We have been working on just the indicators for a long time now, but now it's time to get the service working again and see how the chart is built based on the data provided. However, since the whole thing is not that simple, we will have to be attentive to understand what awaits us ahead.
A large number of the models we have reviewed so far are based on the Transformer architecture. However, they may be inefficient when dealing with long sequences. And in this article, we will get acquainted with an alternative direction of time series forecasting based on state space models.
In our previous article, we introduced a simple script called "The Quarters Drawer." Building on that foundation, we are now taking the next step by creating a monitor Expert Advisor (EA) to track these quarters and provide oversight regarding potential market reactions at these levels. Join us as we explore the process of developing a zone detection tool in this article.
Discover how to effortlessly import and utilize the History Manager EX5 library in your MQL5 source code to process trade histories in your MetaTrader 5 account in this series' final article. With simple one-line function calls in MQL5, you can efficiently manage and analyze your trading data. Additionally, you will learn how to create different trade history analytics scripts and develop a price-based Expert Advisor as practical use-case examples. The example EA leverages price data and the History Manager EX5 library to make informed trading decisions, adjust trade volumes, and implement recovery strategies based on previously closed trades.
Do the positions of planets and stars affect financial markets? Let's arm ourselves with statistics and big data, and embark on an exciting journey into the world where stars and stock charts intersect.
Traders often face drawdowns from false signals, while waiting for confirmation can lead to missed opportunities. This article introduces a triangular trading strategy using Silver’s pricing in Dollars (XAGUSD) and Euros (XAGEUR), along with the EURUSD exchange rate, to filter out noise. By leveraging cross-market relationships, traders can uncover hidden sentiment and refine their entries in real time.
Cycles are of great importance in our lives. Day and night, seasons, days of the week and many other cycles of different nature are present in the life of any person. In this article, we will consider cycles in financial markets.
The Fractals by Bill Williams is a potent indicator that is easy to overlook when one initially spots it on a price chart. It appears too busy and probably not incisive enough. We aim to draw away this curtain on this indicator by examining what its various patterns could accomplish when examined with forward walk tests on all, with wizard assembled Expert Advisor.
This article guides you through building a custom Heikin Ashi indicator from scratch and demonstrates how to integrate custom indicators into an EA. It covers indicator calculations, trade execution logic, and risk management techniques to enhance automated trading strategies.
The article considers the Artificial Algae Algorithm (AAA) based on biological processes characteristic of microalgae. The algorithm includes spiral motion, evolutionary process and adaptation, which allows it to solve optimization problems. The article provides an in-depth analysis of the working principles of AAA and its potential in mathematical modeling, highlighting the connection between nature and algorithmic solutions.
In this article we will build a basic neuron. And although it looks simple, and many may consider this code completely trivial and meaningless, I want you to have fun studying this simple sketch of a neuron. Don't be afraid to modify the code, understanding it fully is the goal.
In this article, we develop an Expert Advisor in MQL5 for the Trend Flat Momentum Strategy. We combine a two moving averages crossover with RSI and CCI momentum filters to generate trade signals. We also cover backtesting and potential enhancements for real-world performance.
The Kalman filter is a recursive algorithm used in algorithmic trading to estimate the true state of a financial time series by filtering out noise from price movements. It dynamically updates predictions based on new market data, making it valuable for adaptive strategies like mean reversion. This article first introduces the Kalman filter, covering its calculation and implementation. Next, we apply the filter to a classic mean-reversion forex strategy as an example. Finally, we conduct various statistical analyses by comparing the filter with a moving average across different forex pairs.
We will create an indicator based on the Gann's Square of 9, built by squaring time and price. We will prepare the code and test the indicator in the platform on different time intervals.
In this article we will look at the main operators. Although the topic is simple to understand, there are certain points that are of great importance when it comes to including mathematical expressions in the code format. Without an adequate understanding of these details, programmers with little or no experience eventually give up trying to create their own solutions.
Points of support and resistance are critical levels that signal potential trend reversals and continuations. Although identifying these levels can be challenging, once you pinpoint them, you’re well-prepared to navigate the market. For further assistance, check out the Quarters Drawer tool featured in this article, it will help you identify both primary and minor support and resistance levels.
In this article, we implement automated trade entry using the MQL5 Economic Calendar by applying user-defined filters and time offsets to identify qualifying news events. We compare forecast and previous values to determine whether to open a BUY or SELL trade. Dynamic countdown timers display the remaining time until news release and reset automatically after a trade.
In this article, we build an Expert Advisor in MQL5 for the Asian Breakout Strategy by calculating the session's high and low and applying trend filtering with a moving average. We implement dynamic object styling, user-defined time inputs, and robust risk management. Finally, we demonstrate backtesting and optimization techniques to refine the program.
