Algorithmic Trading Articles for Experts

Trading with the MQL5 Economic Calendar (Part 5): Enhancing the Dashboard with Responsive Controls and Filter Buttons

In this article, we create buttons for currency pair filters, importance levels, time filters, and a cancel option to improve dashboard control. These buttons are programmed to respond dynamically to user actions, allowing seamless interaction. We also automate their behavior to reflect real-time changes on the dashboard. This enhances the overall functionality, mobility, and responsiveness of the panel.

Trading Insights Through Volume: Moving Beyond OHLC Charts

Algorithmic trading system that combines volume analysis with machine learning techniques, specifically LSTM neural networks. Unlike traditional trading approaches that primarily focus on price movements, this system emphasizes volume patterns and their derivatives to predict market movements. The methodology incorporates three main components: volume derivatives analysis (first and second derivatives), LSTM predictions for volume patterns, and traditional technical indicators.

Trading with the MQL5 Economic Calendar (Part 3): Adding Currency, Importance, and Time Filters

In this article, we implement filters in the MQL5 Economic Calendar dashboard to refine news event displays by currency, importance, and time. We first establish filter criteria for each category and then integrate these into the dashboard to display only relevant events. Finally, we ensure each filter dynamically updates to provide traders with focused, real-time economic insights.

Neural Networks Made Easy (Part 94): Optimizing the Input Sequence

When working with time series, we always use the source data in their historical sequence. But is this the best option? There is an opinion that changing the sequence of the input data will improve the efficiency of the trained models. In this article I invite you to get acquainted with one of the methods for optimizing the input sequence.

Price Action Analysis Toolkit Development (Part 2): Analytical Comment Script

Aligned with our vision of simplifying price action, we are pleased to introduce another tool that can significantly enhance your market analysis and help you make well-informed decisions. This tool displays key technical indicators such as previous day's prices, significant support and resistance levels, and trading volume, while automatically generating visual cues on the chart.

Neural Networks Made Easy (Part 93): Adaptive Forecasting in Frequency and Time Domains (Final Part)

In this article, we continue the implementation of the approaches of the ATFNet model, which adaptively combines the results of 2 blocks (frequency and time) within time series forecasting.

Automating Trading Strategies in MQL5 (Part 1): The Profitunity System (Trading Chaos by Bill Williams)

In this article, we examine the Profitunity System by Bill Williams, breaking down its core components and unique approach to trading within market chaos. We guide readers through implementing the system in MQL5, focusing on automating key indicators and entry/exit signals. Finally, we test and optimize the strategy, providing insights into its performance across various market scenarios.

Neural Networks Made Easy (Part 92): Adaptive Forecasting in Frequency and Time Domains

The authors of the FreDF method experimentally confirmed the advantage of combined forecasting in the frequency and time domains. However, the use of the weight hyperparameter is not optimal for non-stationary time series. In this article, we will get acquainted with the method of adaptive combination of forecasts in frequency and time domains.

How to Implement Auto Optimization in MQL5 Expert Advisors

Step by step guide for auto optimization in MQL5 for Expert Advisors. We will cover robust optimization logic, best practices for parameter selection, and how to reconstruct strategies with back-testing. Additionally, higher-level methods like walk-forward optimization will be discussed to enhance your trading approach.

Example of CNA (Causality Network Analysis), SMOC (Stochastic Model Optimal Control) and Nash Game Theory with Deep Learning

We will add Deep Learning to those three examples that were published in previous articles and compare results with previous. The aim is to learn how to add DL to other EA.

Creating a Trading Administrator Panel in MQL5 (Part III): Enhancing the GUI with Visual Styling (I)

In this article, we will focus on visually styling the graphical user interface (GUI) of our Trading Administrator Panel using MQL5. We’ll explore various techniques and features available in MQL5 that allow for customization and optimization of the interface, ensuring it meets the needs of traders while maintaining an attractive aesthetic.

Trading with the MQL5 Economic Calendar (Part 2): Creating a News Dashboard Panel

In this article, we create a practical news dashboard panel using the MQL5 Economic Calendar to enhance our trading strategy. We begin by designing the layout, focusing on key elements like event names, importance, and timing, before moving into the setup within MQL5. Finally, we implement a filtering system to display only the most relevant news, giving traders quick access to impactful economic events.

Reimagining Classic Strategies (Part XI): Moving Average Cross Over (II)

The moving averages and the stochastic oscillator could be used to generate trend following trading signals. However, these signals will only be observed after the price action has occurred. We can effectively overcome this inherent lag in technical indicators using AI. This article will teach you how to create a fully autonomous AI-powered Expert Advisor in a manner that can improve any of your existing trading strategies. Even the oldest trading strategy possible can be improved.

