Algorithmic Trading Articles for Expert advisors

Data label for time series mining (Part 4)：Interpretability Decomposition Using Label Data

This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!

Developing a Replay System (Part 31): Expert Advisor project — C_Mouse class (V)

We need a timer that can show how much time is left till the end of the replay/simulation run. This may seem at first glance to be a simple and quick solution. Many simply try to adapt and use the same system that the trading server uses. But there's one thing that many people don't consider when thinking about this solution: with replay, and even m ore with simulation, the clock works differently. All this complicates the creation of such a system.

Many traders on Moscow Exchange would like to automate their trading algorithms, but they do not know where to start. The MQL5 language offers a huge range of trading functions, and it additionally provides ready classes that help users to make their first steps in algo trading.

Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL)

In this article, we will get acquainted with an interesting algorithm that is built at the intersection of supervised and reinforcement learning methods.

Design Patterns in software development and MQL5 (Part 2): Structural Patterns

In this article, we will continue our articles about Design Patterns after learning how much this topic is more important for us as developers to develop extendable, reliable applications not only by the MQL5 programming language but others as well. We will learn about another type of Design Patterns which is the structural one to learn how to design systems by using what we have as classes to form larger structures.

Design Patterns in software development and MQL5 (Part I): Creational Patterns

There are methods that can be used to solve many problems that can be repeated. Once understand how to use these methods it can be very helpful to create your software effectively and apply the concept of DRY ((Do not Repeat Yourself). In this context, the topic of Design Patterns will serve very well because they are patterns that provide solutions to well-described and repeated problems.

Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)

We continue to discuss the family of Decision Transformer methods. From previous article, we have already noticed that training the transformer underlying the architecture of these methods is a rather complex task and requires a large labeled dataset for training. In this article we will look at an algorithm for using unlabeled trajectories for preliminary model training.

In this article, we will look at the principles of creating multi-symbol, multi-period indicators. We will also see how to access the data of such indicators from Expert Advisors and other indicators. We will consider the main features of using multi-indicators in Expert Advisors and indicators and will see how to plot them through custom indicator buffers.

Cross-validation and basics of causal inference in CatBoost models, export to ONNX format

The article proposes the method of creating bots using machine learning.

Trader-friendly stop loss and take profit

Stop loss and take profit can have a significant impact on trading results. In this article, we will look at several ways to find optimal stop order values.

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 7): ZigZag with Awesome Oscillator Indicators Signal

The multi-currency expert advisor in this article is an expert advisor or automated trading that uses ZigZag indicator which are filtered with the Awesome Oscillator or filter each other's signals.

Introduction to MQL5 (Part 2): Navigating Predefined Variables, Common Functions, and Control Flow Statements

Embark on an illuminating journey with Part Two of our MQL5 series. These articles are not just tutorials, they're doorways to an enchanted realm where programming novices and wizards alike unite. What makes this journey truly magical? Part Two of our MQL5 series stands out with its refreshing simplicity, making complex concepts accessible to all. Engage with us interactively as we answer your questions, ensuring an enriching and personalized learning experience. Let's build a community where understanding MQL5 is an adventure for everyone. Welcome to the enchantment!

Introduction to MQL5 (Part 4): Mastering Structures, Classes, and Time Functions

Unlock the secrets of MQL5 programming in our latest article! Delve into the essentials of structures, classes, and time functions, empowering your coding journey. Whether you're a beginner or an experienced developer, our guide simplifies complex concepts, providing valuable insights for mastering MQL5. Elevate your programming skills and stay ahead in the world of algorithmic trading!

Neural networks made easy (Part 62): Using Decision Transformer in hierarchical models

In recent articles, we have seen several options for using the Decision Transformer method. The method allows analyzing not only the current state, but also the trajectory of previous states and actions performed in them. In this article, we will focus on using this method in hierarchical models.

Developing a Replay System (Part 30): Expert Advisor project — C_Mouse class (IV)

Today we will learn a technique that can help us a lot in different stages of our professional life as a programmer. Often it is not the platform itself that is limited, but the knowledge of the person who talks about the limitations. This article will tell you that with common sense and creativity you can make the MetaTrader 5 platform much more interesting and versatile without resorting to creating crazy programs or anything like that, and create simple yet safe and reliable code. We will use our creativity to modify existing code without deleting or adding a single line to the source code.

