Articles with examples of trading robots developed in MQL5

This time we are going to create a multi-currency Expert Advisor with a trading algorithm based on work with the pending orders Buy Stop and Sell Stop. This article considers the following matters: trading in a specified time range, placing/modifying/deleting pending orders, checking if the last position was closed at Take Profit or Stop Loss and control of the deals history for each symbol.

The previous articles contain the implementation of the classes for creating constituent parts of the main menu. Now, it is time to take a close look at the event handlers in the principle base classes and in the classes of the created controls. We will also pay special attention to managing the state of the chart depending on the location of the mouse cursor.

Today we will learn how to link an MetaTrader 5 terminal with Twitter so that you can tweet your EAs' trading signals. We are developing a Social Decision Support System in PHP based on a RESTful web service. This idea comes from a particular conception of automatic trading called computer-assisted trading. We want the cognitive abilities of human traders to filter those trading signals which otherwise would be automatically placed on the market by the Expert Advisors.

This article explains the idea of multi-mode trading robot programming in MQL5. Every mode is implemented with the object-oriented approach. Instances of both mode classes hierarchy and classes for testing are provided. Multi-mode programming of trading robots is supposed to take into account all peculiarities of every operational mode of an EA written in MQL5. Functions and enumeration are created for identifying the mode.

In order to develop an expert to participate in Automated Trading Championship 2010, let's use a template of ready expert advisor. Even novice MQL5 programmer will be capable of this task, because for your strategies the basic classes, functions, templates are already developed. It's enough to write a minimal amount of code to implement your trading idea.

In the previous chapters of the eighth part of the series, our library has been reinforced by several classes for developing mouse pointers, calendars and tree views. The current article deals with the file navigator control that can also be used as part of an MQL application graphical interface.

In the article, I will start developing the functionality for working with the Depth of Market. I will also create the class of the Depth of Market abstract order object and its descendants.

An automated system will not be successful without proper security. However, security will not be ensured without a good understanding of certain things. In this article, we will explore why achieving maximum security in automated systems is such a challenge.

Create a trading journal using MetaTrader and Google Sheets! You will learn how to sync your trading data via HTTP POST and retrieve it using HTTP requests. In the end, You have a trading journal that will help you keep track of your trades effectively and efficiently.

In the article "Dr. Tradelove..." we created an Expert Advisor, which independently optimizes parameters of a pre-selected trading system. Moreover, we decided to create an Expert Advisor that can not only optimize parameters of one trading system underlying the EA, but also select the best one of several trading systems. Let's see what can come of it...

The article regards spindle chart plotting and its usage in trading strategies and experts. First let's discuss the chart's appearance, plotting and connection with japanese candlestick chart. Next we analyze the indicator's implementation in the source code in the MQL5 language. Let's test the expert based on indicator and formulate the trading strategy.

As the library grows, its code must be optimized again in order to reduce its size. The version of the library described in this article has become even more object-oriented. This made the code easier to learn. A detailed description of the latest changes will allow the readers to develop the library independently based on their own needs.

If you think automated systems are simple, then you probably don't fully understand what it takes to create them. In this article, we will talk about the problem that kills a lot of Expert Advisors. The indiscriminate triggering of orders is a possible solution to this problem.

The previous chapter of part VIII on graphical interfaces has focused on the elements of static and drop-down calendar. The second chapter will be dedicated to an equally complex element — a tree view, that is included in every complete library used for creating graphical interfaces. A tree view implemented in this article contains multiple flexible settings and modes, thus allowing to adjust this element of control to your needs.

In this article, we explore how to use the MQL5 Economic Calendar for trading by first understanding its core functionalities. We then implement key functions of the Economic Calendar in MQL5 to extract relevant news data for trading decisions. Finally, we conclude by showcasing how to utilize this information to enhance trading strategies effectively.

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. Our EA in its current state can work in any situation but it is not yet ready for automation. We still have to work on a few points.

In this article, we will look at the principles of creating multi-symbol, multi-period indicators. We will also see how to access the data of such indicators from Expert Advisors and other indicators. We will consider the main features of using multi-indicators in Expert Advisors and indicators and will see how to plot them through custom indicator buffers.

In this article, we enhance our Zone Recovery System by introducing trailing stops and multi-basket trading capabilities. We explore how the improved architecture uses dynamic trailing stops to lock in profits and a basket management system to handle multiple trade signals efficiently. Through implementation and backtesting, we demonstrate a more robust trading system tailored for adaptive market performance.

In this article, we will discuss the implementation of MQL5 in partnership with Python to perform broker-related operations. Imagine having a continuously running Expert Advisor (EA) hosted on a VPS, executing trades on your behalf. At some point, the ability of the EA to manage funds becomes paramount. This includes operations such as topping up your trading account and initiating withdrawals. In this discussion, we will shed light on the advantages and practical implementation of these features, ensuring seamless integration of fund management into your trading strategy. Stay tuned!

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. In the previous article, we started to develop an order system that we will use in our automated EA. However, we have created only one of the necessary functions.

In this article, we create a multi-symbol trading strategy using CCI and AO indicators to detect trend reversals. We cover its design, MQL5 implementation, and backtesting process. The article concludes with tips for performance improvement.

Liquidity zones are commonly traded by waiting for the price to return and retest the zone of interest, often through the placement of pending orders within these areas. In this article, we leverage MQL5 to bring this concept to life, demonstrating how such zones can be identified programmatically and how risk management can be systematically applied. Join the discussion as we explore both the logic behind liquidity-based trading and its practical implementation.