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Simplifying Databases in MQL5 (Part 1): Introduction to Databases and SQL
Simplifying Databases in MQL5 (Part 1): Introduction to Databases and SQL
We explore how to manipulate databases in MQL5 using the language's native functions. We cover everything from table creation, insertion, updating, and deletion to data import and export, all with sample code. The content serves as a solid foundation for understanding the internal mechanics of data access, paving the way for the discussion of ORM, where we'll build one in MQL5.
Getting Started with MQL5 Algo Forge
Getting Started with MQL5 Algo Forge
We are introducing MQL5 Algo Forge — a dedicated portal for algorithmic trading developers. It combines the power of Git with an intuitive interface for managing and organizing projects within the MQL5 ecosystem. Here, you can follow interesting authors, form teams, and collaborate on algorithmic trading projects.
From Novice to Expert: Mastering Detailed Trading Reports with Reporting EA
From Novice to Expert: Mastering Detailed Trading Reports with Reporting EA
In this article, we delve into enhancing the details of trading reports and delivering the final document via email in PDF format. This marks a progression from our previous work, as we continue exploring how to harness the power of MQL5 and Python to generate and schedule trading reports in the most convenient and professional formats. Join us in this discussion to learn more about optimizing trading report generation within the MQL5 ecosystem.
MQL5 Wizard Techniques you should know (Part 79): Using Gator Oscillator and Accumulation/Distribution Oscillator with Supervised Learning
MQL5 Wizard Techniques you should know (Part 79): Using Gator Oscillator and Accumulation/Distribution Oscillator with Supervised Learning
In the last piece, we concluded our look at the pairing of the gator oscillator and the accumulation/distribution oscillator when used in their typical setting of the raw signals they generate. These two indicators are complimentary as trend and volume indicators, respectively. We now follow up that piece, by examining the effect that supervised learning can have on enhancing some of the feature patterns we had reviewed. Our supervised learning approach is a CNN that engages with kernel regression and dot product similarity to size its kernels and channels. As always, we do this in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
CRUD Operations in Firebase using MQL
CRUD Operations in Firebase using MQL
This article offers a step-by-step guide to mastering CRUD (Create, Read, Update, Delete) operations in Firebase, focusing on its Realtime Database and Firestore. Discover how to use Firebase SDK methods to efficiently manage data in web and mobile apps, from adding new records to querying, modifying, and deleting entries. Explore practical code examples and best practices for structuring and handling data in real-time, empowering developers to build dynamic, scalable applications with Firebase’s flexible NoSQL architecture.
Creating 3D bars based on time, price and volume
Creating 3D bars based on time, price and volume
The article dwells on multivariate 3D price charts and their creation. We will also consider how 3D bars predict price reversals, and how Python and MetaTrader 5 allow us to plot these volume bars in real time.
Developing a multi-currency Expert Advisor (Part 19): Creating stages implemented in Python
Developing a multi-currency Expert Advisor (Part 19): Creating stages implemented in Python
So far we have considered the automation of launching sequential procedures for optimizing EAs exclusively in the standard strategy tester. But what if we would like to perform some handling of the obtained data using other means between such launches? We will attempt to add the ability to create new optimization stages performed by programs written in Python.
Python-MetaTrader 5 Strategy Tester (Part 01): Trade Simulator
Python-MetaTrader 5 Strategy Tester (Part 01): Trade Simulator
The MetaTrader 5 module offered in Python provides a convenient way of opening trades in the MetaTrader 5 app using Python, but it has a huge problem, it doesn't have the strategy tester capability present in the MetaTrader 5 app, In this article series, we will build a framework for back testing your trading strategies in Python environments.
MetaTrader tick info access from MQL5 services to Python application using sockets
MetaTrader tick info access from MQL5 services to Python application using sockets
Sometimes everything is not programmable in the MQL5 language. And even if it is possible to convert existing advanced libraries in MQL5, it would be time-consuming. This article tries to show that we can bypass Windows OS dependency by transporting tick information such as bid, ask and time with MetaTrader services to a Python application using sockets.
From Novice to Expert: Animated News Headline Using MQL5 (VIII) — Quick Trade Buttons for News Trading
From Novice to Expert: Animated News Headline Using MQL5 (VIII) — Quick Trade Buttons for News Trading
While algorithmic trading systems manage automated operations, many news traders and scalpers prefer active control during high-impact news events and fast-paced market conditions, requiring rapid order execution and management. This underscores the need for intuitive front-end tools that integrate real-time news feeds, economic calendar data, indicator insights, AI-driven analytics, and responsive trading controls.
