![Neural networks made easy (Part 80): Graph Transformer Generative Adversarial Model (GTGAN)](https://c.mql5.com/2/72/Neural_networks_are_easy_Part_80_600x314.jpg)
Neural networks made easy (Part 80): Graph Transformer Generative Adversarial Model (GTGAN)
In this article, I will get acquainted with the GTGAN algorithm, which was introduced in January 2024 to solve complex problems of generation architectural layouts with graph constraints.
![Developing an Expert Advisor (EA) based on the Consolidation Range Breakout strategy in MQL5](https://c.mql5.com/2/84/Developing_an_Expert_Advisor_based_on_the_Consolidation_Range_Breakout_strategy_in_MQL5_600x314.jpg)
Developing an Expert Advisor (EA) based on the Consolidation Range Breakout strategy in MQL5
This article outlines the steps to create an Expert Advisor (EA) that capitalizes on price breakouts after consolidation periods. By identifying consolidation ranges and setting breakout levels, traders can automate their trading decisions based on this strategy. The Expert Advisor aims to provide clear entry and exit points while avoiding false breakouts
![Design Patterns in software development and MQL5 (Part 2): Structural Patterns](https://c.mql5.com/2/60/Design_Patterns_zPart_2v_Structural_Patterns_600x314.jpg)
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.
![Neural networks made easy (Part 42): Model procrastination, reasons and solutions](https://c.mql5.com/2/54/NN_Simple_Part_42_procrastination_600x314.jpg)
Neural networks made easy (Part 42): Model procrastination, reasons and solutions
In the context of reinforcement learning, model procrastination can be caused by several reasons. The article considers some of the possible causes of model procrastination and methods for overcoming them.
![Using JSON Data API in your MQL projects](https://c.mql5.com/2/83/Using_Json_Data_API_in_your_MQL_projects_600x314.jpg)
Using JSON Data API in your MQL projects
Imagine that you can use data that is not found in MetaTrader, you only get data from indicators by price analysis and technical analysis. Now imagine that you can access data that will take your trading power steps higher. You can multiply the power of the MetaTrader software if you mix the output of other software, macro analysis methods, and ultra-advanced tools through the API data. In this article, we will teach you how to use APIs and introduce useful and valuable API data services.
![Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding (GCPC)](https://c.mql5.com/2/63/Neural_networks_made_easy_aPart_71__GCPCr_600x314.jpg)
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 40): Using Go-Explore on large amounts of data](https://c.mql5.com/2/54/neural_networks_go_explore_040_600x314.jpg)
Neural networks made easy (Part 40): Using Go-Explore on large amounts of data
This article discusses the use of the Go-Explore algorithm over a long training period, since the random action selection strategy may not lead to a profitable pass as training time increases.
![MQL5 Integration: Python](https://c.mql5.com/2/89/midjourney_image_14135_392_3769_1_600x314.jpg)
MQL5 Integration: Python
Python is a well-known and popular programming language with many features, especially in the fields of finance, data science, Artificial Intelligence, and Machine Learning. Python is a powerful tool that can be useful in trading as well. MQL5 allows us to use this powerful language as an integration to get our objectives done effectively. In this article, we will share how we can use Python as an integration in MQL5 after learning some basic information about Python.
![Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT)](https://c.mql5.com/2/70/Neural_networks_made_easy_Part_78_600x314.jpg)
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.
![Data label for time series mining (Part 4):Interpretability Decomposition Using Label Data](https://c.mql5.com/2/61/Data_label_for_time_series_mining_zPart_4oInterpretability_Decomposition_Using_Label_Data_600x314.jpg)
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!
![Implementing the Zeus EA: Automated Trading with RSI and Moving Averages in MQL5](https://c.mql5.com/2/88/Implementing_the_Zeus_EA__Automated_Trading_with_RSI_and_Moving_Averages_600x314.jpg)
Implementing the Zeus EA: Automated Trading with RSI and Moving Averages in MQL5
This article outlines the steps to implement the Zeus EA based on the RSI and Moving Average indicators for guiding automated trading.
