![Creating an MQL5-Telegram Integrated Expert Advisor (Part 3): Sending Chart Screenshots with Captions from MQL5 to Telegram](https://c.mql5.com/2/89/Screenshot_2024-08-19_131243_600x314.jpg)
Creating an MQL5-Telegram Integrated Expert Advisor (Part 3): Sending Chart Screenshots with Captions from MQL5 to Telegram
In this article, we create an MQL5 Expert Advisor that encodes chart screenshots as image data and sends them to a Telegram chat via HTTP requests. By integrating photo encoding and transmission, we enhance the existing MQL5-Telegram system with visual trading insights directly within Telegram.
![Reimagining Classic Strategies (Part VI): Multiple Time-Frame Analysis](https://c.mql5.com/2/89/midjourney_image_15610_407_3930_2_600x314.jpg)
Reimagining Classic Strategies (Part VI): Multiple Time-Frame Analysis
In this series of articles, we revisit classic strategies to see if we can improve them using AI. In today's article, we will examine the popular strategy of multiple time-frame analysis to judge if the strategy would be enhanced with AI.
![Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR](https://c.mql5.com/2/89/midjourney_image_15570_399_3853_600x314.jpg)
Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR
In this series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.
![Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes](https://c.mql5.com/2/90/Reimagining_Classic_Strategies_kPart_IV3_600x314.jpg)
Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes
In this series of articles, we analyze classical trading strategies using modern algorithms to determine whether we can improve the strategy using AI. In today's article, we revisit a classical approach for trading the SP500 using the relationship it has with US Treasury Notes.
![News Trading Made Easy (Part 3): Performing Trades](https://c.mql5.com/2/88/news-trading-made-easy-3_600x314.jpg)
News Trading Made Easy (Part 3): Performing Trades
In this article, our news trading expert will begin opening trades based on the economic calendar stored in our database. In addition, we will improve the expert's graphics to display more relevant information about upcoming economic calendar events.
![Creating an MQL5-Telegram Integrated Expert Advisor (Part 2): Sending Signals from MQL5 to Telegram](https://c.mql5.com/2/88/midjourney_image_15495_381_3675_1_600x314.jpg)
Creating an MQL5-Telegram Integrated Expert Advisor (Part 2): Sending Signals from MQL5 to Telegram
In this article, we create an MQL5-Telegram integrated Expert Advisor that sends moving average crossover signals to Telegram. We detail the process of generating trading signals from moving average crossovers, implementing the necessary code in MQL5, and ensuring the integration works seamlessly. The result is a system that provides real-time trading alerts directly to your Telegram group chat.
![Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA](https://c.mql5.com/2/88/Example_of_Auto_Optimized_TPs_and_Indicators_parameters_with_SMA_and_EMA__600x314.jpg)
Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA
This article presents a sophisticated Expert Advisor for forex trading, combining machine learning with technical analysis. It focuses on trading Apple stock, featuring adaptive optimization, risk management, and multiple strategies. Backtesting shows promising results with high profitability but also significant drawdowns, indicating potential for further refinement.
![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.
![Creating an MQL5-Telegram Integrated Expert Advisor (Part 1): Sending Messages from MQL5 to Telegram](https://c.mql5.com/2/88/Creating_an_MQL5-Telegram_Integrated_Expert_Advisor_tPart_1w_600x314.jpg)
Creating an MQL5-Telegram Integrated Expert Advisor (Part 1): Sending Messages from MQL5 to Telegram
In this article, we create an Expert Advisor (EA) in MQL5 to send messages to Telegram using a bot. We set up the necessary parameters, including the bot's API token and chat ID, and then perform an HTTP POST request to deliver the messages. Later, we handle the response to ensure successful delivery and troubleshoot any issues that arise in case of failure. This ensures we send messages from MQL5 to Telegram via the created bot.
![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.
![Neural networks made easy (Part 82): Ordinary Differential Equation models (NeuralODE)](https://c.mql5.com/2/73/Neural_networks_are_easy_Part_82_600x314__1.jpg)
Neural networks made easy (Part 82): Ordinary Differential Equation models (NeuralODE)
In this article, we will discuss another type of models that are aimed at studying the dynamics of the environmental state.
![Build Self Optimizing Expert Advisors With MQL5 And Python (Part II): Tuning Deep Neural Networks](https://c.mql5.com/2/87/Build_Self_Optimizing_Expert_Advisors_With_MQL5_And_Python_Part_II_600x314.jpg)
Build Self Optimizing Expert Advisors With MQL5 And Python (Part II): Tuning Deep Neural Networks
Machine learning models come with various adjustable parameters. In this series of articles, we will explore how to customize your AI models to fit your specific market using the SciPy library.
![Risk manager for manual trading](https://c.mql5.com/2/73/Risk_manager_for_manual_trading_600x314.jpg)
Risk manager for manual trading
In this article we will discuss in detail how to write a risk manager class for manual trading from scratch. This class can also be used as a base class for inheritance by algorithmic traders who use automated programs.
![Reimagining Classic Strategies (Part III): Forecasting Higher Highs And Lower Lows](https://c.mql5.com/2/86/Reimagining_Classic_Strategies__Forecasting_Higher_Highs_And_Lower_Lows_600x314.jpg)
Reimagining Classic Strategies (Part III): Forecasting Higher Highs And Lower Lows
In this series article, we will empirically analyze classic trading strategies to see if we can improve them using AI. In today's discussion, we tried to predict higher highs and lower lows using the Linear Discriminant Analysis model.
![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.
![Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5](https://c.mql5.com/2/86/Creating_a_Dynamic_Multi-Symbol_Indicator_Dashboard_in_MQL5_600x314.jpg)
Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5
In this article, we develop a dynamic multi-symbol, multi-period RSI indicator dashboard in MQL5, providing traders real-time RSI values across various symbols and timeframes. The dashboard features interactive buttons, real-time updates, and color-coded indicators to help traders make informed decisions.
![Practicing the development of trading strategies](https://c.mql5.com/2/73/Experience_in_developing_a_trading_strategy_600x314.jpg)
Practicing the development of trading strategies
In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.
![Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)](https://c.mql5.com/2/73/Neural_networks_are_easy_Part_81_600x314.jpg)
Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)
In previous works, we always assessed the current state of the environment. At the same time, the dynamics of changes in indicators always remained "behind the scenes". In this article I want to introduce you to an algorithm that allows you to evaluate the direct change in data between 2 successive environmental states.
![Twitter Sentiment Analysis with Sockets](https://c.mql5.com/2/86/Twitter_Sentiment_Analysis_with_Sockets_600x314.jpg)
Twitter Sentiment Analysis with Sockets
This innovative trading bot integrates MetaTrader 5 with Python to leverage real-time social media sentiment analysis for automated trading decisions. By analyzing Twitter sentiment related to specific financial instruments, the bot translates social media trends into actionable trading signals. It utilizes a client-server architecture with socket communication, enabling seamless interaction between MT5's trading capabilities and Python's data processing power. The system demonstrates the potential of combining quantitative finance with natural language processing, offering a cutting-edge approach to algorithmic trading that capitalizes on alternative data sources. While showing promise, the bot also highlights areas for future enhancement, including more advanced sentiment analysis techniques and improved risk management strategies.
![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.
![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.
![Combine Fundamental And Technical Analysis Strategies in MQL5 For Beginners](https://c.mql5.com/2/85/Combine_Fundamental_And_Technical_Analysis_Strategies_in_MQL5_For_Beginners_600x314.jpg)
Combine Fundamental And Technical Analysis Strategies in MQL5 For Beginners
In this article, we will discuss how to integrate trend following and fundamental principles seamlessly into one Expert Advisors to build a strategy that is more robust. This article will demonstrate how easy it is for anyone to get up and running building customized trading algorithms using MQL5.
![Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)](https://c.mql5.com/2/84/Introduction_to_MQL5_Part_8_Beginners_Guide_to_Building_Expert_Advisors_600x314.jpg)
Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)
This article addresses common beginner questions from MQL5 forums and demonstrates practical solutions. Learn to perform essential tasks like buying and selling, obtaining candlestick prices, and managing automated trading aspects such as trade limits, trading periods, and profit/loss thresholds. Get step-by-step guidance to enhance your understanding and implementation of these concepts in MQL5.
![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
![Cascade Order Trading Strategy Based on EMA Crossovers for MetaTrader 5](https://c.mql5.com/2/84/Cascade_Order_Trading_Strategy_Based_on_EMA_Crossovers_600x314.jpg)
Cascade Order Trading Strategy Based on EMA Crossovers for MetaTrader 5
The article guides in demonstrating an automated algorithm based on EMA Crossovers for MetaTrader 5. Detailed information on all aspects of demonstrating an Expert Advisor in MQL5 and testing it in MetaTrader 5 - from analyzing price range behaviors to risk management.
![Creating an Interactive Graphical User Interface in MQL5 (Part 2): Adding Controls and Responsiveness](https://c.mql5.com/2/84/Creating_an_Interactive_Graphical_User_Interface_in_MQL5_Part_2_600x314.jpg)
Creating an Interactive Graphical User Interface in MQL5 (Part 2): Adding Controls and Responsiveness
Enhancing the MQL5 GUI panel with dynamic features can significantly improve the trading experience for users. By incorporating interactive elements, hover effects, and real-time data updates, the panel becomes a powerful tool for modern traders.
![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.
![How to Integrate Smart Money Concepts (BOS) Coupled with the RSI Indicator into an EA](https://c.mql5.com/2/83/Integrate_Smart_Money_Concepts_coupled_with_the_RSI_Indicator_into_an_EA_600x314.jpg)
How to Integrate Smart Money Concepts (BOS) Coupled with the RSI Indicator into an EA
Smart Money Concept (Break Of Structure) coupled with the RSI Indicator to make in informed automated trading decisions based on the market structure.
![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.
![Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state](https://c.mql5.com/2/71/Neural_networks_are_easy_Part_79_600x314.jpg)
Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state
In the previous article, we got acquainted with one of the methods for detecting objects in an image. However, processing a static image is somewhat different from working with dynamic time series, such as the dynamics of the prices we analyze. In this article, we will consider the method of detecting objects in video, which is somewhat closer to the problem we are solving.
![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.
![Creating an Interactive Graphical User Interface in MQL5 (Part 1): Making the Panel](https://c.mql5.com/2/83/Creating_an_Interactive_Graphical_User_Interface_in_MQL5_Part_1_600x314.jpg)
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.
![Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python](https://c.mql5.com/2/83/Sentiment_Analysis_and_Deep_Learning_for_Trading_with_EA_and_Back-testing_with_Python_600x314__1.jpg)
Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python
In this article, we will introduce Sentiment Analysis and ONNX Models with Python to be used in an EA. One script runs a trained ONNX model from TensorFlow for deep learning predictions, while another fetches news headlines and quantifies sentiment using AI.
![Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)](https://c.mql5.com/2/70/Neural_networks_made_easy_2Part_77g__Cross-Covariance_Transformer_iXCiTe_600x314.jpg)
Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)
In our models, we often use various attention algorithms. And, probably, most often we use Transformers. Their main disadvantage is the resource requirement. In this article, we will consider a new algorithm that can help reduce computing costs without losing quality.
![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.
![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.
![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.