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.
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.
Ten Basic Errors of a Newcomer in Trading
There are ten basic errors of a newcomer intrading: trading at market opening, undue hurry in taking profit, adding of lots in a losing position, closing positions starting with the best one, revenge, the most preferable positions, trading by the principle of 'bought for ever', closing of a profitable strategic position on the first day, closing of a position when alerted to open an opposite position, doubts.
Using PSAR, Heiken Ashi, and Deep Learning Together for Trading
This project explores the fusion of deep learning and technical analysis to test trading strategies in forex. A Python script is used for rapid experimentation, employing an ONNX model alongside traditional indicators like PSAR, SMA, and RSI to predict EUR/USD movements. A MetaTrader 5 script then brings this strategy into a live environment, using historical data and technical analysis to make informed trading decisions. The backtesting results indicate a cautious yet consistent approach, with a focus on risk management and steady growth rather than aggressive profit-seeking.
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.
Timeseries in DoEasy library (part 50): Multi-period multi-symbol standard indicators with a shift
In the article, let’s improve library methods for correct display of multi-symbol multi-period standard indicators, which lines are displayed on the current symbol chart with a shift set in the settings. As well, let’s put things in order in methods of work with standard indicators and remove the redundant code to the library area in the final indicator program.
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.
Developing a multi-currency Expert Advisor (Part 12): Developing prop trading level risk manager
In the EA being developed, we already have a certain mechanism for controlling drawdown. But it is probabilistic in nature, as it is based on the results of testing on historical price data. Therefore, the drawdown can sometimes exceed the maximum expected values (although with a small probability). Let's try to add a mechanism that ensures guaranteed compliance with the specified drawdown level.
Price Action Analysis Toolkit Development (Part 65): Building an MQL5 System to Monitor and Analyze Manually Drawn Fibonacci Levels
The Fibonacci retracement tool is an essential component of price action analysis, providing critical levels for potential market reactions. However, its effectiveness is often limited by the need for continuous human monitoring, which can lead to missed setups. In this part of our series, we introduce a tool that synchronizes and actively monitors manually drawn Fibonacci levels using MQL5, combining discretionary insight with automated oversight.
Data Science and ML (Part 33): Pandas Dataframe in MQL5, Data Collection for ML Usage made easier
When working with machine learning models, it’s essential to ensure consistency in the data used for training, validation, and testing. In this article, we will create our own version of the Pandas library in MQL5 to ensure a unified approach for handling machine learning data, for ensuring the same data is applied inside and outside MQL5, where most of the training occurs.
How to Use Crashlogs to Debug Your Own DLLs
25 to 30% of all crashlogs received from users appear due to errors occurring when functions imported from custom dlls are executed.
Understand and Efficiently use OpenCL API by Recreating built-in support as DLL on Linux (Part 2): OpenCL Simple DLL implementation
Continued from the part 1 in the series, now we proceed to implement as a simple DLL then test with MetaTrader 5. This will prepare us well before developing a full-fledge OpenCL as DLL support in the following part to come.
Mastering PD Arrays: Optimizing Trading from Imbalances in PD Arrays
This is an article about a specialized trend-following EA that aims to clearly elaborate how to frame and utilize trading setups that occur from imbalances found in PD arrays. This article will explore in detail an EA that is specifically designed for traders who are keen on optimizing and utilizing PD arrays and imbalances as entry criteria for their trades and trading decisions. It will also explore how to correctly determine and profile premium and discount arrays and how to validate and utilize each of them when they occur in their respective market conditions, thus trying to maximize opportunities that occur from such scenarios.
From Novice to Expert: Collaborative Debugging in MQL5
Problem-solving can establish a concise routine for mastering complex skills, such as programming in MQL5. This approach allows you to concentrate on solving problems while simultaneously developing your skills. The more problems you tackle, the more advanced expertise is transferred to your brain. Personally, I believe that debugging is the most effective way to master programming. Today, we will walk through the code-cleaning process and discuss the best techniques for transforming a messy program into a clean, functional one. Read through this article and uncover valuable insights.
