Articles with examples of trading robots developed in MQL5

In this article, we will take a look to one the famous strategies of Bill Williams, and discuss it, and try to improve the strategy with other indicators and with predictions.

Here I will consider the fairly new Stochastic Marginal Actor-Critic (SMAC) algorithm, which allows building latent variable policies within the framework of entropy maximization.

As the model is trained based on the experience reproduction buffer, the current Actor policy moves further and further away from the stored examples, which reduces the efficiency of training the model as a whole. In this article, we will look at the algorithm of improving the efficiency of using samples in reinforcement learning algorithms.

In this article we present the implementation of several risk return metrics billed as alternatives to the Sharpe ratio and examine hypothetical equity curves to analyze their characteristics.

As a result of tests performed in previous articles, we came to the conclusion that the optimality of the trained strategy largely depends on the training set used. In this article, we will get acquainted with a fairly simple yet effective method for selecting trajectories to train models.

In this article, we will get acquainted with an interesting algorithm that is built at the intersection of supervised and reinforcement learning methods.

The quality of future state predictions plays an important role in the Goal-Conditioned Predictive Coding method, which we discussed in the previous article. In this article I want to introduce you to an algorithm that can significantly improve the prediction quality in stochastic environments, such as financial markets.

In recent articles, we have seen several options for using the Decision Transformer method. The method allows analyzing not only the current state, but also the trajectory of previous states and actions performed in them. In this article, we will focus on using this method in hierarchical models.

This article introduces a fairly effective method of multi-agent trajectory forecasting, which is able to adapt to various environmental conditions.

In this article, we will see how to develop a quality score that your Expert Advisor can display in the strategy tester. We will look at two well-known calculation methods – Van Tharp and Sunny Harris.

Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.

In this article, I propose to look at the issue of building a trading strategy from a different angle. We will not predict future price movements, but will try to build a trading system based on the analysis of historical data.

The article describes hierarchical training models that offer an effective approach to solving complex machine learning problems. Hierarchical models consist of several levels, each of which is responsible for different aspects of the task.

Imagine that you can use data that is not found in MetaTrader, you only get data from indicators by price analysis and technical analysis. Now imagine that you can access data that will take your trading power steps higher. You can multiply the power of the MetaTrader software if you mix the output of other software, macro analysis methods, and ultra-advanced tools through the ​​API data. In this article, we will teach you how to use APIs and introduce useful and valuable API data services.

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.

During the offline learning, we optimize the Agent's policy based on the training sample data. The resulting strategy gives the Agent confidence in its actions. However, such optimism is not always justified and can cause increased risks during the model operation. Today we will look at one of the methods to reduce these risks.

Having started developing a multi-currency EA, we have already achieved some results and managed to carry out several code improvement iterations. However, our EA was unable to work with pending orders and resume operation after the terminal restart. Let's add these features.

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.

In the previous article, we got acquainted with the Decision Transformer. But the complex stochastic environment of the foreign exchange market did not allow us to fully implement the potential of the presented method. In this article, I will introduce an algorithm that is aimed at improving the performance of algorithms in stochastic environments.

This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.

This article outlines the steps to create an Expert Advisor (EA) that capitalizes on price breakouts after consolidation periods. By identifying consolidation ranges and setting breakout levels, traders can automate their trading decisions based on this strategy. The Expert Advisor aims to provide clear entry and exit points while avoiding false breakouts

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!

The article discusses, from a detailed perspective, how to implement the creation of an Expert Advisor (EA) based on the trading algorithm. This helps to automate the system in the MQL5 and take control of the Daily Drawdown.

This article is a continuation in a series that look at Category Theory implementation in MQL5. In here we examine how Graph-Theory could be integrated with monoids and other data structures when developing a close-out strategy to a trading system.

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.

In this article, we will explore the capabilities of the powerful MQL5 language in drawing various indicator styles on Meta Trader 5. We will also look at scripts and how they can be used in our model.

We continue to discuss the family of Decision Transformer methods. From previous article, we have already noticed that training the transformer underlying the architecture of these methods is a rather complex task and requires a large labeled dataset for training. In this article we will look at an algorithm for using unlabeled trajectories for preliminary model training.

In the previous article, we got acquainted with one of the methods for detecting objects in an image. However, processing a static image is somewhat different from working with dynamic time series, such as the dynamics of the prices we analyze. In this article, we will consider the method of detecting objects in video, which is somewhat closer to the problem we are solving.

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 get acquainted with an algorithm that uses closed-form policy improvement operators to optimize Agent actions in offline mode.

In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.

This article explores a trading strategy that integrates Linear Discriminant Analysis (LDA) with Bollinger Bands, leveraging categorical zone predictions for strategic market entry signals.