Articles on trading system automation in MQL5

In this article, we will explore the third part of our journey in formulating a Dynamic Multi-Pair Expert Advisor (EA), focusing specifically on integrating Mean Reversion and Momentum trading strategies. We will break down how to detect and act on price deviations from the mean (Z-score), and how to measure momentum across multiple forex pairs to determine trade direction.

Layered memory approaches that mimic human cognitive processes enable the processing of complex financial data and adaptation to new signals, thereby improving the effectiveness of investment decisions in dynamic markets.

Join us in our discussion today as we look for an algorithmic procedure to minimize the total number of times we get stopped out of winning trades. The problem we faced is significantly challenging, and most solutions given in community discussions lack set and fixed rules. Our algorithmic approach to solving the problem increased the profitability of our trades and reduced our average loss per trade. However, there are further advancements to be made to completely filter out all trades that will be stopped out, our solution is a good first step for anyone to try.

In the previous article, we started exploring non-gradient optimization methods. We got acquainted with the genetic algorithm. Today, we will continue this topic and will consider another class of evolutionary algorithms.

Graphical price analysis systems are deservedly popular among traders. In this article, I am going to describe the complete Murray system, including its famous levels, as well as some other useful techniques for assessing the current price position and making a trading decision.

Revolutionize your financial market analysis with Principal Component Analysis (PCA)! Discover how this powerful technique can unlock hidden patterns in your data, uncover latent market trends, and optimize your investment strategies. In this article, we explore how PCA can provide a new lens for analyzing complex financial data, revealing insights that would be missed by traditional approaches. Find out how applying PCA to financial market data can give you a competitive edge and help you stay ahead of the curve

In this article, we will create an arbitration system that remains legal in the eyes of brokers, creates thousands of synthetic prices on the Forex market, analyzes them, and successfully trades for profit.

In this article, we develop a dynamic multi-symbol, multi-period RSI indicator dashboard in MQL5, providing traders real-time RSI values across various symbols and timeframes. The dashboard features interactive buttons, real-time updates, and color-coded indicators to help traders make informed decisions.

In this article, we develop a JSON parsing framework in MQL5 to handle data exchange for AI API integration, focusing on a JSON class for processing JSON structures. We implement methods to serialize and deserialize JSON data, supporting various data types like strings, numbers, and objects, essential for communicating with AI services like ChatGPT, enabling future AI-driven trading systems by ensuring accurate data handling and manipulation.

We have built a robust feature engineering pipeline using proper tick-based bars to eliminate data leakage and solved the critical problem of labeling with meta-labeled triple-barrier signals. This installment covers the advanced labeling technique, trend-scanning, for adaptive horizons. After covering the theory, an example shows how trend-scanning labels can be used with meta-labeling to improve on the classic moving average crossover strategy.

This article is about using cycles in trading. We will consider building a trading strategy based on cyclical models.

The MQL5 Standard Library plays a vital role in developing trading algorithms for MetaTrader 5. In this discussion series, our goal is to master its application to simplify the creation of efficient trading tools for MetaTrader 5. These tools include custom Expert Advisors, indicators, and other utilities. We begin today by developing a trend-following Expert Advisor using the CTrade, CiMA, and CiATR classes. This is an especially important topic for everyone—whether you are a beginner or an experienced developer. Join this discussion to discover more.

In this fourth part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Grid EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.

We continue our discussion of reinforcement learning algorithms for solving continuous action space problems. In this article, I will present the Soft Actor-Critic (SAC) algorithm. The main advantage of SAC is the ability to find optimal policies that not only maximize the expected reward, but also have maximum entropy (diversity) of actions.

Machine learning models are difficult to interpret and understanding why our models deviate from our expectations is critical if we want to gain any value from using such advanced techniques. Without comprehensive insight into the inner workings of our model, we might fail to spot bugs that are corrupting our model's performance, we may waste time over engineering features that aren't predictive and in the long run we risk underutilizing the power of these models. Fortunately, there is a sophisticated and well maintained all in one solution that allows us to see exactly what our model is doing underneath the hood.

The objectives of this article are to prove the necessity of using a risk manager and to implement the principles of controlled risk in algorithmic trading in a separate class, so that everyone can verify the effectiveness of the risk standardization approach in intraday trading and investing in financial markets. In this article, we will create a risk manager class for algorithmic trading. This is a logical continuation of the previous article in which we discussed the creation of a risk manager for manual trading.

K-Means clustering takes the approach to grouping data points as a process that’s initially focused on the macro view of a data set that uses random generated cluster centroids before zooming in and adjusting these centroids to accurately represent the data set. We will look at this and exploit a few of its use cases.

In this article we will create a system of cross orders. There is one type of assets that makes traders' life very difficult for traders — futures contracts. But why do they make life difficult?

We will continue to build a system for working in the FOREX market. In order to solve this problem, we must first declare the loading of ticks before loading the previous bars. This solves the problem, but at the same time forces the user to follow some structure in the configuration file, which, personally, does not make much sense to me. The reason is that by designing a program that is responsible for analyzing and executing what is in the configuration file, we can allow the user to declare the elements he needs in any order.

This article addresses common beginner questions from MQL5 forums and demonstrates practical solutions. Learn to perform essential tasks like buying and selling, obtaining candlestick prices, and managing automated trading aspects such as trade limits, trading periods, and profit/loss thresholds. Get step-by-step guidance to enhance your understanding and implementation of these concepts in MQL5.

In this article, we will introduce Sentiment Analysis and ONNX Models with Python to be used in an EA. One script runs a trained ONNX model from TensorFlow for deep learning predictions, while another fetches news headlines and quantifies sentiment using AI.

Today we will add some more resources to our EA. This interesting article can provide some new ideas and methods of presenting information. At the same time, it can assist in fixing minor flaws in your projects.

In this article, we will provide a volume-based indicator, Chaikin Money Flow (CMF) after identifying how it can be constructed, calculated, and used. We will understand how to build a custom indicator. We will share some simple strategies that can be used and then test them to understand which one is better.

The moving averages and the stochastic oscillator could be used to generate trend following trading signals. However, these signals will only be observed after the price action has occurred. We can effectively overcome this inherent lag in technical indicators using AI. This article will teach you how to create a fully autonomous AI-powered Expert Advisor in a manner that can improve any of your existing trading strategies. Even the oldest trading strategy possible can be improved.

We continue to implement the approaches proposed by the authors of the FinCon framework. FinCon is a multi-agent system based on Large Language Models (LLMs). Today, we will implement the necessary modules and conduct comprehensive testing of the model on real historical data.

Every trader's ultimate goal is profitability, which is why many set specific profit targets to achieve within a defined trading period. In this article, we will use Monte Carlo simulations to determine the optimal risk percentage per trade needed to meet trading objectives. The results will help traders assess whether their profit targets are realistic or overly ambitious. Finally, we will discuss which parameters can be adjusted to establish a practical risk percentage per trade that aligns with trading goals.