Articles with examples of trading robots developed in MQL5

In this article, I will continue the development of the chart object class. I will add the list of chart window objects featuring the lists of available indicators.

In this article, we develop a ChatGPT-integrated program in MQL5 with a user interface, leveraging the JSON parsing framework from Part 1 to send prompts to OpenAI’s API and display responses on a MetaTrader 5 chart. We implement a dashboard with an input field, submit button, and response display, handling API communication and text wrapping for user interaction.

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode.

This article describes an example of "Snake" game programming. In MQL5, the game programming became possible primarily due to event handling features. The object-oriented programming greatly simplifies this process. In this article, you will learn the event processing features, the examples of use of the Standard MQL5 Library classes and details of periodic function calls.

In this article, I will create the functionality for tracking some chart object events — adding/removing symbol charts and chart subwindows, as well as adding/removing/changing indicators in chart windows.

The article considers creation of the custom indicator object for the use in EAs. Let’s slightly improve library classes and add methods to get data from indicator objects in EAs.

In this article, we will see how to use the event handling system to quickly and efficiently process issues related to the order system. With this system the EA will work faster, so that it will not have to constantly search for the required data.

Although the creation of an automated EA is not a very difficult task, however, many mistakes can be made without the necessary knowledge. In this article, we will look at how to build the first level of automation, which consists in creating a trigger to activate breakeven and a trailing stop level.

In this article, we create several classes to facilitate real-time communication between MQL5 and Telegram. We focus on retrieving commands from Telegram, decoding and interpreting them, and sending appropriate responses back. By the end, we ensure that these interactions are effectively tested and operational within the trading environment

Whatever trading strategy you use, there will always be a question of what parameters to choose to ensure future profits. This article gives an example of an Expert Advisor with a possibility to optimize multiple symbol parameters at the same time. This method is intended to reduce the effect of overfitting parameters and handle situations where data from a single symbol are not enough for the study.

The article considers the development of an MQL5 Expert Advisor (EA) for MetaTrader 5 aimed at working on MOEX. The EA is to follow a grid strategy while trading on MOEX using MetaTrader 5 terminal. The EA involves closing positions by stop loss and take profit, as well as removing pending orders in case of certain market conditions.

Since a program may use different symbols in its work, a separate list should be created for each of them. In this article, I will combine such lists into a tick data collection. In fact, this will be a regular list based on the class of dynamic array of pointers to instances of CObject class and its descendants of the Standard library.

Do you know what a flowchart is? Can you use it? Do you think flowcharts are for beginners? I suggest that we proceed to this new article and learn how to work with flowcharts.

In this article, we design a custom MQL5 toolkit for easy manual backtesting in the Strategy Tester. We explain its design and implementation, focusing on interactive trade controls. We then show how to use it to test strategies effectively

There is currently a fair number of means for a comfortable remote monitoring of a trading account: mobile terminals, push notifications, working with ICQ. But it all requires Internet connection. This article describes the process of creating an Expert Advisor that will allow you to stay in touch with your trading terminal even when mobile Internet is not available, through calls and text messaging.

Today we will take a look at my first arbitrage robot — a liquidity provider (if you can call it that) for synthetic assets. Currently, this bot is successfully operating as a module in a large machine learning system, but I pulled up an old Forex arbitrage robot from the cloud, so let's take a look at it and think about what we can do with it today.

We continue to study reinforcement learning methods. In the previous article, we got acquainted with the Deep Q-Learning method. In this method, the model is trained to predict the upcoming reward depending on the action taken in a particular situation. Then, an action is performed in accordance with the policy and the expected reward. But it is not always possible to approximate the Q-function. Sometimes its approximation does not generate the desired result. In such cases, approximation methods are applied not to utility functions, but to a direct policy (strategy) of actions. One of such methods is Policy Gradient.

In this article, we will put into practice all the knowledge from this series. We will finally build a 100% automated and functional system. But before that, we still have to learn one last detail.