Quantum computing and trading: A fresh approach to price forecasts
The article describes an innovative approach to forecasting price movements in financial markets using quantum computing. The main focus is on the application of the Quantum Phase Estimation (QPE) algorithm to find prototypes of price patterns allowing traders to significantly speed up the market data analysis.
Pipelines in MQL5
In this piece, we look at a key data preparation step for machine learning that is gaining rapid significance. Data Preprocessing Pipelines. These in essence are a streamlined sequence of data transformation steps that prepare raw data before it is fed to a model. As uninteresting as this may initially seem to the uninducted, this ‘data standardization’ not only saves on training time and execution costs, but it goes a long way in ensuring better generalization. In this article we are focusing on some SCIKIT-LEARN preprocessing functions, and while we are not exploiting the MQL5 Wizard, we will return to it in coming articles.
Betting Modeling as Means of Developing "Market Intuition"
The article dwells on the notion of "market intuition" and ways of developing it. The method described in the article is based on the modeling of financial betting in the form of a simple game.
Risk Management (Part 2): Implementing Lot Calculation in a Graphical Interface
In this article, we will look at how to improve and more effectively apply the concepts presented in the previous article using the powerful MQL5 graphical control libraries. We'll go step by step through the process of creating a fully functional GUI. I'll be explaining the ideas behind it, as well as the purpose and operation of each method used. Additionally, at the end of the article, we will test the panel we created to ensure it functions correctly and meets its stated goals.
MQL5 Trading Tools (Part 7): Informational Dashboard for Multi-Symbol Position and Account Monitoring
In this article, we develop an informational dashboard in MQL5 for monitoring multi-symbol positions and account metrics like balance, equity, and free margin. We implement a sortable grid with real-time updates, CSV export, and a glowing header effect to enhance usability and visual appeal.
Moving to MQL5 Algo Forge (Part 2): Working with Multiple Repositories
In this article, we are considering one of the possible approaches to organizing the storage of the project's source code in a public repository. We will distribute the code across different branches to establish clear and convenient rules for the project development.
MetaTrader 5 Machine Learning Blueprint (Part 8): Bayesian Hyperparameter Optimization with Purged Cross-Validation and Trial Pruning
GridSearchCV and RandomizedSearchCV share a fundamental limitation in financial ML: each trial is independent, so search quality does not improve with additional compute. This article integrates Optuna — using the Tree-structured Parzen Estimator — with PurgedKFold cross-validation, HyperbandPruner early stopping, and a dual-weight convention that separates training weights from evaluation weights. The result is a five-component system: an objective function with fold-level pruning, a suggestion layer that optimizes the weighting scheme jointly with model hyperparameters, a financially-calibrated pruner, a resumable SQLite-backed orchestrator, and a converter to scikit-learn cv_results_ format. The article also establishes the boundary — drawn from Timothy Masters — between statistical objectives where directed search is beneficial and financial objectives where it is harmful.
Introduction to MQL5 (Part 21): Automating Harmonic Pattern Detection
Learn how to detect and display the Gartley harmonic pattern in MetaTrader 5 using MQL5. This article explains each step of the process, from identifying swing points to applying Fibonacci ratios and plotting the full pattern on the chart for clear visual confirmation.
Self Optimizing Expert Advisor With MQL5 And Python (Part V): Deep Markov Models
In this discussion, we will apply a simple Markov Chain on an RSI Indicator, to observe how price behaves after the indicator passes through key levels. We concluded that the strongest buy and sell signals on the NZDJPY pair are generated when the RSI is in the 11-20 range and 71-80 range, respectively. We will demonstrate how you can manipulate your data, to create optimal trading strategies that are learned directly from the data you have. Furthermore, we will demonstrate how to train a deep neural network to learn to use the transition matrix optimally.
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.
News Trading Made Easy (Part 6): Performing Trades (III)
In this article news filtration for individual news events based on their IDs will be implemented. In addition, previous SQL queries will be improved to provide additional information or reduce the query's runtime. Furthermore, the code built in the previous articles will be made functional.
