Add a new article
Pipelines in MQL5
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.
Building MQL5-Like Trade Classes in Python for MetaTrader 5
Building MQL5-Like Trade Classes in Python for MetaTrader 5
MetaTrader 5 python package provides an easy way to build trading applications for the MetaTrader 5 platform in the Python language, while being a powerful and useful tool, this module isn't as easy as MQL5 programming language when it comes to making an algorithmic trading solution. In this article, we are going to build trade classes similar to the one offered in MQL5 to create a similar syntax and make it easier to make trading robots in Python as in MQL5.
Developing a multi-currency Expert Advisor (Part 21): Preparing for an important experiment and optimizing the code
Developing a multi-currency Expert Advisor (Part 21): Preparing for an important experiment and optimizing the code
For further progress it would be good to see if we can improve the results by periodically re-running the automatic optimization and generating a new EA. The stumbling block in many debates about the use of parameter optimization is the question of how long the obtained parameters can be used for trading in the future period while maintaining the profitability and drawdown at the specified levels. And is it even possible to do this?
Automating Trading Strategies in MQL5 (Part 32): Creating a Price Action 5 Drives Harmonic Pattern System
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.
Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (StockFormer)
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.
MQL5 Wizard Techniques you should know (Part 67): Using Patterns of TRIX and the Williams Percent Range
MQL5 Wizard Techniques you should know (Part 67): Using Patterns of TRIX and the Williams Percent Range
The Triple Exponential Moving Average Oscillator (TRIX) and the Williams Percentage Range Oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered recently, is also complementary given that TRIX defines the trend while Williams Percent Range affirms support and Resistance levels. As always, we use the MQL5 wizard to prototype any potential these two may have.
Neural Networks in Trading: An Ensemble of Agents with Attention Mechanisms (MASAAT)
Neural Networks in Trading: An Ensemble of Agents with Attention Mechanisms (MASAAT)
We introduce the Multi-Agent Self-Adaptive Portfolio Optimization Framework (MASAAT), which combines attention mechanisms and time series analysis. MASAAT generates a set of agents that analyze price series and directional changes, enabling the identification of significant fluctuations in asset prices at different levels of detail.
MQL5 Wizard Techniques you should know (Part 63): Using Patterns of DeMarker and Envelope Channels
MQL5 Wizard Techniques you should know (Part 63): Using Patterns of DeMarker and Envelope Channels
The DeMarker Oscillator and the Envelope indicator are momentum and support/resistance tools that can be paired when developing an Expert Advisor. We therefore examine on a pattern by pattern basis what could be of use and what potentially avoid. We are using, as always, a wizard assembled Expert Advisor together with the Patterns-Usage functions that are built into the Expert Signal Class.
MQL5 Wizard Techniques you should know (Part 62): Using Patterns of ADX and CCI with Reinforcement-Learning TRPO
MQL5 Wizard Techniques you should know (Part 62): Using Patterns of ADX and CCI with Reinforcement-Learning TRPO
The ADX Oscillator and CCI oscillator are trend following and momentum indicators that can be paired when developing an Expert Advisor. We continue where we left off in the last article by examining how in-use training, and updating of our developed model, can be made thanks to reinforcement-learning. We are using an algorithm we are yet to cover in these series, known as Trusted Region Policy Optimization. And, as always, Expert Advisor assembly by the MQL5 Wizard allows us to set up our model(s) for testing much quicker and also in a way where it can be distributed and tested with different signal types.
Trend criteria in trading
Trend criteria in trading
Trends are an important part of many trading strategies. In this article, we will look at some of the tools used to identify trends and their characteristics. Understanding and correctly interpreting trends can significantly improve trading efficiency and minimize risks.
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.
Introduction to MQL5 (Part 20): Introduction to Harmonic Patterns
Introduction to MQL5 (Part 20): Introduction to Harmonic Patterns
In this article, we explore the fundamentals of harmonic patterns, their structures, and how they are applied in trading. You’ll learn about Fibonacci retracements, extensions, and how to implement harmonic pattern detection in MQL5, setting the foundation for building advanced trading tools and Expert Advisors.
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.
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.
Neural Networks in Trading: Enhancing Transformer Efficiency by Reducing Sharpness (Final Part)
Neural Networks in Trading: Enhancing Transformer Efficiency by Reducing Sharpness (Final Part)
SAMformer offers a solution to the key drawbacks of Transformer models in long-term time series forecasting, such as training complexity and poor generalization on small datasets. Its shallow architecture and sharpness-aware optimization help avoid suboptimal local minima. In this article, we will continue to implement approaches using MQL5 and evaluate their practical value.
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.
Neural Networks in Trading: Node-Adaptive Graph Representation with NAFS
Neural Networks in Trading: Node-Adaptive Graph Representation with NAFS
We invite you to get acquainted with the NAFS (Node-Adaptive Feature Smoothing) method, which is a non-parametric approach to creating node representations that does not require parameter training. NAFS extracts features of each node given its neighbors and then adaptively combines these features to form a final representation.
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.
MQL5 Wizard Techniques you should know (Part 77): Using Gator Oscillator and the Accumulation/Distribution Oscillator
MQL5 Wizard Techniques you should know (Part 77): Using Gator Oscillator and the Accumulation/Distribution Oscillator
The Gator Oscillator by Bill Williams and the Accumulation/Distribution Oscillator are another indicator pairing that could be used harmoniously within an MQL5 Expert Advisor. We use the Gator Oscillator for its ability to affirm trends, while the A/D is used to provide confirmation of the trends via checks on volume. In exploring this indicator pairing, as always, we use the MQL5 wizard to build and test out their potential.
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.
Price Action Analysis Toolkit Development (Part 33): Candle Range Theory Tool
Price Action Analysis Toolkit Development (Part 33): Candle Range Theory Tool
Upgrade your market reading with the Candle-Range Theory suite for MetaTrader 5, a fully MQL5-native solution that converts raw price bars into real-time volatility intelligence. The lightweight CRangePattern library benchmarks each candle’s true range against an adaptive ATR and classifies it the instant it closes; the CRT Indicator then projects those classifications on your chart as crisp, color-coded rectangles and arrows that reveal tightening consolidations, explosive breakouts, and full-range engulfment the moment they occur.