In this article, we will get acquainted with the Anarchic Society Optimization (ASO) algorithm and discuss how an algorithm based on the irrational and adventurous behavior of participants in an anarchic society (an anomalous system of social interaction free from centralized power and various kinds of hierarchies) is able to explore the solution space and avoid the traps of local optimum. The article presents a unified ASO structure applicable to both continuous and discrete problems.
Replay buffers in Reinforcement Learning are particularly important with off-policy algorithms like DQN or SAC. This then puts the spotlight on the sampling process of this memory-buffer. While default options with SAC, for instance, use random selection from this buffer, Prioritized Experience Replay buffers fine tune this by sampling from the buffer based on a TD-score. We review the importance of Reinforcement Learning, and, as always, examine just this hypothesis (not the cross-validation) in a wizard assembled Expert Advisor.
In this article, we build an MQL5 Expert Advisor to detect Butterfly harmonic patterns. We identify pivot points and validate Fibonacci levels to confirm the pattern. We then visualize the pattern on the chart and automatically execute trades when confirmed.
Most modern multimodal time series forecasting methods use the independent channels approach. This ignores the natural dependence of different channels of the same time series. Smart use of two approaches (independent and mixed channels) is the key to improving the performance of the models.
Join us in our discussion today as we look for an algorithmic procedure to minimize the total number of times we get stopped out of winning trades. The problem we faced is significantly challenging, and most solutions given in community discussions lack set and fixed rules. Our algorithmic approach to solving the problem increased the profitability of our trades and reduced our average loss per trade. However, there are further advancements to be made to completely filter out all trades that will be stopped out, our solution is a good first step for anyone to try.
We continue our acquaintance with the TEMPO method. In this article we will evaluate the actual effectiveness of the proposed approaches on real historical data.
Embracing technical indicators in price action analysis is a powerful approach. These indicators often highlight key levels of reversals and retracements, offering valuable insights into market dynamics. In this article, we demonstrate how we developed an automated tool that generates signals using the Parabolic SAR indicator.
The article is a continuation of the series for beginners. It covers in detail data arrays, the interaction of data and functions, as well as global terminal variables that allow data exchange between different MQL5 programs.
The article is devoted to the AMO algorithm, which models the seasonal migration of animals in search of optimal conditions for life and reproduction. The main features of AMO include the use of topological neighborhood and a probabilistic update mechanism, which makes it easy to implement and flexible for various optimization tasks.
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.
Price action can be effectively analyzed by identifying divergences, with technical indicators such as the RSI providing crucial confirmation signals. In the article below, we explain how automated RSI divergence analysis can identify trend continuations and reversals, thereby offering valuable insights into market sentiment.
In this discussion, we take a step further in breaking down our MQL5 program into smaller, more manageable modules. These modular components will then be integrated into the main program, enhancing its organization and maintainability. This approach simplifies the structure of our main program and makes the individual components reusable in other Expert Advisors (EAs) and indicator developments. By adopting this modular design, we create a solid foundation for future enhancements, benefiting both our project and the broader developer community.
Having a proper understanding of different ideas allows us to do more with less effort. In this article, we'll look at why it's necessary to configure a template before the service can interact with the chart. Also, what if we improve the mouse pointer so we can do more things with it?
Today we will look at how to use predefined MQL5 language variables and constants. In addition, we will analyze another special type of variables: functions. Knowing how to properly work with these variables can mean the difference between an application that works and one that doesn't. In order to understand what is presented here, it is necessary to understand the material that was discussed in previous articles.
Soft Actor Critic is a Reinforcement Learning algorithm that we looked at in a previous article, where we also introduced python and ONNX to these series as efficient approaches to training networks. We revisit the algorithm with the aim of exploiting tensors, computational graphs that are often exploited in Python.
In this article, we build a Keltner Channel indicator with custom canvas graphics in MQL5. We detail the integration of moving averages, ATR calculations, and enhanced chart visualization. We also cover backtesting to evaluate the indicator’s performance for practical trading insights.
Experience a step-by-step guide on creating a custom JSON parser in MQL5, complete with object and array handling, error checking, and serialization. Gain practical insights into bridging your trading logic and structured data with this flexible solution for handling JSON in MetaTrader 5.
We continue to study time series forecasting models. In this article, we get acquainted with a complex algorithm built on the use of a pre-trained language model.
In strategy development, there are many intricate details to consider, many of which are not highlighted for beginner traders. As a result, many traders, myself included, have had to learn these lessons the hard way. This article is based on my observations of common pitfalls that most beginner traders encounter when developing strategies on MQL5. It will offer a range of tips, tricks, and examples to help identify the disqualification of an EA and test the robustness of our own EAs in an easy-to-implement way. The goal is to educate readers, helping them avoid future scams when purchasing EAs as well as preventing mistakes in their own strategy development.
This article improves the logging library by adding formatters in handlers, the CIntervalWatcher class to manage execution cycles, optimization with caching and file rotation, performance tests and practical examples. With these improvements, we ensure an efficient, scalable and adaptable logging system to different development scenarios.
