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.
The Trading Administrator's role goes beyond just Telegram communications; they can also engage in various control activities, including order management, position tracking, and interface customization. In this article, we’ll share practical insights on expanding our program to support multiple functionalities in MQL5. This update aims to overcome the current Admin Panel's limitation of focusing primarily on communication, enabling it to handle a broader range of tasks.
In this article, I would like to introduce you to an interesting trajectory prediction method developed to solve problems in the field of autonomous vehicle movements. The authors of the method combined the best elements of various architectural solutions.
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.
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.
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 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 series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.
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 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.
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.
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 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.
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.
Lightweight time series forecasting models achieve high performance using a minimum number of parameters. This, in turn, reduces the consumption of computing resources and speeds up decision-making. Despite being lightweight, such models achieve forecast quality comparable to more complex ones.
In this article, we automate order block detection in MQL5 using pure price action analysis. We define order blocks, implement their detection, and integrate automated trade execution. Finally, we backtest the strategy to evaluate its performance.
This discussion delves into the challenges encountered when working with large codebases. We will explore the best practices for code organization in MQL5 and implement a practical approach to enhance the readability and scalability of our Trading Administrator Panel source code. Additionally, we aim to develop reusable code components that can potentially benefit other developers in their algorithm development. Read on and join the conversation.
In this article, we take our second attempt to convert the changes in price levels on any market, into a corresponding change in angle. This time around, we selected a more mathematically sophisticated approach than we selected in our first attempt, and the results we obtained suggest that our change in approach may have been the right decision. Join us today, as we discuss how we can use Polar coordinates to calculate the angle formed by changes in price levels, in a meaningful way, regardless of which market you are analyzing.
One of the directions for increasing the efficiency of the model training and convergence process is the improvement of optimization methods. Adam-mini is an adaptive optimization method designed to improve on the basic Adam algorithm.
In this article we will talk about using space-time transformations to effectively predict upcoming price movement. To improve the numerical prediction accuracy in STNN, a continuous attention mechanism is proposed that allows the model to better consider important aspects of the data.
The best practices, defining how to safely us an indicator, are not always easy to follow. Quiet market conditions may surprisingly produce readings on the indicator that do not qualify as a trading signal, leading to missed opportunities for algorithmic traders. This article will suggest a potential solution to this problem, as we discuss how to build trading applications capable of adapting their trading rules to the available market data.
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.
When exploring various model architecture designs, we often devote insufficient attention to the process of model training. In this article, I aim to address this gap.
In this article, we develop a Multi-Level Zone Recovery System in MQL5 that utilizes RSI to generate trading signals. Each signal instance is dynamically added to an array structure, allowing the system to manage multiple signals simultaneously within the Zone Recovery logic. Through this approach, we demonstrate how to handle complex trade management scenarios effectively while maintaining a scalable and robust code design.
This article explores a new dimension of analysis using external libraries specifically designed for advanced analytics. These libraries, like pandas, provide powerful tools for processing and interpreting complex data, enabling traders to gain more profound insights into market dynamics. By integrating such technologies, we can bridge the gap between raw data and actionable strategies. Join us as we lay the foundation for this innovative approach and unlock the potential of combining technology with trading expertise.
This article addresses common beginner questions from MQL5 forums and demonstrates practical solutions. Learn to perform essential tasks like buying and selling, obtaining candlestick prices, and managing automated trading aspects such as trade limits, trading periods, and profit/loss thresholds. Get step-by-step guidance to enhance your understanding and implementation of these concepts in MQL5.
The article discusses, from a detailed perspective, how to implement the creation of an Expert Advisor (EA) based on the trading algorithm. This helps to automate the system in the MQL5 and take control of the Daily Drawdown.
We continue the discussion about the use of piecewise linear representation of time series, which was started in the previous article. Today we will see how to combine this method with other approaches to time series analysis to improve the price trend prediction quality.
Can chaos theory be applied to financial markets? In this article, we will consider how conventional Chaos theory and chaotic systems are different from the concept proposed by Bill Williams.
The previously developed risk manager contained only basic functionality. Let's try to consider possible ways of its development, allowing us to improve trading results without interfering with the logic of trading strategies.
This article is somewhat different from my earlier publications. In this article, we will talk about an alternative representation of time series. Piecewise linear representation of time series is a method of approximating a time series using linear functions over small intervals.
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.
As the year approaches its end, long-term traders often reflect on market history to analyze its behavior and trends, aiming to project potential future movements. In this article, we will explore the development of a long-term entry monitoring Expert Advisor (EA) using MQL5. The objective is to address the challenge of missed long-term trading opportunities caused by manual trading and the absence of automated monitoring systems. We'll use one of the most prominently traded pairs as an example to strategize and develop our solution effectively.
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.
Determining market direction can be straightforward, but knowing when to enter can be challenging. As part of the series titled "Price Action Analysis Toolkit Development", I am excited to introduce another tool that provides entry points, take profit levels, and stop loss placements. To achieve this, we have utilized the MQL5 programming language. Let’s delve into each step in this article.
Transformer architecture-based models demonstrate high efficiency, but their use is complicated by high resource costs both at the training stage and during operation. In this article, I propose to get acquainted with algorithms that allow to reduce memory usage of such models.
