![Propensity score in causal inference](https://c.mql5.com/2/72/Propensity_score_in_causal_inference___600x314.jpg)
Propensity score in causal inference
The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.
![Automated Parameter Optimization for Trading Strategies Using Python and MQL5](https://c.mql5.com/2/82/Automated_Parameter_Optimization_for_Trading_Strategies_Using_Python_and_MQL5___2_600x314.jpg)
Automated Parameter Optimization for Trading Strategies Using Python and MQL5
There are several types of algorithms for self-optimization of trading strategies and parameters. These algorithms are used to automatically improve trading strategies based on historical and current market data. In this article we will look at one of them with python and MQL5 examples.
![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.
![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.
![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.
![Building A Candlestick Trend Constraint Model(Part 5): Notification System(Part II)](https://c.mql5.com/2/82/Building_A_Candlestick_Trend_Constraint_Model_Part_5_Next-7iSmtcwWt-transformed_600x314.jpg)
Building A Candlestick Trend Constraint Model(Part 5): Notification System(Part II)
Today, we are discussing a working Telegram integration for MetaTrader 5 Indicator notifications using the power of MQL5, in partnership with Python and the Telegram Bot API. We will explain everything in detail so that no one misses any point. By the end of this project, you will have gained valuable insights to apply in your projects.
![Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU](https://c.mql5.com/2/82/Integrate_Your_Own_LLM_into_EA_Part_4_600x314.jpg)
Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU
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.
![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.
![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.
![Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part I)](https://c.mql5.com/2/81/Building_A_Candlestick_Trend_Constraint_Model_Part_5_600x314__2.jpg)
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part I)
We will breakdown the main MQL5 code into specified code snippets to illustrate the integration of Telegram and WhatsApp for receiving signal notifications from the Trend Constraint indicator we are creating in this article series. This will help traders, both novices and experienced developers, grasp the concept easily. First, we will cover the setup of MetaTrader 5 for notifications and its significance to the user. This will help developers in advance to take notes to further apply in their systems.
![MQL5 Wizard Techniques you should know (Part 23): CNNs](https://c.mql5.com/2/81/MQL5_Wizard_Techniques_you_should_know_Part_23_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 23): CNNs
Convolutional Neural Networks are another machine learning algorithm that tend to specialize in decomposing multi-dimensioned data sets into key constituent parts. We look at how this is typically achieved and explore a possible application for traders in another MQL5 wizard signal class.
![How to earn money by fulfilling traders' orders in the Freelance service](https://c.mql5.com/2/80/How-to-MQL5-Freelance_600x314.jpg)
How to earn money by fulfilling traders' orders in the Freelance service
MQL5 Freelance is an online service where developers are paid to create trading applications for traders customers. The service has been successfully operating since 2010, with over 100,000 projects completed to date, totaling $7 million in value. As we can see, a substantial amount of money is involved here.
![Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer](https://c.mql5.com/2/69/Neural_networks_made_easy_qPart_767_Exploring_various_modes_of_interaction_Multi-future_Transformer_.jpg)
Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer
This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.
![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.
![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.
![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 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library](https://c.mql5.com/2/80/MQL5_Trading_Toolkit_Part_1_600x314.jpg)
MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library
Learn how to create a developer's toolkit for managing various position operations with MQL5. In this article, I will demonstrate how to create a library of functions (ex5) that will perform simple to advanced position management operations, including automatic handling and reporting of the different errors that arise when dealing with position management tasks with MQL5.
![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
![Gain An Edge Over Any Market (Part II): Forecasting Technical Indicators](https://c.mql5.com/2/80/Gain_An_Edge_Over_Any_Market_Part_II_600x314.jpg)
Gain An Edge Over Any Market (Part II): Forecasting Technical Indicators
Did you know that we can gain more accuracy forecasting certain technical indicators than predicting the underlying price of a traded symbol? Join us to explore how to leverage this insight for better trading strategies.
![Using optimization algorithms to configure EA parameters on the fly](https://c.mql5.com/2/69/Using_optimization_algorithms_to_configure_EA_parameters_on_the_fly_600x314.jpg)
Using optimization algorithms to configure EA parameters on the fly
The article discusses the practical aspects of using optimization algorithms to find the best EA parameters on the fly, as well as virtualization of trading operations and EA logic. The article can be used as an instruction for implementing optimization algorithms into an EA.
![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.
![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 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.
