Articles with MQL5 programming examples

This article explores optimizing RSI levels and periods for better trading signals. We introduce methods to estimate optimal RSI values and automate period selection using grid search and statistical models. Finally, we implement the solution in MQL5 while leveraging Python for analysis. Our approach aims to be pragmatic and straightforward to help you solve potentially complicated problems, with simplicity.

In this article, I will make an attempt to consider some ways of building non-linear indicators and their use in trading. There are quite a few indicators in the MetaTrader trading platform that use non-linear approaches.

Using simple examples, we will examine the principles of calculating moving averages, as well as learn about the ways to optimize indicator calculations, including moving averages.

The article considers standard indicators from the oscillator category. We will create ready-to-use templates for their use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.

This article helps new community members search for and discover their own candlestick patterns. Describing these patterns can be daunting, as it requires manually searching and creatively identifying improvements. Here, we introduce the engulfing candlestick pattern and show how it can be enhanced for more profitable trading applications.

When creating a trading strategy, we need to test a variety of protective stop options. Here is where a dynamic pulling up of the Stop Loss level following the price comes to mind. The best candidate for this is the Parabolic SAR indicator. It is difficult to think of anything simpler and visually clearer.

In this article, we will discuss how we can build Expert Advisors capable of autonomously selecting and changing trading strategies based on prevailing market conditions. We will learn about Markov Chains and how they can be helpful to us as algorithmic traders.

The multi-currency expert advisor in this article is an expert advisor or automated trading that uses ZigZag indicator which are filtered with the Awesome Oscillator or filter each other's signals.

In the previous article "Multiple indicators on one chart" I presented the concept and the basics of how to use multiple indicators on one chart. In this article, I will provide the source code and will explain it in detail.

In this article, we will learn how to make the MetaTrader 5 platform talk. What if we make the EA more fun? Financial market trading is often too boring and monotonous, but we can make this job less tiring. Please note that this project can be dangerous for those who experience problems such as addiction. However, in a general case, it just makes things less boring.

What is the easiest way to implement new functionality? In this article, we will take one step back and then two steps forward.

Moving average cross-overs are widely known by traders in our community, and yet the core of the strategy has changed very little since its inception. In this discussion, we will present you with a slight adjustment to the original strategy, that aims to minimize the lag present in the trading strategy. All fans of the original strategy, could consider revising the strategy in accordance with the insights we will discuss today. By using 2 moving averages with the same period, we reduce the lag in the trading strategy considerably, without violating the foundational principles of the strategy.

In this article, we will explore the application of all classification models available in the Scikit-Learn library to solve the classification task of Fisher's Iris dataset. We will attempt to convert these models into ONNX format and utilize the resulting models in MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions on the full Iris dataset.

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.

Financial markets are typically classified as either in a range mode or a trending mode. This static view of the market may make it easier for us to trade in the short run. However, it is disconnected from the reality of the market. In this article, we look to better understand how exactly financial markets move between these 2 possible modes and how we can use our new understanding of market behavior to gain confidence in our algorithmic trading strategies.

E. coli bacterium foraging strategy inspired scientists to create the BFO optimization algorithm. The algorithm contains original ideas and promising approaches to optimization and is worthy of further study.

This article continues the series for beginners. Here we will discuss the basic principles of developing Expert Advisors (EAs). We will create two EAs: the first one will trade without indicators, using pending orders, and the second one will be based on the standard MA indicator, opening deals at the current price. Here I assume that you are no longer a complete beginner and have a relatively good command of the material from the previous articles.

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.

Join us today as we challenge ourselves to build a profitable break-out trading strategy in MQL5. We selected the EURUSD pair and attempted to trade price breakouts on the hourly timeframe. Our system had difficulty distinguishing between false breakouts and the beginning of true trends. We layered our system with filters intended to minimize our losses whilst increasing our gains. In the end, we successfully made our system profitable and less prone to false breakouts.