Developing a Replay System (Part 29): Expert Advisor project — C_Mouse class (III)

After improving the C_Mouse class, we can focus on creating a class designed to create a completely new framework fr our analysis. We will not use inheritance or polymorphism to create this new class. Instead, we will change, or better said, add new objects to the price line. That's what we will do in this article. In the next one, we will look at how to change the analysis. All this will be done without changing the code of the C_Mouse class. Well, actually, it would be easier to achieve this using inheritance or polymorphism. However, there are other methods to achieve the same result.

Modified Grid-Hedge EA in MQL5 (Part III): Optimizing Simple Hedge Strategy (I)

In this third part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Hedge EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.

Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning

During the offline learning, we optimize the Agent's policy based on the training sample data. The resulting strategy gives the Agent confidence in its actions. However, such optimism is not always justified and can cause increased risks during the model operation. Today we will look at one of the methods to reduce these risks.

Neural networks made easy (Part 60): Online Decision Transformer (ODT)

The last two articles were devoted to the Decision Transformer method, which models action sequences in the context of an autoregressive model of desired rewards. In this article, we will look at another optimization algorithm for this method.

Neural networks are easy (Part 59): Dichotomy of Control (DoC)

In the previous article, we got acquainted with the Decision Transformer. But the complex stochastic environment of the foreign exchange market did not allow us to fully implement the potential of the presented method. In this article, I will introduce an algorithm that is aimed at improving the performance of algorithms in stochastic environments.

Neural networks made easy (Part 58): Decision Transformer (DT)

We continue to explore reinforcement learning methods. In this article, I will focus on a slightly different algorithm that considers the Agent’s policy in the paradigm of constructing a sequence of actions.

Building and testing Keltner Channel trading systems

In this article, we will try to provide trading systems using a very important concept in the financial market which is volatility. We will provide a trading system based on the Keltner Channel indicator after understanding it and how we can code it and how we can create a trading system based on a simple trading strategy and then test it on different assets.

MQL5 Wizard Techniques you should know (Part 11): Number Walls

Number Walls are a variant of Linear Shift Back Registers that prescreen sequences for predictability by checking for convergence. We look at how these ideas could be of use in MQL5.

Data label for time series mining (Part 3)：Example for using label data

This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!

In this article, we will consider pair trading, namely what its principles are and if there are any prospects for its practical application. We will also try to create a pair trading strategy.

MQL5 Wizard Techniques you should know (Part 07): Dendrograms

Data classification for purposes of analysis and forecasting is a very diverse arena within machine learning and it features a large number of approaches and methods. This piece looks at one such approach, namely Agglomerative Hierarchical Classification.

MQL5 Wizard Techniques you should know (Part 09): Pairing K-Means Clustering with Fractal Waves

K-Means clustering takes the approach to grouping data points as a process that’s initially focused on the macro view of a data set that uses random generated cluster centroids before zooming in and adjusting these centroids to accurately represent the data set. We will look at this and exploit a few of its use cases.

Neural networks made easy (Part 56): Using nuclear norm to drive research

The study of the environment in reinforcement learning is a pressing problem. We have already looked at some approaches previously. In this article, we will have a look at yet another method based on maximizing the nuclear norm. It allows agents to identify environmental states with a high degree of novelty and diversity.

Building and testing Aroon Trading Systems

In this article, we will learn how we can build an Aroon trading system after learning the basics of the indicators and the needed steps to build a trading system based on the Aroon indicator. After building this trading system, we will test it to see if it can be profitable or needs more optimization.

Neural networks made easy (Part 57): Stochastic Marginal Actor-Critic (SMAC)

Here I will consider the fairly new Stochastic Marginal Actor-Critic (SMAC) algorithm, which allows building latent variable policies within the framework of entropy maximization.

Modified Grid-Hedge EA in MQL5 (Part II): Making a Simple Grid EA

In this article, we explored the classic grid strategy, detailing its automation using an Expert Advisor in MQL5 and analyzing initial backtest results. We highlighted the strategy's need for high holding capacity and outlined plans for optimizing key parameters like distance, takeProfit, and lot sizes in future installments. The series aims to enhance trading strategy efficiency and adaptability to different market conditions.

MQL5 Wizard Techniques you should know (Part 10). The Unconventional RBM

Restrictive Boltzmann Machines are at the basic level, a two-layer neural network that is proficient at unsupervised classification through dimensionality reduction. We take its basic principles and examine if we were to re-design and train it unorthodoxly, we could get a useful signal filter.

In this article, you will find your way to deal with the most important lines like trendlines, support, and resistance by MQL5.

Data label for time series mining(Part 1)：Make a dataset with trend markers through the EA operation chart

This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!