![Neural networks made easy (Part 57): Stochastic Marginal Actor-Critic (SMAC)](https://c.mql5.com/2/57/stochastic_marginal_actor_critic_600x314.jpg)
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.
![Implementing a Bollinger Bands Trading Strategy with MQL5: A Step-by-Step Guide](https://c.mql5.com/2/86/Implementing_a_Bollinger_Bands_Trading_Strategy_600x314__1.jpg)
Implementing a Bollinger Bands Trading Strategy with MQL5: A Step-by-Step Guide
A step-by-step guide to implementing an automated trading algorithm in MQL5 based on the Bollinger Bands trading strategy. A detailed tutorial based on creating an Expert Advisor that can be useful for traders.
![Category Theory in MQL5 (Part 4): Spans, Experiments, and Compositions](https://c.mql5.com/2/52/Category-Theory-p4_600x314.jpg)
Category Theory in MQL5 (Part 4): Spans, Experiments, and Compositions
Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL5 community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that provides insight while hopefully furthering the use of this remarkable field in Traders' strategy development.
![Neural networks made easy (Part 52): Research with optimism and distribution correction](https://c.mql5.com/2/57/optimistic-actor-critic_600x314.jpg)
Neural networks made easy (Part 52): Research with optimism and distribution correction
As the model is trained based on the experience reproduction buffer, the current Actor policy moves further and further away from the stored examples, which reduces the efficiency of training the model as a whole. In this article, we will look at the algorithm of improving the efficiency of using samples in reinforcement learning algorithms.
![Alternative risk return metrics in MQL5](https://c.mql5.com/2/59/Alternative_risk_return_V3_up_600x314.jpg)
Alternative risk return metrics in MQL5
In this article we present the implementation of several risk return metrics billed as alternatives to the Sharpe ratio and examine hypothetical equity curves to analyze their characteristics.
![Reimagining Classic Strategies (Part II): Bollinger Bands Breakouts](https://c.mql5.com/2/85/Reimagining_Classic_Strategies_Part_II_600x314.jpg)
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.
![Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part III)](https://c.mql5.com/2/83/Building_A_Candlestick_Trend_Constraint_Model__Part_5___CONT_600x314.jpg)
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.
![Neural networks made easy (Part 74): Trajectory prediction with adaptation](https://c.mql5.com/2/65/Neural_networks_are_easy_4Part_74n_Adaptive_trajectory_prediction_600x314.jpg)
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.
![MQL5 Wizard Techniques you should know (Part 11): Number Walls](https://c.mql5.com/2/66/MQL5_Wizard_Techniques_you_should_know_0Part_11w_Number_Walls_600x314.jpg)
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.
![Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part III)](https://c.mql5.com/2/83/Building_A_Candlestick_Trend_Constraint_Model__Part_5___CONT_600x314.jpg)
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.
![Neural networks made easy (Part 72): Trajectory prediction in noisy environments](https://c.mql5.com/2/64/Neural_networks_made_easy_ePart_726_Predicting_trajectories_in_the_presence_of_noise_600x314.jpg)
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.
![Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method](https://c.mql5.com/2/60/Neural_networks_made_easy_mPart_64s_CWBC_600x314.jpg)
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.
![Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL)](https://c.mql5.com/2/61/Neural_Networks_Made_Easy_kPart_659_DWSL_600x314.jpg)
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 62): Using Decision Transformer in hierarchical models](https://c.mql5.com/2/59/Neural_networks_are_easy_aPart_62o_600x314.jpg)
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.
![Creating a market making algorithm in MQL5](https://c.mql5.com/2/64/Creating_a_market_making_algorithm_in_MQL5_600x314.jpg)
Creating a market making algorithm in MQL5
How do market makers work? Let's consider this issue and create a primitive market-making algorithm.