ALGLIB library optimization methods (Part II)
ALGLIB library optimization methods (Part II)
In this article, we will continue to study the remaining optimization methods from the ALGLIB library, paying special attention to their testing on complex multidimensional functions. This will allow us not only to evaluate the efficiency of each algorithm, but also to identify their strengths and weaknesses in different conditions.
Portfolio optimization in Forex: Synthesis of VaR and Markowitz theory
Portfolio optimization in Forex: Synthesis of VaR and Markowitz theory
How does portfolio trading work on Forex? How can Markowitz portfolio theory for portfolio proportion optimization and VaR model for portfolio risk optimization be synthesized? We create a code based on portfolio theory, where, on the one hand, we will get low risk, and on the other, acceptable long-term profitability.
Algorithmic trading based on 3D reversal patterns
Algorithmic trading based on 3D reversal patterns
Discovering a new world of automated trading on 3D bars. What does a trading robot look like on multidimensional price bars? Are "yellow" clusters of 3D bars able to predict trend reversals? What does multidimensional trading look like?
From Novice to Expert: Reporting EA — Setting up the work flow
From Novice to Expert: Reporting EA — Setting up the work flow
Brokerages often provide trading account reports at regular intervals, based on a predefined schedule. These firms, through their API technologies, have access to your account activity and trading history, allowing them to generate performance reports on your behalf. Similarly, the MetaTrader 5 terminal stores detailed records of your trading activity, which can be leveraged using MQL5 to create fully customized reports and define personalized delivery methods.
Price Action Analysis Toolkit Development (Part 34): Turning Raw Market Data into Predictive Models Using an Advanced Ingestion Pipeline
Price Action Analysis Toolkit Development (Part 34): Turning Raw Market Data into Predictive Models Using an Advanced Ingestion Pipeline
Have you ever missed a sudden market spike or been caught off‑guard when one occurred? The best way to anticipate live events is to learn from historical patterns. Intending to train an ML model, this article begins by showing you how to create a script in MetaTrader 5 that ingests historical data and sends it to Python for storage—laying the foundation for your spike‑detection system. Read on to see each step in action.
Utilizing CatBoost Machine Learning model as a Filter for Trend-Following Strategies
Utilizing CatBoost Machine Learning model as a Filter for Trend-Following Strategies
CatBoost is a powerful tree-based machine learning model that specializes in decision-making based on stationary features. Other tree-based models like XGBoost and Random Forest share similar traits in terms of their robustness, ability to handle complex patterns, and interpretability. These models have a wide range of uses, from feature analysis to risk management. In this article, we're going to walk through the procedure of utilizing a trained CatBoost model as a filter for a classic moving average cross trend-following strategy.
Twitter Sentiment Analysis with Sockets
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.
From Novice to Expert: Animated News Headline Using MQL5 (VII) — Post Impact Strategy for News Trading
From Novice to Expert: Animated News Headline Using MQL5 (VII) — Post Impact Strategy for News Trading
The risk of whipsaw is extremely high during the first minute following a high-impact economic news release. In that brief window, price movements can be erratic and volatile, often triggering both sides of pending orders. Shortly after the release—typically within a minute—the market tends to stabilize, resuming or correcting the prevailing trend with more typical volatility. In this section, we’ll explore an alternative approach to news trading, aiming to assess its effectiveness as a valuable addition to a trader’s toolkit. Continue reading for more insights and details in this discussion.
Population ADAM (Adaptive Moment Estimation)
Population ADAM (Adaptive Moment Estimation)
The article presents the transformation of the well-known and popular ADAM gradient optimization method into a population algorithm and its modification with the introduction of hybrid individuals. The new approach allows creating agents that combine elements of successful decisions using probability distribution. The key innovation is the formation of hybrid population individuals that adaptively accumulate information from the most promising solutions, increasing the efficiency of search in complex multidimensional spaces.
