Algorithmic Trading Articles for Expert advisors

Introduction to MQL5 (Part 6): A Beginner's Guide to Array Functions in MQL5 (II)

Embark on the next phase of our MQL5 journey. In this insightful and beginner-friendly article, we'll look into the remaining array functions, demystifying complex concepts to empower you to craft efficient trading strategies. We’ll be discussing ArrayPrint, ArrayInsert, ArraySize, ArrayRange, ArrarRemove, ArraySwap, ArrayReverse, and ArraySort. Elevate your algorithmic trading expertise with these essential array functions. Join us on the path to MQL5 mastery!

Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)

In previous works, we always assessed the current state of the environment. At the same time, the dynamics of changes in indicators always remained "behind the scenes". In this article I want to introduce you to an algorithm that allows you to evaluate the direct change in data between 2 successive environmental states.

Building a Candlestick Trend Constraint Model (Part 8): Expert Advisor Development (I)

In this discussion, we will create our first Expert Advisor in MQL5 based on the indicator we made in the prior article. We will cover all the features required to make the process automatic, including risk management. This will extensively benefit the users to advance from manual execution of trades to automated systems.

Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

In the previous article, we got acquainted with one of the methods for detecting objects in an image. However, processing a static image is somewhat different from working with dynamic time series, such as the dynamics of the prices we analyze. In this article, we will consider the method of detecting objects in video, which is somewhat closer to the problem we are solving.

Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)

In our models, we often use various attention algorithms. And, probably, most often we use Transformers. Their main disadvantage is the resource requirement. In this article, we will consider a new algorithm that can help reduce computing costs without losing quality.

Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT)

In this article, I propose to look at the issue of building a trading strategy from a different angle. We will not predict future price movements, but will try to build a trading system based on the analysis of historical data.

Creating an MQL5-Telegram Integrated Expert Advisor (Part 2): Sending Signals from MQL5 to Telegram

In this article, we create an MQL5-Telegram integrated Expert Advisor that sends moving average crossover signals to Telegram. We detail the process of generating trading signals from moving average crossovers, implementing the necessary code in MQL5, and ensuring the integration works seamlessly. The result is a system that provides real-time trading alerts directly to your Telegram group chat.

Developing a multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status

Having started developing a multi-currency EA, we have already achieved some results and managed to carry out several code improvement iterations. However, our EA was unable to work with pending orders and resume operation after the terminal restart. Let's add these features.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor

Learn about the object-oriented programming paradigm and its application in MQL5 code. This second article goes deeper into the specifics of object-oriented programming, offering hands-on experience through a practical example. You'll learn how to convert our earlier developed procedural price action expert advisor using the EMA indicator and candlestick price data to object-oriented code.

Neural networks made easy (Part 73): AutoBots for predicting price movements

We continue to discuss algorithms for training trajectory prediction models. In this article, we will get acquainted with a method called "AutoBots".

Neural networks made easy (Part 74): Trajectory prediction with adaptation

This article introduces a fairly effective method of multi-agent trajectory forecasting, which is able to adapt to various environmental conditions.

Neural networks made easy (Part 72): Trajectory prediction in noisy environments

The quality of future state predictions plays an important role in the Goal-Conditioned Predictive Coding method, which we discussed in the previous article. In this article I want to introduce you to an algorithm that can significantly improve the prediction quality in stochastic environments, such as financial markets.

Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models

We will guide you through the entire process of DL with python to make a GRU ONNX model, culminating in the creation of an Expert Advisor (EA) designed for trading, and subsequently comparing GRU model with LSTM model.

We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.

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

This part of the article series is dedicated to integrating WhatsApp with MetaTrader 5 for notifications. We have included a flow chart to simplify understanding and will discuss the importance of security measures in integration. The primary purpose of indicators is to simplify analysis through automation, and they should include notification methods for alerting users when specific conditions are met. Discover more in this article.

Automated Parameter Optimization for Trading Strategies Using Python and MQL5

There are several types of algorithms for self-optimization of trading strategies and parameters. These algorithms are used to automatically improve trading strategies based on historical and current market data. In this article we will look at one of them with python and MQL5 examples.

Introduction to MQL5 (Part 6): A Beginner's Guide to Array Functions in MQL5 (II)

Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)

Building a Candlestick Trend Constraint Model (Part 8): Expert Advisor Development (I)

Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)

Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT)

Creating an MQL5-Telegram Integrated Expert Advisor (Part 2): Sending Signals from MQL5 to Telegram

Developing a multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status

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

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

From Novice to Expert: The Essential Journey Through MQL5 Trading

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

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

Propensity score in causal inference

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

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

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

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

Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor

Neural networks made easy (Part 73): AutoBots for predicting price movements

Neural networks made easy (Part 74): Trajectory prediction with adaptation

Neural networks made easy (Part 72): Trajectory prediction in noisy environments

Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models

Statistical Arbitrage with predictions

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

Automated Parameter Optimization for Trading Strategies Using Python and MQL5