![Ready-made templates for including indicators to Expert Advisors (Part 2): Volume and Bill Williams indicators](https://c.mql5.com/2/58/Volume_Bill_Williams_indicators_600x314.jpg)
Ready-made templates for including indicators to Expert Advisors (Part 2): Volume and Bill Williams indicators
In this article, we will look at standard indicators of the Volume and Bill Williams' indicators category. We will create ready-to-use templates for indicator use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.
![How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 6): Two RSI indicators cross each other's lines](https://c.mql5.com/2/64/rj-article-images_600x314.jpg)
How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 6): Two RSI indicators cross each other's lines
The multi-currency expert advisor in this article is an expert advisor or trading robot that uses two RSI indicators with crossing lines, the Fast RSI which crosses with the Slow RSI.
![A Step-by-Step Guide on Trading the Break of Structure (BoS) Strategy](https://c.mql5.com/2/80/A_Step-by-Step_Guide_on_Trading_the_Break_of_Structure__600x314.jpg)
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
![Experiments with neural networks (Part 2): Smart neural network optimization](https://c.mql5.com/2/51/neural_network_experiments_p2_600x314.jpg)
Experiments with neural networks (Part 2): Smart neural network optimization
In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading.
![How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 4): Triangular moving average — Indicator Signals](https://c.mql5.com/2/60/rj-article-images_600x314.jpg)
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 31): Evolutionary algorithms](https://c.mql5.com/2/50/Neural_Networks_are_Simple-_Part_31_600x314.jpg)
Neural networks made easy (Part 31): Evolutionary algorithms
In the previous article, we started exploring non-gradient optimization methods. We got acquainted with the genetic algorithm. Today, we will continue this topic and will consider another class of evolutionary algorithms.
![Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)](https://c.mql5.com/2/57/cic-055_600x314.jpg)
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.
![Multibot in MetaTrader (Part II): Improved dynamic template](https://c.mql5.com/2/71/Multibot_in_MetaTrader_Part_II_600x314.jpg)
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.
![Developing a trading Expert Advisor from scratch (Part 11): Cross order system](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_from_scratch_002_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 11): Cross order system
In this article we will create a system of cross orders. There is one type of assets that makes traders' life very difficult for traders — futures contracts. But why do they make life difficult?
![Neural networks made easy (Part 33): Quantile regression in distributed Q-learning](https://c.mql5.com/2/50/Neural_Networks_Made_Easy_q-learning_600x314.jpg)
Neural networks made easy (Part 33): Quantile regression in distributed Q-learning
We continue studying distributed Q-learning. Today we will look at this approach from the other side. We will consider the possibility of using quantile regression to solve price prediction tasks.
![Multibot in MetaTrader (Part II): Improved dynamic template](https://c.mql5.com/2/71/Multibot_in_MetaTrader_Part_II_600x314.jpg)
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.
![How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session](https://c.mql5.com/2/60/Parabolic_SAR_MTF_600x314.jpg)
How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session
Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.
![Neural networks made easy (Part 56): Using nuclear norm to drive research](https://c.mql5.com/2/57/nuclear_norm_utilization_600x314.jpg)
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.
![Neural networks made easy (Part 54): Using random encoder for efficient research (RE3)](https://c.mql5.com/2/57/random_encoder_for_efficient_exploration_054_600x314.jpg)
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.
![Experiments with neural networks (Part 3): Practical application](https://c.mql5.com/2/51/neural_network_experiments_p3_600x314.jpg)
Experiments with neural networks (Part 3): Practical application
In this article series, I use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 is approached as a self-sufficient tool for using neural networks in trading.
![Filtering and feature extraction in the frequency domain](https://c.mql5.com/2/62/midjourney_image_13881_47_423_2_600x314.jpg)
Filtering and feature extraction in the frequency domain
In this article we explore the application of digital filters on time series represented in the frequency domain so as to extract unique features that may be useful to prediction models.
![Creating multi-symbol, multi-period indicators](https://c.mql5.com/2/59/multi-period_indicators_4_600x314.jpg)
Creating multi-symbol, multi-period indicators
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.
![Neural networks made easy (Part 37): Sparse Attention](https://c.mql5.com/2/53/NN_part_37_Sparse_Attention_600x314.jpg)
Neural networks made easy (Part 37): Sparse Attention
In the previous article, we discussed relational models which use attention mechanisms in their architecture. One of the specific features of these models is the intensive utilization of computing resources. In this article, we will consider one of the mechanisms for reducing the number of computational operations inside the Self-Attention block. This will increase the general performance of the model.
![Developing a trading Expert Advisor from scratch (Part 20): New order system (III)](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_from_scratch_011_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 20): New order system (III)
We continue to implement the new order system. The creation of such a system requires a good command of MQL5, as well as an understanding of how the MetaTrader 5 platform actually works and what resources it provides.
![Trailing stop in trading](https://c.mql5.com/2/67/Trailing_stop_in_trading_600x314.jpg)
Trailing stop in trading
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.
![Creating an EA that works automatically (Part 07): Account types (II)](https://c.mql5.com/2/50/aprendendo_construindo_007_600x314.jpg)
Creating an EA that works automatically (Part 07): Account types (II)
Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. The trader should always be aware of what the automatic EA is doing, so that if it "goes off the rails", the trader could remove it from the chart as soon as possible and take control of the situation.