![Reimagining Classic Strategies: Crude Oil](https://c.mql5.com/2/79/Reimagining_Classic_Strategies____Crude_Oil_600x314.jpg)
Reimagining Classic Strategies: Crude Oil
In this article, we revisit a classic crude oil trading strategy with the aim of enhancing it by leveraging supervised machine learning algorithms. We will construct a least-squares model to predict future Brent crude oil prices based on the spread between Brent and WTI crude oil prices. Our goal is to identify a leading indicator of future changes in Brent prices.
![Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies](https://c.mql5.com/2/69/Developing_a_multi-currency_advisor_5Part_2f_Transition_to_virtual_positions_of_trading_strategies_6.jpg)
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.
![Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)](https://c.mql5.com/2/69/Population_optimization_algorithms___Artificial_Multi-Social_Search_Objects_zMSO4_600x314.jpg)
Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)
This is a continuation of the previous article considering the idea of social groups. The article explores the evolution of social groups using movement and memory algorithms. The results will help to understand the evolution of social systems and apply them in optimization and search for solutions.
![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.
![Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU](https://c.mql5.com/2/79/Integrate_Your_Own_LLM_into_EA__Part_3_-_Training_Your_Own_LLM_with_CPU_600x314.jpg)
Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU
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.
![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.
![Modified Grid-Hedge EA in MQL5 (Part IV): Optimizing Simple Grid Strategy (I)](https://c.mql5.com/2/79/Modified_Grid-Hedge_EA_in_MQL5_Part_III_600x314__1.jpg)
Modified Grid-Hedge EA in MQL5 (Part IV): Optimizing Simple Grid Strategy (I)
In this fourth part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Grid 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 70): Closed-Form Policy Improvement Operators (CFPI)](https://c.mql5.com/2/63/Neural_Networks_Made_Easy_0Part_70g_CFPI_600x314.jpg)
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.
![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.
![Developing a multi-currency Expert Advisor (Part 1): Collaboration of several trading strategies](https://c.mql5.com/2/65/Developing_a_multi-currency_advisor_tPart_10_600x314.jpg)
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.
![MQL5 Wizard Techniques you should know (Part 20): Symbolic Regression](https://c.mql5.com/2/78/MQL5_Wizard_Techniques_you_should_know_mPart_20b_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 20): Symbolic Regression
Symbolic Regression is a form of regression that starts with minimal to no assumptions on what the underlying model that maps the sets of data under study would look like. Even though it can be implemented by Bayesian Methods or Neural Networks, we look at how an implementation with Genetic Algorithms can help customize an expert signal class usable in the MQL5 wizard.
![Learn how to trade the Fair Value Gap (FVG)/Imbalances step-by-step: A Smart Money concept approach](https://c.mql5.com/2/78/Learn_how_to_trade_the_Fair_Value_Gap_600x314.jpg)
Learn how to trade the Fair Value Gap (FVG)/Imbalances step-by-step: A Smart Money concept approach
A step-by-step guide to creating and implementing an automated trading algorithm in MQL5 based on the Fair Value Gap (FVG) trading strategy. A detailed tutorial on creating an expert advisor that can be useful for both beginners and experienced traders.
![Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)](https://c.mql5.com/2/63/Upscales.ai_1703440115554_600x314.jpg)
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.
![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.
![Statistical Arbitrage with predictions](https://c.mql5.com/2/77/Statistical_Arbitrage_with_predictions_600x314.jpg)
Statistical Arbitrage with predictions
We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.
![Building A Candlestick Trend Constraint Model(Part 2): Merging Native Indicators](https://c.mql5.com/2/77/Building_A_Candlestick_Trend_Constraint_Model5Part_2s_600x314.jpg)
Building A Candlestick Trend Constraint Model(Part 2): Merging Native Indicators
This article focuses on taking advantage of in-built meta trader 5 indicators to screen out off-trend signals. Advancing from the previous article we will explore how to do it using MQL5 code to communicate our idea to the final program.
![Introduction to MQL5 (Part 7): Beginner's Guide to Building Expert Advisors and Utilizing AI-Generated Code in MQL5](https://c.mql5.com/2/77/Introduction_to_MQL5_sPart_7v_Beginnerys_Guide_to_Building_Expert_Advisors_and_Utilizing_AI-Generate.jpg)
Introduction to MQL5 (Part 7): Beginner's Guide to Building Expert Advisors and Utilizing AI-Generated Code in MQL5
Discover the ultimate beginner's guide to building Expert Advisors (EAs) with MQL5 in our comprehensive article. Learn step-by-step how to construct EAs using pseudocode and harness the power of AI-generated code. Whether you're new to algorithmic trading or seeking to enhance your skills, this guide provides a clear path to creating effective EAs.