Algorithmic Trading Articles for Experts

Neural Networks in Trading: A Complex Trajectory Prediction Method (Traj-LLM)

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.

Neural Networks in Trading: State Space Models

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.

Introduction to MQL5 (Part 13): A Beginner's Guide to Building Custom Indicators (II)

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.

Neural Networks in Trading: Using Language Models for Time Series Forecasting

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.

Trading with the MQL5 Economic Calendar (Part 6): Automating Trade Entry with News Event Analysis and Countdown Timers

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.

Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR

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.

Automating Trading Strategies in MQL5 (Part 9): Building an Expert Advisor for the Asian Breakout Strategy

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.

Automating Trading Strategies in MQL5 (Part 8): Building an Expert Advisor with Butterfly Harmonic Patterns

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.

Neural Networks in Trading: Injection of Global Information into Independent Channels (InjectTST)

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.

Neural Networks in Trading: Practical Results of the TEMPO Method

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.

Price Action Analysis Toolkit Development (Part 14): Parabolic Stop and Reverse Tool

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.

Automating Trading Strategies in MQL5 (Part 7): Building a Grid Trading EA with Dynamic Lot Scaling

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.

Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (II): Modularization

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.

Deconstructing examples of trading strategies in the client terminal

The article uses block diagrams to examine the logic of the candlestick-based training EAs located in the Experts\Free Robots folder of the terminal.

Cascade Order Trading Strategy Based on EMA Crossovers for MetaTrader 5

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.

Neural Networks in Trading: Lightweight Models for Time Series Forecasting

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.

Automating Trading Strategies in MQL5 (Part 6): Mastering Order Block Detection for Smart Money Trading

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.

How to Integrate Smart Money Concepts (BOS) Coupled with the RSI Indicator into an EA

Smart Money Concept (Break Of Structure) coupled with the RSI Indicator to make informed automated trading decisions based on the market structure.

Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (I)

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.

Feature Engineering With Python And MQL5 (Part III): Angle Of Price (2) Polar Coordinates

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.

Neural Networks in Trading: Reducing Memory Consumption with Adam-mini Optimization

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.

Neural Networks in Trading: Spatio-Temporal Neural Network (STNN)

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.

Build Self Optimizing Expert Advisors in MQL5 (Part 5): Self Adapting Trading Rules

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.

Automating Trading Strategies in MQL5 (Part 5): Developing the Adaptive Crossover RSI Trading Suite Strategy

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.

Neural Networks Made Easy (Part 97): Training Models With MSFformer

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.

Automating Trading Strategies in MQL5 (Part 4): Building a Multi-Level Zone Recovery System

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.

Price Action Analysis Toolkit Development (Part 9): External Flow

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.

Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)

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.

Creating a Daily Drawdown Limiter EA 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.

Neural Networks in Trading: Dual-Attention-Based Trend Prediction Model

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.

Chaos theory in trading (Part 1): Introduction, application in financial markets and Lyapunov exponent

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.

Developing a multi-currency Expert Advisor (Part 14): Adaptive volume change in risk manager

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.

Neural Networks in Trading: Piecewise Linear Representation of Time Series

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.

Creating an MQL5 Expert Advisor Based on the Daily Range Breakout Strategy

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.

Developing A Swing Entries Monitoring (EA)

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.

Neural Networks Made Easy (Part 96): Multi-Scale Feature Extraction (MSFformer)

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.

Price Action Analysis Toolkit Development (Part 5): Volatility Navigator EA

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.

Neural Networks Made Easy (Part 95): Reducing Memory Consumption in Transformer Models

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.

Creating a Trading Administrator Panel in MQL5 (Part VIII): Analytics Panel

Today, we delve into incorporating useful trading metrics within a specialized window integrated into the Admin Panel EA. This discussion focuses on the implementation of MQL5 to develop an Analytics Panel and highlights the value of the data it provides to trading administrators. The impact is largely educational, as valuable lessons are drawn from the development process, benefiting both upcoming and experienced developers. This feature demonstrates the limitless opportunities this development series offers in equipping trade managers with advanced software tools. Additionally, we'll explore the implementation of the PieChart and ChartCanvas classes as part of the continued expansion of the Trading Administrator panel’s capabilities.

Creating an MQL5 Expert Advisor Based on the PIRANHA Strategy by Utilizing Bollinger Bands

In this article, we create an Expert Advisor (EA) in MQL5 based on the PIRANHA strategy, utilizing Bollinger Bands to enhance trading effectiveness. We discuss the key principles of the strategy, the coding implementation, and methods for testing and optimization. This knowledge will enable you to deploy the EA in your trading scenarios effectively