Algorithmic Trading Articles for Experts

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.

Creating an Interactive Graphical User Interface in MQL5 (Part 1): Making the Panel

This article explores the fundamental steps in crafting and implementing a Graphical User Interface (GUI) panel using MetaQuotes Language 5 (MQL5). Custom utility panels enhance user interaction in trading by simplifying common tasks and visualizing essential trading information. By creating custom panels, traders can streamline their workflow and save time during trading operations.

This article simplifies triangular arbitrage, showing you how to use predictions and specialized software to trade currencies smarter, even if you're new to the market. Ready to trade with expertise?

Bill Williams Strategy with and without other indicators and predictions

In this article, we will take a look to one the famous strategies of Bill Williams, and discuss it, and try to improve the strategy with other indicators and with predictions.

Building A Candlestick Trend Constraint Model (Part 4): Customizing Display Style For Each Trend Wave

In this article, we will explore the capabilities of the powerful MQL5 language in drawing various indicator styles on Meta Trader 5. We will also look at scripts and how they can be used in our model.

Neural networks made easy (Part 75): Improving the performance of trajectory prediction models

The models we create are becoming larger and more complex. This increases the costs of not only their training as well as operation. However, the time required to make a decision is often critical. In this regard, let us consider methods for optimizing model performance without loss of quality.

Developing a multi-currency Expert Advisor (Part 3): Architecture revision

We have already made some progress in developing a multi-currency EA with several strategies working in parallel. Considering the accumulated experience, let's review the architecture of our solution and try to improve it before we go too far ahead.

A Step-by-Step Guide on Trading the Break of Structure (BoS) Strategy

A comprehensive guide to developing an automated trading algorithm based on the Break of Structure (BoS) strategy. Detailed information on all aspects of creating an advisor in MQL5 and testing it in MetaTrader 5 — from analyzing price support and resistance to risk management

Developing a multi-currency Expert Advisor (Part 1): Collaboration of several trading strategies

There are quite a lot of different trading strategies. So, it might be useful to apply several strategies working in parallel to diversify risks and increase the stability of trading results. But if each strategy is implemented as a separate Expert Advisor (EA), then managing their work on one trading account becomes much more difficult. To solve this problem, it would be reasonable to implement the operation of different trading strategies within a single EA.

Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding (GCPC)

In previous articles, we discussed the Decision Transformer method and several algorithms derived from it. We experimented with different goal setting methods. During the experiments, we worked with various ways of setting goals. However, the model's study of the earlier passed trajectory always remained outside our attention. In this article. I want to introduce you to a method that fills this gap.

Neural networks made easy (Part 70): Closed-Form Policy Improvement Operators (CFPI)

In this article, we will get acquainted with an algorithm that uses closed-form policy improvement operators to optimize Agent actions in offline mode.

Master MQL5 from beginner to pro (Part II): Basic data types and use of variable

This is a continuation of the series for beginners. In this article, we'll look at how to create constants and variables, write dates, colors, and other useful data. We will learn how to create enumerations like days of the week or line styles (solid, dotted, etc.). Variables and expressions are the basis of programming. They are definitely present in 99% of programs, so understanding them is critical. Therefore, if you are new to programming, this article can be very useful for you. Required programming knowledge level: very basic, within the limits of my previous article (see the link at the beginning).

Introduction to MQL5 (Part 3): Mastering the Core Elements of MQL5

Explore the fundamentals of MQL5 programming in this beginner-friendly article, where we demystify arrays, custom functions, preprocessors, and event handling, all explained with clarity making every line of code accessible. Join us in unlocking the power of MQL5 with a unique approach that ensures understanding at every step. This article sets the foundation for mastering MQL5, emphasizing the explanation of each line of code, and providing a distinct and enriching learning experience.

MQL5 Wizard Techniques you should know (Part 16): Principal Component Analysis with Eigen Vectors

Principal Component Analysis, a dimensionality reducing technique in data analysis, is looked at in this article, with how it could be implemented with Eigen values and vectors. As always, we aim to develop a prototype expert-signal-class usable in the MQL5 wizard.

News Trading Made Easy (Part 2): Risk Management

In this article, inheritance will be introduced into our previous and new code. A new database design will be implemented to provide efficiency. Additionally, a risk management class will be created to tackle volume calculations.

Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies

Let's continue developing a multi-currency EA with several strategies working in parallel. Let's try to move all the work associated with opening market positions from the strategy level to the level of the EA managing the strategies. The strategies themselves will trade only virtually, without opening market positions.

In this article, we will look at the use of a trailing stop in trading. We will assess how useful and effective it is, and how it can be used. The efficiency of a trailing stop largely depends on price volatility and the selection of the stop loss level. A variety of approaches can be used to set a stop loss.

Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)

In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.

Deep Learning Forecast and ordering with Python and MetaTrader5 python package and ONNX model file

The project involves using Python for deep learning-based forecasting in financial markets. We will explore the intricacies of testing the model's performance using key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) and we will learn how to wrap everything into an executable. We will also make a ONNX model file with its EA.

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 4): Triangular moving average — Indicator Signals

The Multi-Currency Expert Advisor in this article is 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 only from one symbol chart. This time we will use only 1 indicator, namely Triangular moving average in multi-timeframes or single timeframe.

Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization

Since the first articles devoted to reinforcement learning, we have in one way or another touched upon 2 problems: exploring the environment and determining the reward function. Recent articles have been devoted to the problem of exploration in offline learning. In this article, I would like to introduce you to an algorithm whose authors completely eliminated the reward function.

Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5

In this article, we investigate how the Generalized Hurst Exponent and the Variance Ratio test can be utilized to analyze the behaviour of price series in MQL5.

Creating a market making algorithm in MQL5

How do market makers work? Let's consider this issue and create a primitive market-making algorithm.

Neural networks made easy (Part 66): Exploration problems in offline learning

Models are trained offline using data from a prepared training dataset. While providing certain advantages, its negative side is that information about the environment is greatly compressed to the size of the training dataset. Which, in turn, limits the possibilities of exploration. In this article, we will consider a method that enables the filling of a training dataset with the most diverse data possible.

Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method

As a result of tests performed in previous articles, we came to the conclusion that the optimality of the trained strategy largely depends on the training set used. In this article, we will get acquainted with a fairly simple yet effective method for selecting trajectories to train models.

Quantitative analysis in MQL5: Implementing a promising algorithm

We will analyze the question of what quantitative analysis is and how it is used by major players. We will create one of the quantitative analysis algorithms in the MQL5 language.

Neural networks made easy (Part 67): Using past experience to solve new tasks

In this article, we continue discussing methods for collecting data into a training set. Obviously, the learning process requires constant interaction with the environment. However, situations can be different.

Build Self Optmising Expert Advisors in MQL5

Build expert advisors that look forward and adjust themselves to any market.

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.

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.

WebSockets for MetaTrader 5 — Using the Windows API

In this article, we will use the WinHttp.dll to create a WebSocket client for MetaTrader 5 programs. The client will ultimately be implemented as a class and also tested against the Deriv.com WebSocket API.

Master MQL5 from beginner to pro (Part I): Getting started with programming

This article is an introduction to a series of articles about programming. It is assumed here that the reader has never dealt with programming before. So, this series starts from the very basics. Programming knowledge level: Absolute Beginner.

Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction

This article describes the implementation of a regression model based on a decision tree. The model should predict prices of financial assets. We have already prepared the data, trained and evaluated the model, as well as adjusted and optimized it. However, it is important to note that this model is intended for study purposes only and should not be used in real trading.

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.

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.

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

Creating an Interactive Graphical User Interface in MQL5 (Part 1): Making the Panel

Triangular arbitrage with predictions

Bill Williams Strategy with and without other indicators and predictions

Building A Candlestick Trend Constraint Model (Part 4): Customizing Display Style For Each Trend Wave

Neural networks made easy (Part 75): Improving the performance of trajectory prediction models

Developing a multi-currency Expert Advisor (Part 3): Architecture revision

A Step-by-Step Guide on Trading the Break of Structure (BoS) Strategy

Developing a multi-currency Expert Advisor (Part 1): Collaboration of several trading strategies

Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding (GCPC)

Neural networks made easy (Part 70): Closed-Form Policy Improvement Operators (CFPI)

Master MQL5 from beginner to pro (Part II): Basic data types and use of variable

Introduction to MQL5 (Part 3): Mastering the Core Elements of MQL5

MQL5 Wizard Techniques you should know (Part 16): Principal Component Analysis with Eigen Vectors

News Trading Made Easy (Part 2): Risk Management

Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies

Trailing stop in trading

Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)

Deep Learning Forecast and ordering with Python and MetaTrader5 python package and ONNX model file

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 4): Triangular moving average — Indicator Signals

Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization

Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5

Creating a market making algorithm in MQL5

Neural networks made easy (Part 66): Exploration problems in offline learning

Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method

Quantitative analysis in MQL5: Implementing a promising algorithm

Neural networks made easy (Part 67): Using past experience to solve new tasks

Build Self Optmising Expert Advisors in MQL5

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

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

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

WebSockets for MetaTrader 5 — Using the Windows API

Master MQL5 from beginner to pro (Part I): Getting started with programming

Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction

Creating multi-symbol, multi-period indicators

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

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