following to efficiently find optimal solutions.

Eagle Strategy is an algorithm that mimics the eagle's two-phase hunting strategy: global search via Levy flights using Mantegna method, alternating with intense local exploitation using the firefly algorithm, a mathematically sound approach to balancing exploration and exploitation, and a bioinspired concept that combines two natural phenomena into a single computational method.

This article explores the integration of cryptography within MQL5, enhancing the security and functionality of trading algorithms. We’ll cover key cryptographic methods and their practical implementation in automated trading.

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.

In this article, we will continue to connect the new strategy to the created auto optimization system. Let's look at what changes need to be made to the optimization project creation EA, as well as the second and third stage EAs.

When we develop something in xlwings or any other package that allows reading and writing directly to Excel, we must note that all programs, functions, or procedures execute and then complete their task. They do not remain in a loop, no matter how hard we try to do things differently.

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.

This article explores the fundamentals of the HTTP protocol, covering the main methods (GET, POST, PUT, DELETE), status codes and the structure of URLs. In addition, it presents the beginning of the construction of the Conexus library with the CQueryParam and CURL classes, which facilitate the manipulation of URLs and query parameters in HTTP requests.

The FrAMA Indicator and the Force Index Oscillator are trend and volume tools that could be paired when developing an Expert Advisor. We continue from our last article that introduced this pair by considering machine learning applicability to the pair. We are using a convolution neural network that uses the dot-product kernel in making forecasts with these indicators’ inputs. This is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

Imagine a malicious actor infiltrating the Trading Administrator room, gaining access to the computers and the Admin Panel used to communicate valuable insights to millions of traders worldwide. Such an intrusion could lead to disastrous consequences, such as the unauthorized sending of misleading messages or random clicks on buttons that trigger unintended actions. In this discussion, we will explore the security measures in MQL5 and the new security features we have implemented in our Admin Panel to safeguard against these threats. By enhancing our security protocols, we aim to protect our communication channels and maintain the trust of our global trading community. Find more insights in this article discussion.

Previously, we evaluated the selection of a group of trading strategy instances, with the aim of improving the results of their joint operation, only on the same time period, in which the optimization of individual instances was carried out. Let's see what happens in the forward period.

Imagine a malicious actor infiltrating the Trading Administrator room, gaining access to the computers and the Admin Panel used to communicate valuable insights to millions of traders worldwide. Such an intrusion could lead to disastrous consequences, such as the unauthorized sending of misleading messages or random clicks on buttons that trigger unintended actions. In this discussion, we will explore the security measures in MQL5 and the new security features we have implemented in our Admin Panel to safeguard against these threats. By enhancing our security protocols, we aim to protect our communication channels and maintain the trust of our global trading community. Find more insights in this article discussion.

This guide integrates a trained XGBoost model (ONNX) into an SMC EA to evaluate trade setups before execution. The Python pipeline labels historical XAUUSD events and produces a 12-feature representation aligned with the EA. The result is a reproducible method to train, export, and embed the model so the EA can filter OB, FVG, and BOS signals programmatically.

This guide integrates a trained XGBoost model (ONNX) into an SMC EA to evaluate trade setups before execution. The Python pipeline labels historical XAUUSD events and produces a 12-feature representation aligned with the EA. The result is a reproducible method to train, export, and embed the model so the EA can filter OB, FVG, and BOS signals programmatically.

We aim to create a system for automatic periodic optimization of trading strategies used in one final EA. As the system evolves, it becomes increasingly complex, so it is necessary to look at it as a whole from time to time in order to identify bottlenecks and suboptimal solutions.

This article teaches you how to retrieve and extract price data from external platforms using APIs and the WebRequest function in MQL5. You’ll learn how URLs are structured, how API responses are formatted, how to convert server data into readable strings, and how to identify and extract specific values from JSON responses.

In the last article, we examined the pairing of Ichimoku and the ADX under an Inference Learning framework. For this piece we revisit, Reinforcement Learning when used with an indicator pairing we considered last in ‘Part 68’. The TRIX and Williams Percent Range. Our algorithm for this review will be the Quantile Regression DQN. As usual, we present this as a custom signal class designed for implementation with the MQL5 Wizard.