Algorithmic Trading Articles — Guides & Examples

Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA

This article presents a sophisticated Expert Advisor for forex trading, combining machine learning with technical analysis. It focuses on trading Apple stock, featuring adaptive optimization, risk management, and multiple strategies. Backtesting shows promising results with high profitability but also significant drawdowns, indicating potential for further refinement.

Developing a multi-currency Expert Advisor (Part 6): Automating the selection of an instance group

After optimizing the trading strategy, we receive sets of parameters. We can use them to create several instances of trading strategies combined in one EA. Previously, we did this manually. Here we will try to automate this process.

The Group Method of Data Handling: Implementing the Multilayered Iterative Algorithm in MQL5

In this article we describe the implementation of the Multilayered Iterative Algorithm of the Group Method of Data Handling in MQL5.

Building A Candlestick Trend Constraint Model (Part 6): All in one integration

One major challenge is managing multiple chart windows of the same pair running the same program with different features. Let's discuss how to consolidate several integrations into one main program. Additionally, we will share insights on configuring the program to print to a journal and commenting on the successful signal broadcast on the chart interface. Find more information in this article as we progress the article series.

Learn how you can get ahead of any market you wish to trade, regardless of your current level of skill.

DoEasy. Service functions (Part 2): Inside Bar pattern

In this article, we will continue to look at price patterns in the DoEasy library. We will also create the Inside Bar pattern class of the Price Action formations.

Time series clustering in causal inference

Clustering algorithms in machine learning are important unsupervised learning algorithms that can divide the original data into groups with similar observations. By using these groups, you can analyze the market for a specific cluster, search for the most stable clusters using new data, and make causal inferences. The article proposes an original method for time series clustering in Python.

Neural networks made easy (Part 82): Ordinary Differential Equation models (NeuralODE)

In this article, we will discuss another type of models that are aimed at studying the dynamics of the environmental state.

MQL5 Wizard Techniques you should know (Part 30): Spotlight on Batch-Normalization in Machine Learning

Batch normalization is the pre-processing of data before it is fed into a machine learning algorithm, like a neural network. This is always done while being mindful of the type of Activation to be used by the algorithm. We therefore explore the different approaches that one can take in reaping the benefits of this, with the help of a wizard assembled Expert Advisor.

Price-Driven CGI Model: Advanced Data Post-Processing and Implementation

In this article, we will explore the development of a fully customizable Price Data export script using MQL5, marking new advancements in the simulation of the Price Man CGI Model. We have implemented advanced refinement techniques to ensure that the data is user-friendly and optimized for animation purposes. Additionally, we will uncover the capabilities of Blender 3D in effectively working with and visualizing price data, demonstrating its potential for creating dynamic and engaging animations.

Integrating MQL5 with data processing packages (Part 1): Advanced Data analysis and Statistical Processing

Integration enables seamless workflow where raw financial data from MQL5 can be imported into data processing packages like Jupyter Lab for advanced analysis including statistical testing.

From Novice to Expert: The Essential Journey Through MQL5 Trading

Unlock your potential! You're surrounded by opportunities. Discover 3 top secrets to kickstart your MQL5 journey or take it to the next level. Let's dive into discussion of tips and tricks for beginners and pros alike.

Developing a Replay System (Part 40): Starting the second phase (I)

Today we'll talk about the new phase of the replay/simulator system. At this stage, the conversation will become truly interesting and quite rich in content. I strongly recommend that you read the article carefully and use the links provided in it. This will help you understand the content better.

Data Science and ML (Part 28): Predicting Multiple Futures for EURUSD, Using AI

It is a common practice for many Artificial Intelligence models to predict a single future value. However, in this article, we will delve into the powerful technique of using machine learning models to predict multiple future values. This approach, known as multistep forecasting, allows us to predict not only tomorrow's closing price but also the day after tomorrow's and beyond. By mastering multistep forecasting, traders and data scientists can gain deeper insights and make more informed decisions, significantly enhancing their predictive capabilities and strategic planning.

Mastering Market Dynamics: Creating a Support and Resistance Strategy Expert Advisor (EA)

A comprehensive guide to developing an automated trading algorithm based on the Support and Resistance strategy. Detailed information on all aspects of creating an expert advisor in MQL5 and testing it in MetaTrader 5 – from analyzing price range behaviors to risk management.