Neural Networks Made Easy (Part 90): Frequency Interpolation of Time Series (FITS)

By studying the FEDformer method, we opened the door to the frequency domain of time series representation. In this new article, we will continue the topic we started. We will consider a method with which we can not only conduct an analysis, but also predict subsequent states in a particular area.

Neural networks made easy (Part 89): Frequency Enhanced Decomposition Transformer (FEDformer)

All the models we have considered so far analyze the state of the environment as a time sequence. However, the time series can also be represented in the form of frequency features. In this article, I introduce you to an algorithm that uses frequency components of a time sequence to predict future states.

News Trading Made Easy (Part 5): Performing Trades (II)

This article will expand on the trade management class to include buy-stop and sell-stop orders to trade news events and implement an expiration constraint on these orders to prevent any overnight trading. A slippage function will be embedded into the expert to try and prevent or minimize possible slippage that may occur when using stop orders in trading, especially during news events.

Trading with the MQL5 Economic Calendar (Part 1): Mastering the Functions of the MQL5 Economic Calendar

In this article, we explore how to use the MQL5 Economic Calendar for trading by first understanding its core functionalities. We then implement key functions of the Economic Calendar in MQL5 to extract relevant news data for trading decisions. Finally, we conclude by showcasing how to utilize this information to enhance trading strategies effectively.

News Trading Made Easy (Part 4): Performance Enhancement

This article will dive into methods to improve the expert's runtime in the strategy tester, the code will be written to divide news event times into hourly categories. These news event times will be accessed within their specified hour. This ensures that the EA can efficiently manage event-driven trades in both high and low-volatility environments.

Creating an MQL5-Telegram Integrated Expert Advisor (Part 3): Sending Chart Screenshots with Captions from MQL5 to Telegram

In this article, we create an MQL5 Expert Advisor that encodes chart screenshots as image data and sends them to a Telegram chat via HTTP requests. By integrating photo encoding and transmission, we enhance the existing MQL5-Telegram system with visual trading insights directly within Telegram.

Creating a Trading Administrator Panel in MQL5 (Part V): Two-Factor Authentication (2FA)

Today, we will discuss enhancing security for the Trading Administrator Panel currently under development. We will explore how to implement MQL5 in a new security strategy, integrating the Telegram API for two-factor authentication (2FA). This discussion will provide valuable insights into the application of MQL5 in reinforcing security measures. Additionally, we will examine the MathRand function, focusing on its functionality and how it can be effectively utilized within our security framework. Continue reading to discover more!

Formulating Dynamic Multi-Pair EA (Part 1): Currency Correlation and Inverse Correlation

Dynamic multi pair Expert Advisor leverages both on correlation and inverse correlation strategies to optimize trading performance. By analyzing real-time market data, it identifies and exploits the relationship between currency pairs.

How to Create an Interactive MQL5 Dashboard/Panel Using the Controls Class (Part 2): Adding Button Responsiveness

In this article, we focus on transforming our static MQL5 dashboard panel into an interactive tool by enabling button responsiveness. We explore how to automate the functionality of the GUI components, ensuring they react appropriately to user clicks. By the end of the article, we establish a dynamic interface that enhances user engagement and trading experience.

Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes

In this series of articles, we analyze classical trading strategies using modern algorithms to determine whether we can improve the strategy using AI. In today's article, we revisit a classical approach for trading the SP500 using the relationship it has with US Treasury Notes.

News Trading Made Easy (Part 3): Performing Trades

In this article, our news trading expert will begin opening trades based on the economic calendar stored in our database. In addition, we will improve the expert's graphics to display more relevant information about upcoming economic calendar events.

Creating a Trading Administrator Panel in MQL5 (Part IV): Login Security Layer

Imagine a malicious actor infiltrating the Trading Administrator room, gaining access to the computers and the Admin Panel used to communicate valuable insights to millions of traders worldwide. Such an intrusion could lead to disastrous consequences, such as the unauthorized sending of misleading messages or random clicks on buttons that trigger unintended actions. In this discussion, we will explore the security measures in MQL5 and the new security features we have implemented in our Admin Panel to safeguard against these threats. By enhancing our security protocols, we aim to protect our communication channels and maintain the trust of our global trading community. Find more insights in this article discussion.

