![Integrate Your Own LLM into EA (Part 2): Example of Environment Deployment](https://c.mql5.com/2/59/Example_of_Environment_Deployment_600x314.jpg)
Integrate Your Own LLM into EA (Part 2): Example of Environment Deployment
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.
![Neural networks made easy (Part 75): Improving the performance of trajectory prediction models](https://c.mql5.com/2/68/Neural_Networks_Made_Easy_5Part_75d_Improving_the_Performance_of_Trajectory_Prediction_Models_600x31.jpg)
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.
![Do Traders Need Services From Developers?](https://c.mql5.com/2/10/MQL5_freelance_avatar.png)
![Do Traders Need Services From Developers?](https://c.mql5.com/i/articles/overlay.png)
Do Traders Need Services From Developers?
Algorithmic trading becomes more popular and needed, which naturally led to a demand for exotic algorithms and unusual tasks. To some extent, such complex applications are available in the Code Base or in the Market. Although traders have simple access to those apps in a couple of clicks, these apps may not satisfy all needs in full. In this case, traders look for developers who can write a desired application in the MQL5 Freelance section and assign an order.
![Modified Grid-Hedge EA in MQL5 (Part II): Making a Simple Grid EA](https://c.mql5.com/2/64/Modified_Grid-Hedge_EA_in_MQL5_uPart_IIt_Making_a_Simple_Grid_EA_600x314.jpg)
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.
![Multilayer perceptron and backpropagation algorithm (Part 3): Integration with the Strategy Tester - Overview (I).](https://c.mql5.com/2/51/Perceptron_Multicamadas_e_o_Algoritmo_Backpropagation_600x314.jpg)
Multilayer perceptron and backpropagation algorithm (Part 3): Integration with the Strategy Tester - Overview (I).
The multilayer perceptron is an evolution of the simple perceptron which can solve non-linear separable problems. Together with the backpropagation algorithm, this neural network can be effectively trained. In Part 3 of the Multilayer Perceptron and Backpropagation series, we'll see how to integrate this technique into the Strategy Tester. This integration will allow the use of complex data analysis aimed at making better decisions to optimize your trading strategies. In this article, we will discuss the advantages and problems of this technique.
![Experiments with neural networks (Part 4): Templates](https://c.mql5.com/2/52/neural_network_experiments-004_600x314.jpg)
Experiments with neural networks (Part 4): Templates
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. Simple explanation.
![Brute force approach to patterns search (Part V): Fresh angle](https://c.mql5.com/2/57/The_Bruteforce_Approach_Part_5_600x314.jpg)
Brute force approach to patterns search (Part V): Fresh angle
In this article, I will show a completely different approach to algorithmic trading I ended up with after quite a long time. Of course, all this has to do with my brute force program, which has undergone a number of changes that allow it to solve several problems simultaneously. Nevertheless, the article has turned out to be more general and as simple as possible, which is why it is also suitable for those who know nothing about brute force.
![Neural networks made easy (Part 35): Intrinsic Curiosity Module](https://c.mql5.com/2/50/Neural_Networks_Made_035_600x314.jpg)
Neural networks made easy (Part 35): Intrinsic Curiosity Module
We continue to study reinforcement learning algorithms. All the algorithms we have considered so far required the creation of a reward policy to enable the agent to evaluate each of its actions at each transition from one system state to another. However, this approach is rather artificial. In practice, there is some time lag between an action and a reward. In this article, we will get acquainted with a model training algorithm which can work with various time delays from the action to the reward.
![Category Theory in MQL5 (Part 20): A detour to Self-Attention and the Transformer](https://c.mql5.com/2/58/Category-Theory-p20_600x314.jpg)
Category Theory in MQL5 (Part 20): A detour to Self-Attention and the Transformer
We digress in our series by pondering at part of the algorithm to chatGPT. Are there any similarities or concepts borrowed from natural transformations? We attempt to answer these and other questions in a fun piece, with our code in a signal class format.
![Filtering by History](https://c.mql5.com/2/14/244_1.png)
![Filtering by History](https://c.mql5.com/i/articles/overlay.png)
Filtering by History
The article describes the usage of virtual trading as an integral part of trade opening filter.
![Developing a Replay System (Part 27): Expert Advisor project — C_Mouse class (I)](https://c.mql5.com/2/58/Projeto_Expert_AdvisorcClasse_C_Mous_600x314.jpg)
Developing a Replay System (Part 27): Expert Advisor project — C_Mouse class (I)
In this article we will implement the C_Mouse class. It provides the ability to program at the highest level. However, talking about high-level or low-level programming languages is not about including obscene words or jargon in the code. It's the other way around. When we talk about high-level or low-level programming, we mean how easy or difficult the code is for other programmers to understand.