![ALGLIB numerical analysis library in MQL5](https://c.mql5.com/2/58/ALGLIB_in_MQL5_600x314.jpg)
ALGLIB numerical analysis library in MQL5
The article takes a quick look at the ALGLIB 3.19 numerical analysis library, its applications and new algorithms that can improve the efficiency of financial data analysis.
![Neural networks made easy (Part 18): Association rules](https://c.mql5.com/2/49/Neural_Networks_Easy_010_600x314.jpg)
Neural networks made easy (Part 18): Association rules
As a continuation of this series of articles, let's consider another type of problems within unsupervised learning methods: mining association rules. This problem type was first used in retail, namely supermarkets, to analyze market baskets. In this article, we will talk about the applicability of such algorithms in trading.
![Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor](https://c.mql5.com/2/71/MQL5_Article-02_Artwork_hero_1200_x_628px_600x314.jpg)
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 17): Dimensionality reduction](https://c.mql5.com/2/49/Neural_networks_made_easy_007_600x314.jpg)
Neural networks made easy (Part 17): Dimensionality reduction
In this part we continue discussing Artificial Intelligence models. Namely, we study unsupervised learning algorithms. We have already discussed one of the clustering algorithms. In this article, I am sharing a variant of solving problems related to dimensionality reduction.
![Testing and optimization of binary options strategies in MetaTrader 5](https://c.mql5.com/2/0/binary-strategy-tester_600x314.jpg)
Testing and optimization of binary options strategies in MetaTrader 5
In this article, I will check and optimize binary options strategies in MetaTrader 5.
![Neural networks made easy (Part 58): Decision Transformer (DT)](https://c.mql5.com/2/58/decision-transformer_600x314.jpg)
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.
![Experiments with neural networks (Part 7): Passing indicators](https://c.mql5.com/2/59/Experiments_with_neural_networks_7_600x314.jpg)
Experiments with neural networks (Part 7): Passing indicators
Examples of passing indicators to a perceptron. The article describes general concepts and showcases the simplest ready-made Expert Advisor followed by the results of its optimization and forward test.
![Neural networks made easy (Part 53): Reward decomposition](https://c.mql5.com/2/57/decomposition_of_remuneration_053_600x314.jpg)
Neural networks made easy (Part 53): Reward decomposition
We have already talked more than once about the importance of correctly selecting the reward function, which we use to stimulate the desired behavior of the Agent by adding rewards or penalties for individual actions. But the question remains open about the decryption of our signals by the Agent. In this article, we will talk about reward decomposition in terms of transmitting individual signals to the trained Agent.
![Master MQL5 from beginner to pro (Part II): Basic data types and use of variable](https://c.mql5.com/2/64/Learning_MQL5_-_from_beginner_to_pro_mPart_II6_600x314.jpg)
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).
![Neural networks made easy (Part 22): Unsupervised learning of recurrent models](https://c.mql5.com/2/49/Neural_Networks_Easy_014_600x314.jpg)
Neural networks made easy (Part 22): Unsupervised learning of recurrent models
We continue to study unsupervised learning algorithms. This time I suggest that we discuss the features of autoencoders when applied to recurrent model training.
![Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)](https://c.mql5.com/2/26/MQL5-avatar-X-Auto-table-001.png)
![Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)](https://c.mql5.com/i/articles/overlay.png)
Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)
We continue to complement the Rendered table (CCanvasTable) with new features. The table will now have: highlighting of the rows when hovered; ability to add an array of icons for each cell and a method for switching them; ability to set or modify the cell text during the runtime, and more.
![Wrapping ONNX models in classes](https://c.mql5.com/2/54/ONNX_Models_in_the_Class_600x314.jpg)
Wrapping ONNX models in classes
Object-oriented programming enables creation of a more compact code that is easy to read and modify. Here we will have a look at the example for three ONNX models.
![Neural networks made easy (Part 73): AutoBots for predicting price movements](https://c.mql5.com/2/64/Neural_networks_are_easy_8Part_73g__AutoBots_for_predicting_price_movement_600x314.jpg)
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 44): Learning skills with dynamics in mind](https://c.mql5.com/2/55/Neural_Networks_are_Just_a_Part_600x314.jpg)
Neural networks made easy (Part 44): Learning skills with dynamics in mind
In the previous article, we introduced the DIAYN method, which offers the algorithm for learning a variety of skills. The acquired skills can be used for various tasks. But such skills can be quite unpredictable, which can make them difficult to use. In this article, we will look at an algorithm for learning predictable skills.
![Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5](https://c.mql5.com/2/69/Implementing_the_Generalized_Hurst_Exponent_and_the_Variance_Ratio_test_in_MQL5_600x314__3.jpg)
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.
![Mastering Market Dynamics: Creating a Support and Resistance Strategy Expert Advisor (EA)](https://c.mql5.com/2/82/Creating_a_Support_and_Resistance_Strategy_Expert_Advisor_600x314.jpg)
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.
![Neural networks made easy (Part 66): Exploration problems in offline learning](https://c.mql5.com/2/61/Neural_networks_are_easy_Part_66_600x314.jpg)
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 23): Building a tool for Transfer Learning](https://c.mql5.com/2/49/Neural_Networks_Easy_015_600x314.jpg)
Neural networks made easy (Part 23): Building a tool for Transfer Learning
In this series of articles, we have already mentioned Transfer Learning more than once. However, this was only mentioning. in this article, I suggest filling this gap and taking a closer look at Transfer Learning.
![Data Science and Machine Learning (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal](https://c.mql5.com/2/77/Data_Science_and_ML_9Part_22t_600x314.jpg)
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.