MQL5 Wizard techniques you should know (Part 02): Kohonen Maps
These series of articles will proposition that the MQL5 Wizard should be a mainstay for traders. Why? Because not only does the trader save time by assembling his new ideas with the MQL5 Wizard, and greatly reduce mistakes from duplicate coding; he is ultimately set-up to channel his energy on the few critical areas of his trading philosophy.
Creating an EA that works automatically (Part 14): Automation (VI)
In this article, we will put into practice all the knowledge from this series. We will finally build a 100% automated and functional system. But before that, we still have to learn one last detail.
How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session
Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.
Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (StockFormer)
In this article, we will discuss the hybrid trading system StockFormer, which combines predictive coding and reinforcement learning (RL) algorithms. The framework uses 3 Transformer branches with an integrated Diversified Multi-Head Attention (DMH-Attn) mechanism that improves on the vanilla attention module with a multi-headed Feed-Forward block, allowing it to capture diverse time series patterns across different subspaces.
Working with GSM Modem from an MQL5 Expert Advisor
There is currently a fair number of means for a comfortable remote monitoring of a trading account: mobile terminals, push notifications, working with ICQ. But it all requires Internet connection. This article describes the process of creating an Expert Advisor that will allow you to stay in touch with your trading terminal even when mobile Internet is not available, through calls and text messaging.
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.
Neural networks made easy (Part 28): Policy gradient algorithm
We continue to study reinforcement learning methods. In the previous article, we got acquainted with the Deep Q-Learning method. In this method, the model is trained to predict the upcoming reward depending on the action taken in a particular situation. Then, an action is performed in accordance with the policy and the expected reward. But it is not always possible to approximate the Q-function. Sometimes its approximation does not generate the desired result. In such cases, approximation methods are applied not to utility functions, but to a direct policy (strategy) of actions. One of such methods is Policy Gradient.
Developing a trading Expert Advisor from scratch (Part 8): A conceptual leap
What is the easiest way to implement new functionality? In this article, we will take one step back and then two steps forward.
MQL5 Wizard Techniques you should know (Part 48): Bill Williams Alligator
The Alligator Indicator, which was the brain child of Bill Williams, is a versatile trend identification indicator that yields clear signals and is often combined with other indicators. The MQL5 wizard classes and assembly allow us to test a variety of signals on a pattern basis, and so we consider this indicator as well.
Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?
Are you tired of constantly trying to predict the stock market? Do you wish you had a crystal ball to help you make more informed investment decisions? Self-trained neural networks might be the solution you've been looking for. In this article, we explore whether these powerful algorithms can help you "ride the wave" and outsmart the stock market. By analyzing vast amounts of data and identifying patterns, self-trained neural networks can make predictions that are often more accurate than human traders. Discover how you can use this cutting-edge technology to maximize your profits and make smarter investment decisions.
Developing a trading Expert Advisor from scratch (Part 29): The talking platform
In this article, we will learn how to make the MetaTrader 5 platform talk. What if we make the EA more fun? Financial market trading is often too boring and monotonous, but we can make this job less tiring. Please note that this project can be dangerous for those who experience problems such as addiction. However, in a general case, it just makes things less boring.
Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor
Learn about the object-oriented programming paradigm and its application in MQL5 code. This second article goes deeper into the specifics of object-oriented programming, offering hands-on experience through a practical example. You'll learn how to convert our earlier developed procedural price action expert advisor using the EMA indicator and candlestick price data to object-oriented code.
Improve Your Trading Charts With Interactive GUI's in MQL5 (Part I): Movable GUI (I)
Unleash the power of dynamic data representation in your trading strategies or utilities with our comprehensive guide on creating movable GUI in MQL5. Dive into the core concept of chart events and learn how to design and implement simple and multiple movable GUI on the same chart. This article also explores the process of adding elements to your GUI, enhancing their functionality and aesthetic appeal.
Build Self Optimizing Expert Advisors in MQL5 (Part 5): Self Adapting Trading Rules
The best practices, defining how to safely us an indicator, are not always easy to follow. Quiet market conditions may surprisingly produce readings on the indicator that do not qualify as a trading signal, leading to missed opportunities for algorithmic traders. This article will suggest a potential solution to this problem, as we discuss how to build trading applications capable of adapting their trading rules to the available market data.
Neural networks made easy (Part 24): Improving the tool for Transfer Learning
In the previous article, we created a tool for creating and editing the architecture of neural networks. Today we will continue working on this tool. We will try to make it more user friendly. This may see, top be a step away form our topic. But don't you think that a well organized workspace plays an important role in achieving the result.
Neural networks made easy (Part 19): Association rules using MQL5
We continue considering association rules. In the previous article, we have discussed theoretical aspect of this type of problem. In this article, I will show the implementation of the FP Growth method using MQL5. We will also test the implemented solution using real data.
Statistical Arbitrage Through Mean Reversion in Pairs Trading: Beating the Market by Math
This article describes the fundamentals of portfolio-level statistical arbitrage. Its goal is to facilitate the understanding of the principles of statistical arbitrage to readers without deep math knowledge and propose a starting point conceptual framework. The article includes a working Expert Advisor, some notes about its one-year backtest, and the respective backtest configuration settings (.ini file) for the reproduction of the experiment.
How to create a custom True Strength Index indicator using MQL5
Here is a new article about how to create a custom indicator. This time we will work with the True Strength Index (TSI) and will create an Expert Advisor based on it.
Introduction to MQL5 (Part 19): Automating Wolfe Wave Detection
This article shows how to programmatically identify bullish and bearish Wolfe Wave patterns and trade them using MQL5. We’ll explore how to identify Wolfe Wave structures programmatically and execute trades based on them using MQL5. This includes detecting key swing points, validating pattern rules, and preparing the EA to act on the signals it finds.
Introduction to MQL5 (Part 16): Building Expert Advisors Using Technical Chart Patterns
This article introduces beginners to building an MQL5 Expert Advisor that identifies and trades a classic technical chart pattern — the Head and Shoulders. It covers how to detect the pattern using price action, draw it on the chart, set entry, stop loss, and take profit levels, and automate trade execution based on the pattern.
The Blue Monkey (BM) Algorithm
The article presents an implementation of the Blue Monkey metaheuristic algorithm, which is based on a model of the social behavior of blue monkeys. The article examines the key mechanisms of the algorithm — the group structure of the population, following local leaders, and generational renewal through the replacement of the worst adults with the best offspring — and analyzes the test results.
Prices in DoEasy library (part 59): Object to store data of one tick
From this article on, start creating library functionality to work with price data. Today, create an object class which will store all price data which arrived with yet another tick.
The Blue Monkey (BM) Algorithm
The article presents an implementation of the Blue Monkey metaheuristic algorithm, which is based on a model of the social behavior of blue monkeys. The article examines the key mechanisms of the algorithm — the group structure of the population, following local leaders, and generational renewal through the replacement of the worst adults with the best offspring — and analyzes the test results.
Creating an EA that works automatically (Part 05): Manual triggers (II)
Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. At the end of the previous article, I suggested that it would be appropriate to allow manual use of the EA, at least for a while.
Multiple indicators on one chart (Part 05): Turning MetaTrader 5 into a RAD system (I)
There are a lot of people who do not know how to program but they are quite creative and have great ideas. However, the lack of programming knowledge prevents them from implementing these ideas. Let's see together how to create a Chart Trade using the MetaTrader 5 platform itself, as if it were an IDE.
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.
MQL5 Trading Tools (Part 19): Building an Interactive Tools Palette for Chart Drawing
In this article, we build an interactive tools palette in MQL5 for chart drawing, with draggable, resizable panels and theme switching. We add buttons for tools like crosshair, trendlines, lines, rectangles, Fibonacci, text, and arrows, handling mouse events for activation and instructions. This system improves trading analysis through a customizable UI, supporting real-time interactions on charts
Parallel Particle Swarm Optimization
The article describes a method of fast optimization using the particle swarm algorithm. It also presents the method implementation in MQL, which is ready for use both in single-threaded mode inside an Expert Advisor and in a parallel multi-threaded mode as an add-on that runs on local tester agents.
Introduction to MQL5 (Part 25): Building an EA that Trades with Chart Objects (II)
This article explains how to build an Expert Advisor (EA) that interacts with chart objects, particularly trend lines, to identify and trade breakout and reversal opportunities. You will learn how the EA confirms valid signals, manages trade frequency, and maintains consistency with user-selected strategies.
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.
Developing a trading Expert Advisor from scratch (Part 22): New order system (V)
Today we will continue to develop the new order system. It is not that easy to implement a new system as we often encounter problems which greatly complicate the process. When these problems appear, we have to stop and re-analyze the direction in which we are moving.
Introduction to MQL5 (Part 24): Building an EA that Trades with Chart Objects
This article teaches you how to create an Expert Advisor that detects support and resistance zones drawn on the chart and executes trades automatically based on them.
Neural Networks in Trading: A Multi-Agent System with Conceptual Reinforcement (FinCon)
We invite you to explore the FinCon framework, which is a a Large Language Model (LLM)-based multi-agent system. The framework uses conceptual verbal reinforcement to improve decision making and risk management, enabling effective performance on a variety of financial tasks.
Data Science and ML (Part 37): Using Candlestick patterns and AI to beat the market
Candlestick patterns help traders understand market psychology and identify trends in financial markets, they enable more informed trading decisions that can lead to better outcomes. In this article, we will explore how to use candlestick patterns with AI models to achieve optimal trading performance.
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.
Experiments with neural networks (Part 5): Normalizing inputs for passing to a neural network
Neural networks are an ultimate tool in traders' toolkit. Let's check if this assumption is true. MetaTrader 5 is approached as a self-sufficient medium for using neural networks in trading. A simple explanation is provided.
Data Science and Machine Learning (Part 06): Gradient Descent
The gradient descent plays a significant role in training neural networks and many machine learning algorithms. It is a quick and intelligent algorithm despite its impressive work it is still misunderstood by a lot of data scientists let's see what it is all about.
Neural Networks in Trading: An Agent with Layered Memory
Layered memory approaches that mimic human cognitive processes enable the processing of complex financial data and adaptation to new signals, thereby improving the effectiveness of investment decisions in dynamic markets.
How to build and optimize a volume-based trading system (Chaikin Money Flow - CMF)
In this article, we will provide a volume-based indicator, Chaikin Money Flow (CMF) after identifying how it can be constructed, calculated, and used. We will understand how to build a custom indicator. We will share some simple strategies that can be used and then test them to understand which one is better.
Neural networks made easy (Part 67): Using past experience to solve new tasks
In this article, we continue discussing methods for collecting data into a training set. Obviously, the learning process requires constant interaction with the environment. However, situations can be different.