![Data Science and Machine Learning (Part 19): Supercharge Your AI models with AdaBoost](https://c.mql5.com/2/65/Data_Science_and_Machine_Learning_7Part_191_Supercharge_Your_AI_models_with_AdaBoost_600x314.jpg)
Data Science and Machine Learning (Part 19): Supercharge Your AI models with AdaBoost
AdaBoost, a powerful boosting algorithm designed to elevate the performance of your AI models. AdaBoost, short for Adaptive Boosting, is a sophisticated ensemble learning technique that seamlessly integrates weak learners, enhancing their collective predictive strength.
![Building A Candlestick Trend Constraint Model(Part 3): Detecting changes in trends while using this system](https://c.mql5.com/2/78/Building_A_Candlestick_Trend_Constraint_Model_Part_3___NO_BG_600x314.jpg)
Building A Candlestick Trend Constraint Model(Part 3): Detecting changes in trends while using this system
This article explores how economic news releases, investor behavior, and various factors can influence market trend reversals. It includes a video explanation and proceeds by incorporating MQL5 code into our program to detect trend reversals, alert us, and take appropriate actions based on market conditions. This builds upon previous articles in the series.
![Neural networks made easy (Part 48): Methods for reducing overestimation of Q-function values](https://c.mql5.com/2/56/NN_part_48_600x314.jpg)
Neural networks made easy (Part 48): Methods for reducing overestimation of Q-function values
In the previous article, we introduced the DDPG method, which allows training models in a continuous action space. However, like other Q-learning methods, DDPG is prone to overestimating Q-function values. This problem often results in training an agent with a suboptimal strategy. In this article, we will look at some approaches to overcome the mentioned issue.
![Category Theory in MQL5 (Part 18): Naturality Square](https://c.mql5.com/2/57/category-theory-p18_600x314.jpg)
Category Theory in MQL5 (Part 18): Naturality Square
This article continues our series into category theory by introducing natural transformations, a key pillar within the subject. We look at the seemingly complex definition, then delve into examples and applications with this series’ ‘bread and butter’; volatility forecasting.
![Developing Zone Recovery Martingale strategy in MQL5](https://c.mql5.com/2/82/Developing_Zone_Recovery_Martingale_strategy_in_MQL5_600x314.jpg)
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.
![Neural networks made easy (Part 47): Continuous action space](https://c.mql5.com/2/55/Neural_Networks_Part_47_600x314.jpg)
Neural networks made easy (Part 47): Continuous action space
In this article, we expand the range of tasks of our agent. The training process will include some aspects of money and risk management, which are an integral part of any trading strategy.
![Automated exchange grid trading using stop pending orders on Moscow Exchange (MOEX)](https://c.mql5.com/2/54/moex-mesh-trading_600x314.jpg)
Automated exchange grid trading using stop pending orders on Moscow Exchange (MOEX)
The article considers the grid trading approach based on stop pending orders and implemented in an MQL5 Expert Advisor on the Moscow Exchange (MOEX). When trading in the market, one of the simplest strategies is a grid of orders designed to "catch" the market price.
![Developing a Replay System (Part 32): Order System (I)](https://c.mql5.com/2/59/sistema_de_Replay_32__600x314.jpg)
Developing a Replay System (Part 32): Order System (I)
Of all the things that we have developed so far, this system, as you will probably notice and eventually agree, is the most complex. Now we need to do something very simple: make our system simulate the operation of a trading server. This need to accurately implement the way the trading server operates seems like a no-brainer. At least in words. But we need to do this so that the everything is seamless and transparent for the user of the replay/simulation system.
![Building A Candlestick Trend Constraint Model (Part 1): For EAs And Technical Indicators](https://c.mql5.com/2/76/Building_A_Candlestick_Trend_Constraint_Model_lPart_13__600x314.jpg)
Building A Candlestick Trend Constraint Model (Part 1): For EAs And Technical Indicators
This article is aimed at beginners and pro-MQL5 developers. It provides a piece of code to define and constrain signal-generating indicators to trends in higher timeframes. In this way, traders can enhance their strategies by incorporating a broader market perspective, leading to potentially more robust and reliable trading signals.
