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From Novice to Expert: Animated News Headline Using MQL5 (II)
From Novice to Expert: Animated News Headline Using MQL5 (II)
Today, we take another step forward by integrating an external news API as the source of headlines for our News Headline EA. In this phase, we’ll explore various news sources—both established and emerging—and learn how to access their APIs effectively. We'll also cover methods for parsing the retrieved data into a format optimized for display within our Expert Advisor. Join the discussion as we explore the benefits of accessing news headlines and the economic calendar directly on the chart, all within a compact, non-intrusive interface.
Mastering Log Records (Part 8): Error Records That Translate Themselves
Mastering Log Records (Part 8): Error Records That Translate Themselves
In this eighth installment of the Mastering Log Records series, we explore the implementation of multilingual error messages in Logify, a powerful logging library for MQL5. You’ll learn how to structure errors with context, translate messages into multiple languages, and dynamically format logs by severity level. All of this with a clean, extensible, and production-ready design.
MQL5 Wizard Techniques you should know (Part 70): Using Patterns of SAR and the RVI with a Exponential Kernel Network
MQL5 Wizard Techniques you should know (Part 70): Using Patterns of SAR and the RVI with a Exponential Kernel Network
We follow up our last article, where we introduced the indicator pair of the SAR and the RVI, by considering how this indicator pairing could be extended with Machine Learning. SAR and RVI are a trend and momentum complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Automating Trading Strategies in MQL5 (Part 34): Trendline Breakout System with R-Squared Goodness of Fit
Automating Trading Strategies in MQL5 (Part 34): Trendline Breakout System with R-Squared Goodness of Fit
In this article, we develop a Trendline Breakout System in MQL5 that identifies support and resistance trendlines using swing points, validated by R-squared goodness of fit and angle constraints, to automate breakout trades. Our plan is to detect swing highs and lows within a specified lookback period, construct trendlines with a minimum number of touch points, and validate them using R-squared metrics and angle constraints to ensure reliability.
MQL5 Wizard Techniques you should know (Part 80): Using Patterns of Ichimoku and the ADX-Wilder with TD3 Reinforcement Learning
MQL5 Wizard Techniques you should know (Part 80): Using Patterns of Ichimoku and the ADX-Wilder with TD3 Reinforcement Learning
This article follows up ‘Part-74’, where we examined the pairing of Ichimoku and the ADX under a Supervised Learning framework, by moving our focus to Reinforcement Learning. Ichimoku and ADX form a complementary combination of support/resistance mapping and trend strength spotting. In this installment, we indulge in how the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm can be used with this indicator set. As with earlier parts of the series, the implementation is carried out in a custom signal class designed for integration with the MQL5 Wizard, which facilitates seamless Expert Advisor assembly.
Automating Trading Strategies in MQL5 (Part 33): Creating a Price Action Shark Harmonic Pattern System
Automating Trading Strategies in MQL5 (Part 33): Creating a Price Action Shark Harmonic Pattern System
In this article, we develop a Shark pattern system in MQL5 that identifies bullish and bearish Shark 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 X-A-B-C-D pattern structure
The MQL5 Standard Library Explorer (Part 1): Introduction with CTrade, CiMA, and CiATR
The MQL5 Standard Library Explorer (Part 1): Introduction with CTrade, CiMA, and CiATR
The MQL5 Standard Library plays a vital role in developing trading algorithms for MetaTrader 5. In this discussion series, our goal is to master its application to simplify the creation of efficient trading tools for MetaTrader 5. These tools include custom Expert Advisors, indicators, and other utilities. We begin today by developing a trend-following Expert Advisor using the CTrade, CiMA, and CiATR classes. This is an especially important topic for everyone—whether you are a beginner or an experienced developer. Join this discussion to discover more.
Creating a Trading Administrator Panel in MQL5 (Part XII): Integration of a Forex Values Calculator
Creating a Trading Administrator Panel in MQL5 (Part XII): Integration of a Forex Values Calculator
Accurate calculation of key trading values is an indispensable part of every trader’s workflow. In this article, we will discuss, the integration of a powerful utility—the Forex Calculator—into the Trade Management Panel, further extending the functionality of our multi-panel Trading Administrator system. Efficiently determining risk, position size, and potential profit is essential when placing trades, and this new feature is designed to make that process faster and more intuitive within the panel. Join us as we explore the practical application of MQL5 in building advanced, trading panels.
Automating Trading Strategies in MQL5 (Part 2): The Kumo Breakout System with Ichimoku and Awesome Oscillator
Automating Trading Strategies in MQL5 (Part 2): The Kumo Breakout System with Ichimoku and Awesome Oscillator
In this article, we create an Expert Advisor (EA) that automates the Kumo Breakout strategy using the Ichimoku Kinko Hyo indicator and the Awesome Oscillator. We walk through the process of initializing indicator handles, detecting breakout conditions, and coding automated trade entries and exits. Additionally, we implement trailing stops and position management logic to enhance the EA's performance and adaptability to market conditions.
Overcoming The Limitation of Machine Learning (Part 4): Overcoming Irreducible Error Using Multiple Forecast Horizons
Overcoming The Limitation of Machine Learning (Part 4): Overcoming Irreducible Error Using Multiple Forecast Horizons
Machine learning is often viewed through statistical or linear algebraic lenses, but this article emphasizes a geometric perspective of model predictions. It demonstrates that models do not truly approximate the target but rather map it onto a new coordinate system, creating an inherent misalignment that results in irreducible error. The article proposes that multi-step predictions, comparing the model’s forecasts across different horizons, offer a more effective approach than direct comparisons with the target. By applying this method to a trading model, the article demonstrates significant improvements in profitability and accuracy without changing the underlying model.