![Developing a multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status](https://c.mql5.com/2/71/Developing_a_multi-currency_advisor_Part_4_600x314.jpg)
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 Replay System (Part 31): Expert Advisor project — C_Mouse class (V)](https://c.mql5.com/2/59/sistema_de_Replay_up_600x314.jpg)
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.
![Developing a quality factor for Expert Advisors](https://c.mql5.com/2/55/Desenvolvendo_um_fator_de_qualidade_para_os_EAs_600x314.jpg)
Developing a quality factor for Expert Advisors
In this article, we will see how to develop a quality score that your Expert Advisor can display in the strategy tester. We will look at two well-known calculation methods – Van Tharp and Sunny Harris.
![Creating a Daily Drawdown Limiter EA in MQL5](https://c.mql5.com/2/83/Creating_a_Daily_Drawdown_Limiter_EA_in_MQL5_600x314.jpg)
Creating a Daily Drawdown Limiter EA in MQL5
The article discusses, from a detailed perspective, how to implement the creation of an Expert Advisor (EA) based on the trading algorithm. This helps to automate the system in the MQL5 and take control of the Daily Drawdown.
![Modified Grid-Hedge EA in MQL5 (Part III): Optimizing Simple Hedge Strategy (I)](https://c.mql5.com/2/72/Modified_Grid-Hedge_EA_in_MQL5_Part_III_600x314.jpg)
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.
![The case for using a Composite Data Set this Q4 in weighing SPDR XLY's next performance](https://c.mql5.com/2/61/Composite_Data_Set_this_Q4_in_weighing_SPDR_XLY_600x314.jpg)
The case for using a Composite Data Set this Q4 in weighing SPDR XLY's next performance
We consider XLY, SPDR’s consumer discretionary spending ETF and see if with tools in MetaTrader’s IDE we can sift through an array of data sets in selecting what could work with a forecasting model with a forward outlook of not more than a year.
![MQL5 Wizard Techniques you should know (Part 28): GANs Revisited with a Primer on Learning Rates](https://c.mql5.com/2/85/MQL5_Wizard_Techniques_you_should_know_Part_28_600x314.jpg)
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.
![Neural networks made easy (Part 41): Hierarchical models](https://c.mql5.com/2/54/NN_Simple_Part_41_Hierarchical_Models_600x314.jpg)
Neural networks made easy (Part 41): Hierarchical models
The article describes hierarchical training models that offer an effective approach to solving complex machine learning problems. Hierarchical models consist of several levels, each of which is responsible for different aspects of the task.
![Developing a multi-currency Expert Advisor (Part 5): Variable position sizes](https://c.mql5.com/2/73/Developing_a_multi-currency_advisor_Part_5_Variable_position_sizes_600x314.jpg)
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.
![Building a Candlestick Trend Constraint Model (Part 8): Expert Advisor Development (I)](https://c.mql5.com/2/88/midjourney_image_15321_390_3753_3_600x314.jpg)
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.
![Cross-validation and basics of causal inference in CatBoost models, export to ONNX format](https://c.mql5.com/2/60/CatBoost_export_to_ONNX_format_600x314.jpg)
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.
![Developing a multi-currency Expert Advisor (Part 3): Architecture revision](https://c.mql5.com/2/70/Developing_a_multi-currency_advisor_6Part_3q__Architecture_review_600x314.jpg)
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.
![MQL5 Wizard Techniques you should know (Part 22): Conditional GANs](https://c.mql5.com/2/80/MQL5_Wizard_Techniques_you_should_know_Part_22_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 22): Conditional GANs
Generative Adversarial Networks are a pairing of Neural Networks that train off of each other for more accurate results. We adopt the conditional type of these networks as we look to possible application in forecasting Financial time series within an Expert Signal Class.
![MQL5 Wizard Techniques you should know (Part 24): Moving Averages](https://c.mql5.com/2/82/MQL5_Wizard_Techniques_you_should_know_Part_24_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 24): Moving Averages
Moving Averages are a very common indicator that are used and understood by most Traders. We explore possible use cases that may not be so common within MQL5 Wizard assembled Expert Advisors.