MQL5 Wizard Techniques you should know (Part 78): Gator and AD Oscillator Strategies for Market Resilience
The article presents the second half of a structured approach to trading with the Gator Oscillator and Accumulation/Distribution. By introducing five new patterns, the author shows how to filter false moves, detect early reversals, and align signals across timeframes. With clear coding examples and performance tests, the material bridges theory and practice for MQL5 developers.
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.
Integrating MQL5 with data processing packages (Part 2): Machine Learning and Predictive Analytics
In our series on integrating MQL5 with data processing packages, we delve in to the powerful combination of machine learning and predictive analysis. We will explore how to seamlessly connect MQL5 with popular machine learning libraries, to enable sophisticated predictive models for financial markets.
Interview with Atsushi Yamanaka (ATC 2011)
What is common between skydiving, Futures, Hawaii, translations and spies? We didn't know it until we've managed to communicate with disqualified participant Atsushi Yamanaka (alohafx). His has a creed "Life is Good!", and one can hardly doubt that. It was interesting to know that distances between the continents are not an obstacle for communication among our Championship's participants.
Population optimization algorithms: Cuckoo Optimization Algorithm (COA)
The next algorithm I will consider is cuckoo search optimization using Levy flights. This is one of the latest optimization algorithms and a new leader in the leaderboard.
Neural networks made easy (Part 44): Learning skills with dynamics in mind
In the previous article, we introduced the DIAYN method, which offers the algorithm for learning a variety of skills. The acquired skills can be used for various tasks. But such skills can be quite unpredictable, which can make them difficult to use. In this article, we will look at an algorithm for learning predictable skills.
Multiple Symbol Analysis With Python And MQL5 (Part 3): Triangular Exchange Rates
Traders often face drawdowns from false signals, while waiting for confirmation can lead to missed opportunities. This article introduces a triangular trading strategy using Silver’s pricing in Dollars (XAGUSD) and Euros (XAGEUR), along with the EURUSD exchange rate, to filter out noise. By leveraging cross-market relationships, traders can uncover hidden sentiment and refine their entries in real time.
DoEasy. Controls (Part 30): Animating the ScrollBar control
In this article, I will continue the development of the ScrollBar control and start implementing the mouse interaction functionality. In addition, I will expand the lists of mouse state flags and events.
Developing a Replay System (Part 78): New Chart Trade (V)
In this article, we will look at how to implement part of the receiver code. Here we will implement an Expert Advisor to test and learn how the protocol interaction works. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Automating Trading Strategies in MQL5 (Part 32): Creating a Price Action 5 Drives Harmonic Pattern System
In this article, we develop a 5 Drives pattern system in MQL5 that identifies bullish and bearish 5 Drives harmonic patterns using pivot points and Fibonacci ratios, executing trades with customizable entry, stop loss, and take-profit levels based on user-selected options. We enhance trader insight with visual feedback through chart objects like triangles, trendlines, and labels to clearly display the A-B-C-D-E-F pattern structure.
Interview with Francisco García García (ATC 2012)
Today we interview Francisco García García (chuliweb) from Spain. A week ago his Expert Advisor reached the 8th place, but the unfortunate logic error in programming threw it from the first page of the Championship leaders. As confirmed by statistics, such an error is not uncommon for many participants.
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.
Neural Networks in Trading: Hierarchical Vector Transformer (HiVT)
We invite you to get acquainted with the Hierarchical Vector Transformer (HiVT) method, which was developed for fast and accurate forecasting of multimodal time series.
Data Science and Machine Learning (Part 17): Money in the Trees? The Art and Science of Random Forests in Forex Trading
Discover the secrets of algorithmic alchemy as we guide you through the blend of artistry and precision in decoding financial landscapes. Unearth how Random Forests transform data into predictive prowess, offering a unique perspective on navigating the complex terrain of stock markets. Join us on this journey into the heart of financial wizardry, where we demystify the role of Random Forests in shaping market destiny and unlocking the doors to lucrative opportunities
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.