Articles on trading system automation in MQL5

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 create an Expert Advisor (EA) in MQL5 to send messages to Telegram using a bot. We set up the necessary parameters, including the bot's API token and chat ID, and then perform an HTTP POST request to deliver the messages. Later, we handle the response to ensure successful delivery and troubleshoot any issues that arise in case of failure. This ensures we send messages from MQL5 to Telegram via the created bot.

In this article, you will find your way to deal with the most important lines like trendlines, support, and resistance by MQL5.

The MetaTrader 5 module offered in Python provides a convenient way of opening trades in the MetaTrader 5 app using Python, but it has a huge problem, it doesn't have the strategy tester capability present in the MetaTrader 5 app, In this article series, we will build a framework for back testing your trading strategies in Python environments.

Finally, the visual system will start working, although it will not yet be completed. Here we will finish making the main changes. There will be quite a few of them, but they are all necessary. Well, the whole work will be quite interesting.

The Multi-Currency Expert Advisor in this article is Expert Advisor or trading robot that can trade (open orders, close orders and manage orders for example: Trailing Stop Loss and Trailing Profit) for more than 1 symbol pair only from one symbol chart. This time we will use only 1 indicator, namely Parabolic SAR or iSAR in multi-timeframes starting from PERIOD_M15 to PERIOD_D1.

Successfully employing algorithmic trading requires continuous, interdisciplinary learning. However, the infinite range of possibilities can consume years of effort without yielding tangible results. To address this, we propose a framework that gradually introduces complexity, allowing traders to refine their strategies iteratively rather than committing indefinite time to uncertain outcomes.

Any Program in any programming language has a specific structure. In this article, you will learn essential parts of the MQL5 program structure by understanding the programming basics of every part of the MQL5 program structure that can be very helpful when creating our MQL5 trading system or trading tool that can be executable in the MetaTrader 5.

In this article, we develop the Adaptive Crossover RSI Trading Suite System, which uses 14- and 50-period moving average crossovers for signals, confirmed by a 14-period RSI filter. The system includes a trading day filter, signal arrows with annotations, and a real-time dashboard for monitoring. This approach ensures precision and adaptability in automated trading.

Welcome to a new article in our series about learning how to design a trading system by the most popular technical indicator as we will learn in this article about a new technical indicator and how we can design a trading system by it and this indicator is the Bull's Power indicator.

In my previous articles, I have explained how to create an indicator with multiple subwindows, which becomes interesting when using custom indicators. This time we will see how to add multiple windows to an Expert Advisor.

In this article, we create an MQL5 Expert Advisor based on the Daily Range Breakout strategy. We cover the strategy’s key concepts, design the EA blueprint, and implement the breakout logic in MQL5. In the end, we explore techniques for backtesting and optimizing the EA to maximize its effectiveness.

In this article, we build a grid trading expert advisor in MQL5 that uses dynamic lot scaling. We cover the strategy design, code implementation, and backtesting process. Finally, we share key insights and best practices for optimizing the automated trading system.

In my previous article, I showed you how to create a Chart Trade using MetaTrader 5 objects and thus to turn the platform into a RAD system. The system works very well, and for sure many of the readers might have thought about creating a library, which would allow having extended functionality in the proposed system. Based on this, it would be possible to develop a more intuitive Expert Advisor with a nicer and easier to use interface.

In this article, we develop a London Session Breakout System that identifies pre-London range breakouts and places pending orders with customizable trade types and risk settings. We incorporate features like trailing stops, risk-to-reward ratios, maximum drawdown limits, and a control panel for real-time monitoring and management.

This MetaTrader 5 Expert Advisor implements a Scalping OrderFlow strategy with advanced risk management. It uses multiple technical indicators to identify trading opportunities based on order flow imbalances. Backtesting shows potential profitability but highlights the need for further optimization, especially in risk management and trade outcome ratios. Suitable for experienced traders, it requires thorough testing and understanding before live deployment.

This is the first part of the new order system. Since we started documenting this EA in our articles, it has undergone various changes and improvements while maintaining the same on-chart order system model.

In this article, we automate the Midnight Range Breakout with Break of Structure strategy in MQL5, detailing code for breakout detection and trade execution. We define precise risk parameters for entries, stops, and profits. Backtesting and optimization are included for practical trading.

In this article, we will try to provide trading systems using a very important concept in the financial market which is volatility. We will provide a trading system based on the Keltner Channel indicator after understanding it and how we can code it and how we can create a trading system based on a simple trading strategy and then test it on different assets.