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.
We invite you to explore FinAgent, a multimodal financial trading agent framework designed to analyze various types of data reflecting market dynamics and historical trading patterns.
This article introduces a fully automated MQL5 system designed to identify and trade market swings with precision. Unlike traditional fixed-bar swing indicators, this system adapts dynamically to evolving price structure—detecting swing highs and swing lows in real time to capture directional opportunities as they form.
Today, we take an important step toward helping every developer understand how to read class structures and quickly build Expert Advisors using the MQL5 Standard Library. The library is rich and expandable, yet it can feel like being handed a complex toolkit without a manual. Here we share and discuss an alternative integration routine—a concise, repeatable workflow that shows how to connect classes reliably in real projects.
This piece follows up ‘Part-84’, where we introduced the pairing of Stochastic and the Fractal Adaptive Moving Average. We now shift focus to Inference Learning, where we look to see if laggard patterns in the last article could have their fortunes turned around. The Stochastic and FrAMA are a momentum-trend complimentary pairing. For our inference learning, we are revisiting the Beta algorithm of a Variational Auto Encoder. We also, as always, do the implementation of a custom signal class designed for integration with the MQL5 Wizard.
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.
We continue our work on creating the FinMem framework, which uses layered memory approaches that mimic human cognitive processes. This allows the model not only to effectively process complex financial data but also to adapt to new signals, significantly improving the accuracy and effectiveness of investment decisions in dynamically changing markets.
The Stochastic Oscillator and the Fractal Adaptive Moving Average are an indicator pairing that could be used for their ability to compliment each other within an MQL5 Expert Advisor. We introduced this pairing in the last article, and now look to wrap up by considering its 5 last signal patterns. In exploring this, as always, we use the MQL5 wizard to build and test out their potential.
Trading strategies may be challenging to improve because we often don’t fully understand what the strategy is doing wrong. In this discussion, we introduce linear system identification, a branch of control theory. Linear feedback systems can learn from data to identify a system’s errors and guide its behavior toward intended outcomes. While these methods may not provide fully interpretable explanations, they are far more valuable than having no control system at all. Let’s explore linear system identification and observe how it may help us as algorithmic traders to maintain control over our trading applications.
In this article, we'll cover the basics of risk management in trading and learn how to create your first functions for calculating the appropriate lot size for a trade, as well as a stop-loss. Additionally, we will go into detail about how these features work, explaining each step. Our goal is to provide a clear understanding of how to apply these concepts in automated trading. Finally, we will put everything into practice by creating a simple script with an include file.
The Stochastic Oscillator and the Fractal Adaptive Moving Average are another indicator pairing that could be used for their ability to compliment each other within an MQL5 Expert Advisor. We look at the Stochastic for its ability to pinpoint momentum shifts, while the FrAMA is used to provide confirmation of the prevailing trends. In exploring this indicator pairing, as always, we use the MQL5 wizard to build and test out their potential.
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.
In this discussion, we introduce a Higher-to-Lower Timeframe Synchronizer tool designed to solve the problem of analyzing market patterns that span across higher timeframe periods. The built-in period markers in MetaTrader 5 are often limited, rigid, and not easily customizable for non-standard timeframes. Our solution leverages the MQL5 language to develop an indicator that provides a dynamic and visual way to align higher timeframe structures within lower timeframe charts. This tool can be highly valuable for detailed market analysis. To learn more about its features and implementation, I invite you to join the discussion.
In this article, we explore how previously invalidated orderblocks can be reused as mitigation blocks within Smart Money Concepts (SMC). These zones reveal where institutional traders re-enter the market after a failed orderblock, providing high-probability areas for trade continuation in the dominant trend.
In this article, we enhance the ChatGPT-integrated program in MQL5 overcoming multiline input limitations with improved text rendering, introducing a sidebar for navigating persistent chat storage using AES256 encryption and ZIP compression, and generating initial trade signals through chart data integration.
In the previous article, we explored the theoretical foundations and began implementing the approaches of the Multitask-Stockformer framework, which combines the wavelet transform and the Self-Attention multitask model. We continue to implement the algorithms of this framework and evaluate their effectiveness on real historical data.
In the last article, we examined the pairing of Ichimoku and the ADX under an Inference Learning framework. For this piece we revisit, Reinforcement Learning when used with an indicator pairing we considered last in ‘Part 68’. The TRIX and Williams Percent Range. Our algorithm for this review will be the Quantile Regression DQN. As usual, we present this as a custom signal class designed for implementation with the MQL5 Wizard.