MQL5 Wizard Techniques you should know (Part 23): CNNs

Convolutional Neural Networks are another machine learning algorithm that tend to specialize in decomposing multi-dimensioned data sets into key constituent parts. We look at how this is typically achieved and explore a possible application for traders in another MQL5 wizard signal class.

Developing Zone Recovery Martingale strategy in MQL5

The article discusses, in a detailed perspective, the steps that need to be implemented towards the creation of an expert advisor based on the Zone Recovery trading algorithm. This helps aotomate the system saving time for algotraders.

Developing a multi-currency Expert Advisor (Part 5): Variable position sizes

In the previous parts, the Expert Advisor (EA) under development was able to use only a fixed position size for trading. This is acceptable for testing, but is not advisable when trading on a real account. Let's make it possible to trade using variable position sizes.

Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5

In this article, we develop a dynamic multi-symbol, multi-period RSI indicator dashboard in MQL5, providing traders real-time RSI values across various symbols and timeframes. The dashboard features interactive buttons, real-time updates, and color-coded indicators to help traders make informed decisions.

Practicing the development of trading strategies

In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.

Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part II)

Today, we are discussing a working Telegram integration for MetaTrader 5 Indicator notifications using the power of MQL5, in partnership with Python and the Telegram Bot API. We will explain everything in detail so that no one misses any point. By the end of this project, you will have gained valuable insights to apply in your projects.

Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.

Building A Candlestick Trend Constraint Model (Part 7): Refining our model for EA development

In this article, we will delve into the detailed preparation of our indicator for Expert Advisor (EA) development. Our discussion will encompass further refinements to the current version of the indicator to enhance its accuracy and functionality. Additionally, we will introduce new features that mark exit points, addressing a limitation of the previous version, which only identified entry points.

Causal analysis of time series using transfer entropy

In this article, we discuss how statistical causality can be applied to identify predictive variables. We will explore the link between causality and transfer entropy, as well as present MQL5 code for detecting directional transfers of information between two variables.

Seasonality Filtering and time period for Deep Learning ONNX models with python for EA

Can we benefit from seasonality when creating models for Deep Learning with Python? Does filtering data for the ONNX models help to get better results? What time period should we use? We will cover all of this over this article.

The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.

DoEasy. Service functions (Part 1): Price patterns

In this article, we will start developing methods for searching for price patterns using timeseries data. A pattern has a certain set of parameters, common to any type of patterns. All data of this kind will be concentrated in the object class of the base abstract pattern. In the current article, we will create an abstract pattern class and a Pin Bar pattern class.

Introduction to MQL5 (Part 5): A Beginner's Guide to Array Functions in MQL5

Explore the world of MQL5 arrays in Part 5, designed for absolute beginners. Simplifying complex coding concepts, this article focuses on clarity and inclusivity. Join our community of learners, where questions are embraced, and knowledge is shared!

Data Science and Machine Learning (Part 21): Unlocking Neural Networks, Optimization algorithms demystified

Dive into the heart of neural networks as we demystify the optimization algorithms used inside the neural network. In this article, discover the key techniques that unlock the full potential of neural networks, propelling your models to new heights of accuracy and efficiency.

Multibot in MetaTrader (Part II): Improved dynamic template

Developing the theme of the previous article, I decided to create a more flexible and functional template that has greater capabilities and can be effectively used both in freelancing and as a base for developing multi-currency and multi-period EAs with the ability to integrate with external solutions.

MQL5 Wizard Techniques you should know (Part 27): Moving Averages and the Angle of Attack

The Angle of Attack is an often-quoted metric whose steepness is understood to strongly correlate with the strength of a prevailing trend. We look at how it is commonly used and understood and examine if there are changes that could be introduced in how it's measured for the benefit of a trade system that puts it in use.

Hybridization of population algorithms. Sequential and parallel structures

Here we will dive into the world of hybridization of optimization algorithms by looking at three key types: strategy mixing, sequential and parallel hybridization. We will conduct a series of experiments combining and testing relevant optimization algorithms.