MQL5 Trading Toolkit (Part 3): Developing a Pending Orders Management EX5 Library

Learn how to develop and implement a comprehensive pending orders EX5 library in your MQL5 code or projects. This article will show you how to create an extensive pending orders management EX5 library and guide you through importing and implementing it by building a trading panel or graphical user interface (GUI). The expert advisor orders panel will allow users to open, monitor, and delete pending orders associated with a specified magic number directly from the graphical interface on the chart window.

Reimagining Classic Strategies (Part X): Can AI Power The MACD?

Join us as we empirically analyzed the MACD indicator, to test if applying AI to a strategy, including the indicator, would yield any improvements in our accuracy on forecasting the EURUSD. We simultaneously assessed if the indicator itself is easier to predict than price, as well as if the indicator's value is predictive of future price levels. We will furnish you with the information you need to decide whether you should consider investing your time into integrating the MACD in your AI trading strategies.

How to Create an Interactive MQL5 Dashboard/Panel Using the Controls Class (Part 1): Setting Up the Panel

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.

How to develop any type of Trailing Stop and connect it to an EA

In this article, we will look at classes for convenient creation of various trailings, as well as learn how to connect a trailing stop to any EA.

Implementing the Deus EA: Automated Trading with RSI and Moving Averages in MQL5

This article outlines the steps to implement the Deus EA based on the RSI and Moving Average indicators for guiding automated trading.

How to create a trading journal with MetaTrader and Google Sheets

Create a trading journal using MetaTrader and Google Sheets! You will learn how to sync your trading data via HTTP POST and retrieve it using HTTP requests. In the end, You have a trading journal that will help you keep track of your trades effectively and efficiently.

Creating a Trading Administrator Panel in MQL5 (Part III): Extending Built-in Classes for Theme Management (II)

In this discussion, we will carefully extend the existing Dialog library to incorporate theme management logic. Furthermore, we will integrate methods for theme switching into the CDialog, CEdit, and CButton classes utilized in our Admin Panel project. Continue reading for more insightful perspectives.

From Novice to Expert: Collaborative Debugging in MQL5

Problem-solving can establish a concise routine for mastering complex skills, such as programming in MQL5. This approach allows you to concentrate on solving problems while simultaneously developing your skills. The more problems you tackle, the more advanced expertise is transferred to your brain. Personally, I believe that debugging is the most effective way to master programming. Today, we will walk through the code-cleaning process and discuss the best techniques for transforming a messy program into a clean, functional one. Read through this article and uncover valuable insights.

Creating an MQL5-Telegram Integrated Expert Advisor (Part 1): Sending Messages from MQL5 to Telegram

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.

The objectives of this article are to prove the necessity of using a risk manager and to implement the principles of controlled risk in algorithmic trading in a separate class, so that everyone can verify the effectiveness of the risk standardization approach in intraday trading and investing in financial markets. In this article, we will create a risk manager class for algorithmic trading. This is a logical continuation of the previous article in which we discussed the creation of a risk manager for manual trading.

Creating an MQL5-Telegram Integrated Expert Advisor (Part 7): Command Analysis for Indicator Automation on Charts

In this article, we explore how to integrate Telegram commands with MQL5 to automate the addition of indicators on trading charts. We cover the process of parsing user commands, executing them in MQL5, and testing the system to ensure smooth indicator-based trading

Example of new Indicator and Conditional LSTM

This article explores the development of an Expert Advisor (EA) for automated trading that combines technical analysis with deep learning predictions.

Neural Networks Made Easy (Part 88): Time-Series Dense Encoder (TiDE)

In an attempt to obtain the most accurate forecasts, researchers often complicate forecasting models. Which in turn leads to increased model training and maintenance costs. Is such an increase always justified? This article introduces an algorithm that uses the simplicity and speed of linear models and demonstrates results on par with the best models with a more complex architecture.

Data Science and Machine Learning (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal

In the fast-paced world of financial markets, separating meaningful signals from the noise is crucial for successful trading. By employing sophisticated neural network architectures, autoencoders excel at uncovering hidden patterns within market data, transforming noisy input into actionable insights. In this article, we explore how autoencoders are revolutionizing trading practices, offering traders a powerful tool to enhance decision-making and gain a competitive edge in today's dynamic markets.

Learn how to trade the Fair Value Gap (FVG)/Imbalances step-by-step: A Smart Money concept approach

A step-by-step guide to creating and implementing an automated trading algorithm in MQL5 based on the Fair Value Gap (FVG) trading strategy. A detailed tutorial on creating an expert advisor that can be useful for both beginners and experienced traders.