We invite you to explore a framework that combines wavelet transforms and a multi-task self-attention model, aimed at improving the responsiveness and accuracy of forecasting in volatile market conditions. The wavelet transform allows asset returns to be decomposed into high and low frequencies, carefully capturing long-term market trends and short-term fluctuations.
In this article, we explore a data-driven approach to discovering and validating non-standard Fibonacci retracement levels that markets may respect. We present a complete workflow tailored for implementation in MQL5, beginning with data collection and bar or swing detection, and extending through clustering, statistical hypothesis testing, backtesting, and integration into an MetaTrader 5 Fibonacci tool. The goal is to create a reproducible pipeline that transforms anecdotal observations into statistically defensible trading signals.
This piece follows up ‘Part-80’, where we examined the pairing of Ichimoku and the ADX under a Reinforcement Learning framework. We now shift focus to Inference Learning. Ichimoku and ADX are complimentary as already covered, however we are going to revisit the conclusions of the last article related to pipeline use. For our inference learning, we are using the Beta algorithm of a Variational Auto Encoder. We also stick with the implementation of a custom signal class designed for integration with the MQL5 Wizard.
We continue our examination of the StockFormer hybrid trading system, which combines predictive coding and reinforcement learning algorithms for financial time series analysis. The system is based on three Transformer branches with a Diversified Multi-Head Attention (DMH-Attn) mechanism that enables the capturing of complex patterns and interdependencies between assets. Previously, we got acquainted with the theoretical aspects of the framework and implemented the DMH-Attn mechanisms. Today, we will talk about the model architecture and training.
In this article, we create a supply and demand trading system in MQL5 that identifies supply and demand zones through consolidation ranges, validates them with impulsive moves, and trades retests with trend confirmation and customizable risk parameters. The system visualizes zones with dynamic labels and colors, supporting trailing stops for risk management.
In this article, we upgrade the ChatGPT-integrated program in MQL5 to a scrollable single chat-oriented UI, enhancing conversation history display with timestamps and dynamic scrolling. The system builds on JSON parsing to manage multi-turn messages, supporting customizable scrollbar modes and hover effects for improved user interaction.
This article explains how to develop a professional Heikin Ashi-based Expert Advisor (EA) in MQL5. You will learn how to set up input parameters, enumerations, indicators, global variables, and implement the core trading logic. You will also be able to run a backtest on gold to validate your work.
In this article, we create a Breaker Block Trading System in MQL5 that identifies consolidation ranges, detects breakouts, and validates breaker blocks with swing points to trade retests with defined risk parameters. The system visualizes order and breaker blocks with dynamic labels and arrows, supporting automated trading and trailing stops.
In this article, we develop an MQL5 First Run User Setup Wizard for Expert Advisors, featuring a scrollable guide with an interactive dashboard, dynamic text formatting, and visual controls like buttons and a checkbox allowing users to navigate instructions and configure trading parameters efficiently. Users of the program get to have insight of what the program is all about and what to do on the first run, more like an orientation model.
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.
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.
This article explains how to design and optimise a trading system using the Detrended Price Oscillator (DPO) in MQL5. It outlines the indicator's core logic, demonstrating how it identifies short-term cycles by filtering out long-term trends. Through a series of step-by-step examples and simple strategies, readers will learn how to code it, define entry and exit signals, and conduct backtesting. Finally, the article presents practical optimization methods to enhance performance and adapt the system to changing market conditions.
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
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.
In this article, we develop a ChatGPT-integrated program in MQL5 with a user interface, leveraging the JSON parsing framework from Part 1 to send prompts to OpenAI’s API and display responses on a MetaTrader 5 chart. We implement a dashboard with an input field, submit button, and response display, handling API communication and text wrapping for user interaction.
This article introduces the ParaFrac Oscillator and its V2 model as trading tools. It outlines three trading strategies developed using these indicators. Each strategy was tested and optimized to identify their strengths and weaknesses. Comparative analysis highlighted the performance differences between the original and V2 models.
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.
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.
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.
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.
In financial markets, the laws of retracement remain among the most undeniable forces. It is a rule of thumb that price will always retrace—whether in large moves or even within the smallest tick patterns, which often appear as a zigzag. However, the retracement pattern itself is never fixed; it remains uncertain and subject to anticipation. This uncertainty explains why traders rely on multiple Fibonacci levels, each carrying a certain probability of influence. In this discussion, we introduce a refined strategy that applies Fibonacci techniques to address the challenges of trading shortly after major economic event announcements. By combining retracement principles with event-driven market behavior, we aim to uncover more reliable entry and exit opportunities. Join to explore the full discussion and see how Fibonacci can be adapted to post-event trading.
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.