Reimagining Classic Strategies (Part II): Bollinger Bands Breakouts

This article explores a trading strategy that integrates Linear Discriminant Analysis (LDA) with Bollinger Bands, leveraging categorical zone predictions for strategic market entry signals.

Combine Fundamental And Technical Analysis Strategies in MQL5 For Beginners

In this article, we will discuss how to integrate trend following and fundamental principles seamlessly into one Expert Advisors to build a strategy that is more robust. This article will demonstrate how easy it is for anyone to get up and running building customized trading algorithms using MQL5.

MQL5 Wizard Techniques you should know (Part 28): GANs Revisited with a Primer on Learning Rates

The Learning Rate, is a step size towards a training target in many machine learning algorithms’ training processes. We examine the impact its many schedules and formats can have on the performance of a Generative Adversarial Network, a type of neural network that we had examined in an earlier article.

Population optimization algorithms: Resistance to getting stuck in local extrema (Part II)

We continue our experiment that aims to examine the behavior of population optimization algorithms in the context of their ability to efficiently escape local minima when population diversity is low and reach global maxima. Research results are provided.

In this article, I will introduce a very important tool for developers. If you are not familiar with GIT, read this article to get an idea of what it is and how to use it with MQL5.

Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks

In the previous article, we discussed a simple RNN which despite its inability to understand long-term dependencies in the data, was able to make a profitable strategy. In this article, we are discussing both the Long-Short Term Memory(LSTM) and the Gated Recurrent Unit(GRU). These two were introduced to overcome the shortcomings of a simple RNN and to outsmart it.

SP500 Trading Strategy in MQL5 For Beginners

Discover how to leverage MQL5 to forecast the S&P 500 with precision, blending in classical technical analysis for added stability and combining algorithms with time-tested principles for robust market insights.

Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer

This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.

Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA

Developing a multi-currency Expert Advisor (Part 6): Automating the selection of an instance group

The Group Method of Data Handling: Implementing the Multilayered Iterative Algorithm in MQL5

Building A Candlestick Trend Constraint Model (Part 6): All in one integration

Gain An Edge Over Any Market

DoEasy. Service functions (Part 2): Inside Bar pattern

Time series clustering in causal inference

Neural networks made easy (Part 82): Ordinary Differential Equation models (NeuralODE)

MQL5 Wizard Techniques you should know (Part 30): Spotlight on Batch-Normalization in Machine Learning

Price-Driven CGI Model: Advanced Data Post-Processing and Implementation

Integrating MQL5 with data processing packages (Part 1): Advanced Data analysis and Statistical Processing

From Novice to Expert: The Essential Journey Through MQL5 Trading

Developing a Replay System (Part 40): Starting the second phase (I)

Data Science and ML (Part 28): Predicting Multiple Futures for EURUSD, Using AI

Mastering Market Dynamics: Creating a Support and Resistance Strategy Expert Advisor (EA)

MQL5 Wizard Techniques you should know (Part 23): CNNs

Developing Zone Recovery Martingale strategy in MQL5

Developing a multi-currency Expert Advisor (Part 5): Variable position sizes

Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5

Practicing the development of trading strategies

Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part II)

Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU

Building A Candlestick Trend Constraint Model (Part 7): Refining our model for EA development

Causal analysis of time series using transfer entropy

Seasonality Filtering and time period for Deep Learning ONNX models with python for EA

Propensity score in causal inference

DoEasy. Service functions (Part 1): Price patterns

Introduction to MQL5 (Part 5): A Beginner's Guide to Array Functions in MQL5

Data Science and Machine Learning (Part 21): Unlocking Neural Networks, Optimization algorithms demystified

Multibot in MetaTrader (Part II): Improved dynamic template

MQL5 Wizard Techniques you should know (Part 27): Moving Averages and the Angle of Attack

Hybridization of population algorithms. Sequential and parallel structures

Reimagining Classic Strategies (Part II): Bollinger Bands Breakouts

Combine Fundamental And Technical Analysis Strategies in MQL5 For Beginners

MQL5 Wizard Techniques you should know (Part 28): GANs Revisited with a Primer on Learning Rates

Population optimization algorithms: Resistance to getting stuck in local extrema (Part II)

GIT: What is it?

Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks

SP500 Trading Strategy in MQL5 For Beginners

Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer