Articles on trading system automation in MQL5

We continue the development of a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. In the tenth part, we resumed our work on the library compatibility with MQL4 and defined the events of opening positions and activating pending orders. In this article, we will define the events of closing positions and get rid of the unused order properties.

In the first part, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. Further on, we implemented the collection of history orders and deals. Our next step is creating a class for a convenient selection and sorting of orders, deals and positions in collection lists. We are going to implement the base library object called Engine and add collection of market orders and positions to the library.

Finding rules for a trade system and programming them in an Expert Advisor is a half of the job. Somehow, you need to correct the operation of the Expert Advisor as it accumulates the results of trading. This article describes one of approaches, which allows improving performance of an Expert Advisor through creation of a feedback that measures slope of the balance curve.

In this article, we create an interactive trading dashboard using the Controls class in MQL5, designed to streamline trading operations. The panel features a title, navigation buttons for Trade, Close, and Information, and specialized action buttons for executing trades and managing positions. By the end of the article, you will have a foundational panel ready for further enhancements in future installments.

The MetaTrader 5 platform allows developing and testing trading robots that simultaneously trade multiple financial instruments. The built-in Strategy Tester automatically downloads required tick history from the broker's server taking into account contract specifications, so the developer does not need to do anything manually. This makes it possible to easily and reliably reproduce trading environment conditions, including even millisecond intervals between the arrival of ticks on different symbols. In this article we will demonstrate the development and testing of a spread strategy on two Moscow Exchange futures.

In this article, we will consider combining the lists of bar objects for each used symbol period into a single symbol timeseries object. Thus, each symbol will have an object storing the lists of all used symbol timeseries periods.

The article considers the methodology for developing trading algorithms, in which a consistent scientific approach is used to analyze possible price patterns and to build trading algorithms based on these patterns. Development ideals are demonstrated using examples.

In this article, I will create two classes (the class of DOM snapshot object and the class of DOM snapshot series object) and test creation of the DOM data series.

Volatility based breakout system identifies market ranges, then trades when price breaks above or below those levels, filtered by volatility measures such as ATR. This approach helps capture strong directional moves.

In this article, we develop a Trendline Breakout System in MQL5 that identifies support and resistance trendlines using swing points, validated by R-squared goodness of fit and angle constraints, to automate breakout trades. Our plan is to detect swing highs and lows within a specified lookback period, construct trendlines with a minimum number of touch points, and validate them using R-squared metrics and angle constraints to ensure reliability.

In the previous articles of this series, we have seen two reinforced learning algorithms. Each of them has its own advantages and disadvantages. As often happens in such cases, next comes the idea to combine both methods into an algorithm, using the best of the two. This would compensate for the shortcomings of each of them. One of such methods will be discussed in this article.

We have earlier discussed some types of neural network implementations. In the considered networks, the same operations are repeated for each neuron. A logical further step is to utilize multithreaded computing capabilities provided by modern technology in an effort to speed up the neural network learning process. One of the possible implementations is described in this article.

Trading Signals service recently introduced in MetaTrader 5 allows traders to copy trading operations of any signals provider. Users can select any signal, subscribe to it and all deals will be copied at their accounts. Signals providers can set their subscription prices and receive a fixed monthly fee from their subscribers.

In this article, we’ve transitioned from manually coding candlestick‑pattern detection in Python to leveraging TA‑Lib, a library that recognizes over sixty distinct patterns. These formations offer valuable insights into potential market reversals and trend continuations. Follow along to learn more.

Every price action trader manually uses trendlines to confirm trends and spot potential turning or continuation levels. In this series on developing a price action analysis toolkit, we introduce a tool focused on drawing slanted trendlines for easy market analysis. This tool simplifies the process for traders by clearly outlining key trends and levels essential for effective price action evaluation.

In this article, I will show the criteria to be used when selecting a system or a signal for investing your funds, as well as describe the optimal approach to the development of trading systems and highlight the importance of this matter in Forex trading.

We continue to study reinforcement learning. In this article, we will get acquainted with the Deep Q-Learning method. The use of this method has enabled the DeepMind team to create a model that can outperform a human when playing Atari computer games. I think it will be useful to evaluate the possibilities of the technology for solving trading problems.

The article guides in demonstrating an automated algorithm based on EMA Crossovers for MetaTrader 5. Detailed information on all aspects of demonstrating an Expert Advisor in MQL5 and testing it in MetaTrader 5 - from analyzing price range behaviors to risk management.

Efficient extraction and integration of long-term dependencies and short-term features remain an important task in time series analysis. Their proper understanding and integration are necessary to create accurate and reliable predictive models.

The "area method" trading system works based on unusual interpretation of the RSI oscillator readings. The indicator that visualizes the area method, and the Expert Advisor that trades using this system are detailed here. The article is also supplemented with detailed findings of testing the Expert Advisor for various symbols, time frames and values of the area.