![Developing a Replay System (Part 26): Expert Advisor project — C_Terminal class](https://c.mql5.com/2/58/replay-p26_600x314.jpg)
Developing a Replay System (Part 26): Expert Advisor project — C_Terminal class
We can now start creating an Expert Advisor for use in the replay/simulation system. However, we need something improved, not a random solution. Despite this, we should not be intimidated by the initial complexity. It's important to start somewhere, otherwise we end up ruminating about the difficulty of a task without even trying to overcome it. That's what programming is all about: overcoming obstacles through learning, testing, and extensive research.
![Building A Candlestick Trend Constraint Model (Part 1): For EAs And Technical Indicators](https://c.mql5.com/2/76/Building_A_Candlestick_Trend_Constraint_Model_lPart_13__600x314.jpg)
Building A Candlestick Trend Constraint Model (Part 1): For EAs And Technical Indicators
This article is aimed at beginners and pro-MQL5 developers. It provides a piece of code to define and constrain signal-generating indicators to trends in higher timeframes. In this way, traders can enhance their strategies by incorporating a broader market perspective, leading to potentially more robust and reliable trading signals.
![Developing a trading Expert Advisor from scratch (Part 25): Providing system robustness (II)](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_004_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 25): Providing system robustness (II)
In this article, we will make the final step towards the EA's performance. So, be prepared for a long read. To make our Expert Advisor reliable, we will first remove everything from the code that is not part of the trading system.
![Developing a trading Expert Advisor from scratch (Part 26): Towards the future (I)](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_005_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 26): Towards the future (I)
Today we will take our order system to the next level. But before that, we need to solve a few problems. Now we have some questions that are related to how we want to work and what things we do during the trading day.
![Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization](https://c.mql5.com/2/62/midjourney_image_13912_49_444_1_600x314.jpg)
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.
![Neural networks made easy (Part 60): Online Decision Transformer (ODT)](https://c.mql5.com/2/59/Online_Decision_Transformer_UP_600x314.jpg)
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.
![Quantitative analysis in MQL5: Implementing a promising algorithm](https://c.mql5.com/2/62/Quantitative_analysis_in_MQL5_-__implementing_a_promising_algorithm_600x314.jpg)
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.
![Developing a Replay System — Market simulation (Part 14): Birth of the SIMULATOR (IV)](https://c.mql5.com/2/55/Desenvolvendo_um_sistema_de_Replay_Parte_14_600x314.jpg)
Developing a Replay System — Market simulation (Part 14): Birth of the SIMULATOR (IV)
In this article we will continue the simulator development stage. this time we will see how to effectively create a RANDOM WALK type movement. This type of movement is very intriguing because it forms the basis of everything that happens in the capital market. In addition, we will begin to understand some concepts that are fundamental to those conducting market analysis.
![Developing a Replay System — Market simulation (Part 11): Birth of the SIMULATOR (I)](https://c.mql5.com/2/54/Desenvolvendo_um_sistema_de_Replay_Parte_11_600x314.jpg)
Developing a Replay System — Market simulation (Part 11): Birth of the SIMULATOR (I)
In order to use the data that forms the bars, we must abandon replay and start developing a simulator. We will use 1 minute bars because they offer the least amount of difficulty.
![Developing a Replay System — Market simulation (Part 17): Ticks and more ticks (I)](https://c.mql5.com/2/55/replay-p17_600x314.jpg)
Developing a Replay System — Market simulation (Part 17): Ticks and more ticks (I)
Here we will see how to implement something really interesting, but at the same time very difficult due to certain points that can be very confusing. The worst thing that can happen is that some traders who consider themselves professionals do not know anything about the importance of these concepts in the capital market. Well, although we focus here on programming, understanding some of the issues involved in market trading is paramount to what we are going to implement.
![Using PatchTST Machine Learning Algorithm for Predicting Next 24 Hours of Price Action](https://c.mql5.com/2/83/Using_PatchTST_Machine_Learning_Algorithm_for_Predicting_Next_24_Hours_of_Price_Action_600x314.jpg)
Using PatchTST Machine Learning Algorithm for Predicting Next 24 Hours of Price Action
In this article, we apply a relatively complex neural network algorithm released in 2023 called PatchTST for predicting the price action for the next 24 hours. We will use the official repository, make slight modifications, train a model for EURUSD, and apply it to making future predictions both in Python and MQL5.