Design Patterns in software development and MQL5 (Part 4): Behavioral Patterns 2

In this article, we will complete our series about the Design Patterns topic, we mentioned that there are three types of design patterns creational, structural, and behavioral. We will complete the remaining patterns of the behavioral type which can help set the method of interaction between objects in a way that makes our code clean.

Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)

Contrastive training is an unsupervised method of training representation. Its goal is to train a model to highlight similarities and differences in data sets. In this article, we will talk about using contrastive training approaches to explore different Actor skills.

Understanding Programming Paradigms (Part 1): A Procedural Approach to Developing a Price Action Expert Advisor

Learn about programming paradigms and their application in MQL5 code. This article explores the specifics of procedural programming, offering hands-on experience through a practical example. You'll learn how to develop a price action expert advisor using the EMA indicator and candlestick price data. Additionally, the article introduces you to the functional programming paradigm.

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 5): Bollinger Bands On Keltner Channel — Indicators Signal

The Multi-Currency Expert Advisor in this article is an Expert Advisor or Trading Robot that can trade (open orders, close orders and manage orders for example: Trailing Stop Loss and Trailing Profit) for more than one symbol pair from only one symbol chart. In this article we will use signals from two indicators, in this case Bollinger Bands® on Keltner Channel.

Neural networks made easy (Part 54): Using random encoder for efficient research (RE3)

Whenever we consider reinforcement learning methods, we are faced with the issue of efficiently exploring the environment. Solving this issue often leads to complication of the algorithm and training of additional models. In this article, we will look at an alternative approach to solving this problem.

Modified Grid-Hedge EA in MQL5 (Part I): Making a Simple Hedge EA

We will be creating a simple hedge EA as a base for our more advanced Grid-Hedge EA, which will be a mixture of classic grid and classic hedge strategies. By the end of this article, you will know how to create a simple hedge strategy, and you will also get to know what people say about whether this strategy is truly 100% profitable.

Data label for time series mining (Part 4)：Interpretability Decomposition Using Label Data

Developing a Replay System (Part 31): Expert Advisor project — C_Mouse class (V)

Creating a trading robot for Moscow Exchange. Where to start?

Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL)

Design Patterns in software development and MQL5 (Part 2): Structural Patterns

Design Patterns in software development and MQL5 (Part I): Creational Patterns

Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)

Creating multi-symbol, multi-period indicators

Cross-validation and basics of causal inference in CatBoost models, export to ONNX format

Trader-friendly stop loss and take profit

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 7): ZigZag with Awesome Oscillator Indicators Signal

Introduction to MQL5 (Part 2): Navigating Predefined Variables, Common Functions, and Control Flow Statements

Introduction to MQL5 (Part 4): Mastering Structures, Classes, and Time Functions

Neural networks made easy (Part 62): Using Decision Transformer in hierarchical models

Developing a Replay System (Part 30): Expert Advisor project — C_Mouse class (IV)

Developing a Replay System (Part 29): Expert Advisor project — C_Mouse class (III)

Modified Grid-Hedge EA in MQL5 (Part III): Optimizing Simple Hedge Strategy (I)

Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning

Neural networks made easy (Part 60): Online Decision Transformer (ODT)

Neural networks are easy (Part 59): Dichotomy of Control (DoC)

Neural networks made easy (Part 58): Decision Transformer (DT)

Building and testing Keltner Channel trading systems

MQL5 Wizard Techniques you should know (Part 11): Number Walls

Data label for time series mining (Part 3)：Example for using label data

Pair trading

MQL5 Wizard Techniques you should know (Part 07): Dendrograms

MQL5 Wizard Techniques you should know (Part 09): Pairing K-Means Clustering with Fractal Waves

Neural networks made easy (Part 56): Using nuclear norm to drive research

Building and testing Aroon Trading Systems

Neural networks made easy (Part 57): Stochastic Marginal Actor-Critic (SMAC)

Modified Grid-Hedge EA in MQL5 (Part II): Making a Simple Grid EA

MQL5 Wizard Techniques you should know (Part 10). The Unconventional RBM

How to deal with lines using MQL5

Data label for time series mining(Part 1)：Make a dataset with trend markers through the EA operation chart

Design Patterns in software development and MQL5 (Part 4): Behavioral Patterns 2

Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)

Understanding Programming Paradigms (Part 1): A Procedural Approach to Developing a Price Action Expert Advisor

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 5): Bollinger Bands On Keltner Channel — Indicators Signal

Neural networks made easy (Part 54): Using random encoder for efficient research (RE3)

Modified Grid-Hedge EA in MQL5 (Part I): Making a Simple Hedge EA