Automating The Market Sentiment Indicator
Automating The Market Sentiment Indicator
In this article, we automate a custom market sentiment indicator that classifies market conditions into bullish, bearish, risk-on, risk-off, and neutral. The Expert Advisor delivers real-time insights into prevailing sentiment while streamlining the analysis process for current market trends or direction.
Price Action Analysis Toolkit Development (Part 41): Building a Statistical Price-Level EA in MQL5
Price Action Analysis Toolkit Development (Part 41): Building a Statistical Price-Level EA in MQL5
Statistics has always been at the heart of financial analysis. By definition, statistics is the discipline that collects, analyzes, interprets, and presents data in meaningful ways. Now imagine applying that same framework to candlesticks—compressing raw price action into measurable insights. How helpful would it be to know, for a specific period of time, the central tendency, spread, and distribution of market behavior? In this article, we introduce exactly that approach, showing how statistical methods can transform candlestick data into clear, actionable signals.
Developing a Volatility Based Breakout System
Developing a Volatility Based Breakout System
Volatility based breakout system identifies market ranges, then trades when price breaks above or below those levels, filtered by volatility measures such as ATR. This approach helps capture strong directional moves.
Functions for activating neurons during training: The key to fast convergence?
Functions for activating neurons during training: The key to fast convergence?
This article presents a study of the interaction of different activation functions with optimization algorithms in the context of neural network training. Particular attention is paid to the comparison of the classical ADAM and its population version when working with a wide range of activation functions, including the oscillating ACON and Snake functions. Using a minimalistic MLP (1-1-1) architecture and a single training example, the influence of activation functions on the optimization is isolated from other factors. The article proposes an approach to manage network weights through the boundaries of activation functions and a weight reflection mechanism, which allows avoiding problems with saturation and stagnation in training.
Building AI-Powered Trading Systems in MQL5 (Part 1): Implementing JSON Handling for AI APIs
Building AI-Powered Trading Systems in MQL5 (Part 1): Implementing JSON Handling for AI APIs
In this article, we develop a JSON parsing framework in MQL5 to handle data exchange for AI API integration, focusing on a JSON class for processing JSON structures. We implement methods to serialize and deserialize JSON data, supporting various data types like strings, numbers, and objects, essential for communicating with AI services like ChatGPT, enabling future AI-driven trading systems by ensuring accurate data handling and manipulation.
The Parafrac V2 Oscillator: Integrating Parabolic SAR with Average True Range
The Parafrac V2 Oscillator: Integrating Parabolic SAR with Average True Range
The Parafrac V2 Oscillator is an advanced technical analysis tool that integrates the Parabolic SAR with the Average True Range (ATR) to overcome limitations of its predecessor, which relied on fractals and was prone to signal spikes overshadowing previous and current signals. By leveraging ATR’s volatility measure, the version 2 offers a smoother, more reliable method for detecting trends, reversals, and divergences, helping traders reduce chart congestion and analysis paralysis.
Quantum computing and trading: A fresh approach to price forecasts
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
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.
Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know
Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know
ARIMA, short for Auto Regressive Integrated Moving Average, is a powerful traditional time series forecasting model. With the ability to detect spikes and fluctuations in a time series data, this model can make accurate predictions on the next values. In this article, we are going to understand what is it, how it operates, what you can do with it when it comes to predicting the next prices in the market with high accuracy and much more.
Price Action Analysis Toolkit Development (Part 24): Price Action Quantification Analysis Tool
Price Action Analysis Toolkit Development (Part 24): Price Action Quantification Analysis Tool
Candlestick patterns offer valuable insights into potential market moves. Some single candles signal continuation of the current trend, while others foreshadow reversals, depending on their position within the price action. This article introduces an EA that automatically identifies four key candlestick formations. Explore the following sections to learn how this tool can enhance your price-action analysis.
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.
Price Action Analysis Toolkit Development (Part 40): Market DNA Passport
Price Action Analysis Toolkit Development (Part 40): Market DNA Passport
This article explores the unique identity of each currency pair through the lens of its historical price action. Inspired by the concept of genetic DNA, which encodes the distinct blueprint of every living being, we apply a similar framework to the markets, treating price action as the “DNA” of each pair. By breaking down structural behaviors such as volatility, swings, retracements, spikes, and session characteristics, the tool reveals the underlying profile that distinguishes one pair from another. This approach provides more profound insight into market behavior and equips traders with a structured way to align strategies with the natural tendencies of each instrument.
Dynamic mode decomposition applied to univariate time series in MQL5
Dynamic mode decomposition applied to univariate time series in MQL5
Dynamic mode decomposition (DMD) is a technique usually applied to high-dimensional datasets. In this article, we demonstrate the application of DMD on univariate time series, showing its ability to characterize a series as well as make forecasts. In doing so, we will investigate MQL5's built-in implementation of dynamic mode decomposition, paying particular attention to the new matrix method, DynamicModeDecomposition().
Developing a Custom Market Sentiment Indicator
Developing a Custom Market Sentiment Indicator
In this article we are developing a custom market sentiment indicator to classify conditions into bullish, bearish, risk-on, risk-off, or neutral. Using multi-timeframe, the indicator can provide traders with a clearer perspective of overall market bias and short-term confirmations.
Statistical Arbitrage Through Cointegrated Stocks (Part 4): Real-time Model Updating
Statistical Arbitrage Through Cointegrated Stocks (Part 4): Real-time Model Updating
This article describes a simple but comprehensive statistical arbitrage pipeline for trading a basket of cointegrated stocks. It includes a fully functional Python script for data download and storage; correlation, cointegration, and stationarity tests, along with a sample Metatrader 5 Service implementation for database updating, and the respective Expert Advisor. Some design choices are documented here for reference and for helping in the experiment replication.