![Category Theory in MQL5 (Part 22): A different look at Moving Averages](https://c.mql5.com/2/58/Category-Theory-p22_600x314.jpg)
Category Theory in MQL5 (Part 22): A different look at Moving Averages
In this article we attempt to simplify our illustration of concepts covered in these series by dwelling on just one indicator, the most common and probably the easiest to understand. The moving average. In doing so we consider significance and possible applications of vertical natural transformations.
![Using PatchTST Machine Learning Algorithm for Predicting Next 24 Hours of Price Action](https://c.mql5.com/2/83/Using_PatchTST_Machine_Learning_Algorithm_for_Predicting_Next_24_Hours_of_Price_Action_600x314.jpg)
Using PatchTST Machine Learning Algorithm for Predicting Next 24 Hours of Price Action
In this article, we apply a relatively complex neural network algorithm released in 2023 called PatchTST for predicting the price action for the next 24 hours. We will use the official repository, make slight modifications, train a model for EURUSD, and apply it to making future predictions both in Python and MQL5.
![Neural networks made easy (Part 45): Training state exploration skills](https://c.mql5.com/2/55/Neural_Networks_Part_45_600x314.jpg)
Neural networks made easy (Part 45): Training state exploration skills
Training useful skills without an explicit reward function is one of the main challenges in hierarchical reinforcement learning. Previously, we already got acquainted with two algorithms for solving this problem. But the question of the completeness of environmental research remains open. This article demonstrates a different approach to skill training, the use of which directly depends on the current state of the system.
![Neural networks made easy (Part 51): Behavior-Guided Actor-Critic (BAC)](https://c.mql5.com/2/57/behavior_driven_actor_critic_600x314.jpg)
Neural networks made easy (Part 51): Behavior-Guided Actor-Critic (BAC)
The last two articles considered the Soft Actor-Critic algorithm, which incorporates entropy regularization into the reward function. This approach balances environmental exploration and model exploitation, but it is only applicable to stochastic models. The current article proposes an alternative approach that is applicable to both stochastic and deterministic models.
![MQL5 Wizard Techniques you should know (Part 08): Perceptrons](https://c.mql5.com/2/61/MQL5_Wizard_Techniques_you_should_know_8Part_08e_Perceptrons_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 08): Perceptrons
Perceptrons, single hidden layer networks, can be a good segue for anyone familiar with basic automated trading and is looking to dip into neural networks. We take a step by step look at how this could be realized in a signal class assembly that is part of the MQL5 Wizard classes for expert advisors.
![MQL5 Wizard Techniques you should know (Part 19): Bayesian Inference](https://c.mql5.com/2/78/MQL5_Wizard_Techniques_you_should_know_7Part_19d_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 19): Bayesian Inference
Bayesian inference is the adoption of Bayes Theorem to update probability hypothesis as new information is made available. This intuitively leans to adaptation in time series analysis, and so we have a look at how we could use this in building custom classes not just for the signal but also money-management and trailing-stops.
![Building A Candlestick Trend Constraint Model (Part 7): Refining our model for EA development](https://c.mql5.com/2/86/Building_A_Candlestick_Trend_Constraint_Model_Part_7_600x314.jpg)
Building A Candlestick Trend Constraint Model (Part 7): Refining our model for EA development
In this article, we will delve into the detailed preparation of our indicator for Expert Advisor (EA) development. Our discussion will encompass further refinements to the current version of the indicator to enhance its accuracy and functionality. Additionally, we will introduce new features that mark exit points, addressing a limitation of the previous version, which only identified entry points.
![Market Reactions and Trading Strategies in Response to Dividend Announcements: Evaluating the Efficient Market Hypothesis in Stock Trading](https://c.mql5.com/2/61/Evaluating_the_Efficient_Market_Hypothesis_in_Stock_Trading_600x314.jpg)
Market Reactions and Trading Strategies in Response to Dividend Announcements: Evaluating the Efficient Market Hypothesis in Stock Trading
In this article, we will analyse the impact of dividend announcements on stock market returns and see how investors can earn more returns than those offered by the market when they expect a company to announce dividends. In doing so, we will also check the validity of the Efficient Market Hypothesis in the context of the Indian Stock Market.
![Build Self Optimizing Expert Advisors With MQL5 And Python](https://c.mql5.com/2/85/Build_Self_Optimizing_Expert_Advisors_With_MQL5_And_Python_600x314.jpg)
Build Self Optimizing Expert Advisors With MQL5 And Python
In this article, we will discuss how we can build Expert Advisors capable of autonomously selecting and changing trading strategies based on prevailing market conditions. We will learn about Markov Chains and how they can be helpful to us as algorithmic traders.