Articles on manual and algorithmic trading in MetaTrader 5

The article explores the development of a quantum-inspired trading system, transitioning from a Python prototype to an MQL5 implementation for real-world trading. The system uses quantum computing principles like superposition and entanglement to analyze market states, though it runs on classical computers using quantum simulators. Key features include a three-qubit system for analyzing eight market states simultaneously, 24-hour lookback periods, and seven technical indicators for market analysis. While the accuracy rates might seem modest, they provide a significant edge when combined with proper risk management 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.

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 will make the order system more flexible. Here we will consider changes to the code that will make it more flexible, which will allow us to change position stop levels much faster.

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.

Currently, our EA uses the database to obtain initialization strings for single instances of trading strategies. However, the database is quite large and contains a lot of information that is not needed for the actual EA operation. Let's try to ensure the EA's functionality without a mandatory connection to the database.

Enhancing the MQL5 GUI panel with dynamic features can significantly improve the trading experience for users. By incorporating interactive elements, hover effects, and real-time data updates, the panel becomes a powerful tool for modern traders.

The article implements a fast strategy tester for machine learning models using Numba. It is 50 times faster than the pure Python strategy tester. The author recommends using this library to speed up mathematical calculations, especially the ones involving loops.

Dynamic multi pair Expert Advisor leverages both on correlation and inverse correlation strategies to optimize trading performance. By analyzing real-time market data, it identifies and exploits the relationship between currency pairs.

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.

In this article, We explore the dynamic integration of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in stock market prediction. By leveraging CNNs' ability to extract patterns and RNNs' proficiency in handling sequential data. Let us see how this powerful combination can enhance the accuracy and efficiency of trading algorithms.

This article teaches you how to create an Expert Advisor that detects support and resistance zones drawn on the chart and executes trades automatically based on them.

In this article, I will show a completely different approach to algorithmic trading I ended up with after quite a long time. Of course, all this has to do with my brute force program, which has undergone a number of changes that allow it to solve several problems simultaneously. Nevertheless, the article has turned out to be more general and as simple as possible, which is why it is also suitable for those who know nothing about brute force.

This is an article that I have written aimed to expound and explain Fair Value Gaps, their formation logic for occurring, and automated trading with breakers and market structure shifts.

In this article, we will make the final step towards the EA's performance. So, be prepared for a long read. To make our Expert Advisor reliable, we will first remove everything from the code that is not part of the trading system.

AdaBoost, a powerful boosting algorithm designed to elevate the performance of your AI models. AdaBoost, short for Adaptive Boosting, is a sophisticated ensemble learning technique that seamlessly integrates weak learners, enhancing their collective predictive strength.

Many traders have experienced this situation, often stick to their entry criteria but struggle with trade management. Even with the right setups, emotional decision-making—such as panic exits before trades reach their take-profit or stop-loss levels—can lead to a declining equity curve. How can traders overcome this issue and improve their results? This article will address these questions by examining random win-rates and demonstrating, through Monte Carlo simulation, how traders can refine their strategies by taking profits at reasonable levels before the original target is reached.

Let's continue developing a multi-currency EA with several strategies working in parallel. Let's try to move all the work associated with opening market positions from the strategy level to the level of the EA managing the strategies. The strategies themselves will trade only virtually, without opening market positions.

Explore the world of MQL5 arrays in Part 5, designed for absolute beginners. Simplifying complex coding concepts, this article focuses on clarity and inclusivity. Join our community of learners, where questions are embraced, and knowledge is shared!

Building DLL-free cryptocurrency exchange integrations has long been a challenge, but this solution provides a complete framework for direct market connectivity.

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.

Trends are an important part of many trading strategies. In this article, we will look at some of the tools used to identify trends and their characteristics. Understanding and correctly interpreting trends can significantly improve trading efficiency and minimize risks.

In this article, we will delve into the detailed preparation of our indicator for Expert Advisor (EA) development. Our discussion will encompass further refinements to the current version of the indicator to enhance its accuracy and functionality. Additionally, we will introduce new features that mark exit points, addressing a limitation of the previous version, which only identified entry points.

Hidden Markov Models (HMMs) are powerful statistical tools that identify underlying market states by analyzing observable price movements. In trading, HMMs enhance volatility prediction and inform trend-following strategies by modeling and anticipating shifts in market regimes. In this article, we will present the complete procedure for developing a trend-following strategy that utilizes HMMs to predict volatility as a filter.

This article details building an adaptive Expert Advisor (MarketRegimeEA) using the regime detector from Part 1. It automatically switches trading strategies and risk parameters for trending, ranging, or volatile markets. Practical optimization, transition handling, and a multi-timeframe indicator are included.

Today, we take another step forward by integrating an external news API as the source of headlines for our News Headline EA. In this phase, we’ll explore various news sources—both established and emerging—and learn how to access their APIs effectively. We'll also cover methods for parsing the retrieved data into a format optimized for display within our Expert Advisor. Join the discussion as we explore the benefits of accessing news headlines and the economic calendar directly on the chart, all within a compact, non-intrusive interface.

In this article, we introduce the development of an interactive Trade Assistant Tool in MQL5, designed to simplify placing pending orders in Forex trading. We outline the conceptual design, focusing on a user-friendly GUI for setting entry, stop-loss, and take-profit levels visually on the chart. Additionally, we detail the MQL5 implementation and backtesting process to ensure the tool’s reliability, setting the stage for advanced features in the preceding parts.

Today we will take our order system to the next level. But before that, we need to solve a few problems. Now we have some questions that are related to how we want to work and what things we do during the trading day.

Think about an independent Expert Advisor. Previously, we discussed an indicator-based Expert Advisor that also partnered with an independent script for drawing risk and reward geometry. Today, we will discuss the architecture of an MQL5 Expert Advisor, that integrates, all the features in one program.

This article details creating an MQL5 Market Regime Detection System using statistical methods like autocorrelation and volatility. It provides code for classes to classify trending, ranging, and volatile conditions and a custom indicator.

In this article, we explore the fundamentals of harmonic patterns, their structures, and how they are applied in trading. You’ll learn about Fibonacci retracements, extensions, and how to implement harmonic pattern detection in MQL5, setting the foundation for building advanced trading tools and Expert Advisors.

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 implement the C_Mouse class. It provides the ability to program at the highest level. However, talking about high-level or low-level programming languages is not about including obscene words or jargon in the code. It's the other way around. When we talk about high-level or low-level programming, we mean how easy or difficult the code is for other programmers to understand.