MQL5 Wizard Techniques you should know (Part 76): Using Patterns of Awesome Oscillator and the Envelope Channels with Supervised Learning
MQL5 Wizard Techniques you should know (Part 76): Using Patterns of Awesome Oscillator and the Envelope Channels with Supervised Learning
We follow up on our last article, where we introduced the indicator couple of the Awesome-Oscillator and the Envelope Channel, by looking at how this pairing could be enhanced with Supervised Learning. The Awesome-Oscillator and Envelope-Channel are a trend-spotting and support/resistance complimentary mix. Our supervised learning approach is a CNN that engages the Dot Product Kernel with Cross-Time-Attention to size its kernels and channels. As per usual, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
From Novice to Expert: Animated News Headline Using MQL5 (VI) — Pending Order Strategy for News Trading
From Novice to Expert: Animated News Headline Using MQL5 (VI) — Pending Order Strategy for News Trading
In this article, we shift focus toward integrating news-driven order execution logic—enabling the EA to act, not just inform. Join us as we explore how to implement automated trade execution in MQL5 and extend the News Headline EA into a fully responsive trading system. Expert Advisors offer significant advantages for algorithmic developers thanks to the wide range of features they support. So far, we’ve focused on building a news and calendar events presentation tool, complete with integrated AI insights lanes and technical indicator insights.
Creating a Trading Administrator Panel in MQL5 (Part X): External resource-based interface
Creating a Trading Administrator Panel in MQL5 (Part X): External resource-based interface
Today, we are harnessing the capabilities of MQL5 to utilize external resources—such as images in the BMP format—to create a uniquely styled home interface for the Trading Administrator Panel. The strategy demonstrated here is particularly useful when packaging multiple resources, including images, sounds, and more, for streamlined distribution. Join us in this discussion as we explore how these features are implemented to deliver a modern and visually appealing interface for our New_Admin_Panel EA.
MQL5 Wizard Techniques you should know (Part 58): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns
MQL5 Wizard Techniques you should know (Part 58): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns
Moving Average and Stochastic Oscillator are very common indicators whose collective patterns we explored in the prior article, via a supervised learning network, to see which “patterns-would-stick”. We take our analyses from that article, a step further by considering the effects' reinforcement learning, when used with this trained network, would have on performance. Readers should note our testing is over a very limited time window. Nonetheless, we continue to harness the minimal coding requirements afforded by the MQL5 wizard in showcasing this.
Advanced Memory Management and Optimization Techniques in MQL5
Advanced Memory Management and Optimization Techniques in MQL5
Discover practical techniques to optimize memory usage in MQL5 trading systems. Learn to build efficient, stable, and fast-performing Expert Advisors and indicators. We’ll explore how memory really works in MQL5, the common traps that slow your systems down or cause them to fail, and — most importantly — how to fix them.
Non-linear regression models on the stock exchange
Non-linear regression models on the stock exchange
Non-linear regression models on the stock exchange: Is it possible to predict financial markets? Let's consider creating a model for forecasting prices for EURUSD, and make two robots based on it - in Python and MQL5.
Price Action Analysis Toolkit Development (Part 31): Python Candlestick Recognition Engine (I) — Manual Detection
Price Action Analysis Toolkit Development (Part 31): Python Candlestick Recognition Engine (I) — Manual Detection
Candlestick patterns are fundamental to price-action trading, offering valuable insights into potential market reversals or continuations. Envision a reliable tool that continuously monitors each new price bar, identifies key formations such as engulfing patterns, hammers, dojis, and stars, and promptly notifies you when a significant trading setup is detected. This is precisely the functionality we have developed. Whether you are new to trading or an experienced professional, this system provides real-time alerts for candlestick patterns, enabling you to focus on executing trades with greater confidence and efficiency. Continue reading to learn how it operates and how it can enhance your trading strategy.
Graph Theory: Dijkstra's Algorithm Applied in Trading
Graph Theory: Dijkstra's Algorithm Applied in Trading
Dijkstra's algorithm, a classic shortest-path solution in graph theory, can optimize trading strategies by modeling market networks. Traders can use it to find the most efficient routes in the candlestick chart data.
From Novice to Expert: Animated News Headline Using MQL5 (IV) — Locally hosted AI model market insights
From Novice to Expert: Animated News Headline Using MQL5 (IV) — Locally hosted AI model market insights
In today's discussion, we explore how to self-host open-source AI models and use them to generate market insights. This forms part of our ongoing effort to expand the News Headline EA, introducing an AI Insights Lane that transforms it into a multi-integration assistive tool. The upgraded EA aims to keep traders informed through calendar events, financial breaking news, technical indicators, and now AI-generated market perspectives—offering timely, diverse, and intelligent support to trading decisions. Join the conversation as we explore practical integration strategies and how MQL5 can collaborate with external resources to build a powerful and intelligent trading work terminal.
Using association rules in Forex data analysis
Using association rules in Forex data analysis
How to apply predictive rules of supermarket retail analytics to the real Forex market? How are purchases of cookies, milk and bread related to stock exchange transactions? The article discusses an innovative approach to algorithmic trading based on the use of association rules.