If we are going to automate periodic optimization, we need to think about auto updates of the settings of the EAs already running on the trading account. This should also allow us to run the EA in the strategy tester and change its settings within a single run.
This part explores how to design a Dynamic Multi-Pair Expert Advisor capable of adapting between Scalping and Swing Trading modes. It covers the structural and algorithmic differences in signal generation, trade execution, and risk management, allowing the EA to intelligently switch strategies based on market behavior and user input.
This article helps new community members search for and discover their own candlestick patterns. Describing these patterns can be daunting, as it requires manually searching and creatively identifying improvements. Here, we introduce the engulfing candlestick pattern and show how it can be enhanced for more profitable trading applications.
Factorization is a mathematical process used to gain insights into the attributes of data. When we apply factorization to large sets of market data — organized in rows and columns — we can uncover patterns and characteristics of the market. Factorization is a powerful tool, and this article will show how you can use it within the MetaTrader 5 terminal, through the MQL5 API, to gain more profound insights into your market data.
In this article, we develop an MQL5 Expert Advisor for statistical mean reversion trading, calculating moments like mean, variance, skewness, kurtosis, and Jarque-Bera statistics over a specified period to identify non-normal distributions and generate buy/sell signals based on confidence intervals with adaptive thresholds
In this article, we develop an MQL5 strategy tracker system that detects moving average crossover signals filtered by a long-term MA, simulates or executes trades with configurable TP levels and SL in points, and monitors outcomes like TP/SL hits for performance analysis.
In Part 5 of our MQL5 AI trading system series, we enhance the ChatGPT-integrated Expert Advisor by introducing a collapsible sidebar, improving navigation with small and large history popups for seamless chat selection, while maintaining multiline input handling, persistent encrypted chat storage, and AI-driven trade signal generation from chart data.
Functions are critical things in any programming language, it helps developers apply the concept of (DRY) which means do not repeat yourself, and many other benefits. In this article, you will find much more information about functions and how we can create our own functions in MQL5 with simple applications that can be used or called in any system you have to enrich your trading system without complicating things.
We invite you to get acquainted with the Hierarchical Double-Tower Transformer (Hidformer) framework, which was developed for time series forecasting and data analysis. The framework authors proposed several improvements to the Transformer architecture, which resulted in increased forecast accuracy and reduced computational resource consumption.
Fibonacci levels provide a practical framework that markets often respect, highlighting price zones where reactions are more likely. In this article, we build an expert advisor that applies Fibonacci retracement logic to anticipate likely future moves and trade retracements with pending orders. Explore the full workflow—from swing detection to level plotting, risk controls, and execution.
Analytical Volume Profile Trading (AVPT) explores how liquidity architecture and market memory shape price behavior, enabling more profound insight into institutional positioning and volume-driven structure. By mapping POC, HVNs, LVNs, and Value Areas, traders can identify acceptance, rejection, and imbalance zones with precision.
In Part 6 of our MQL5 AI trading system series, we advance the ChatGPT-integrated Expert Advisor by introducing chat deletion functionality through interactive delete buttons in the sidebar, small/large history popups, and a new search popup, allowing traders to manage and organize persistent conversations efficiently while maintaining encrypted storage and AI-driven signals from chart data.
This article teaches you how to retrieve and extract price data from external platforms using APIs and the WebRequest function in MQL5. You’ll learn how URLs are structured, how API responses are formatted, how to convert server data into readable strings, and how to identify and extract specific values from JSON responses.
In this article, we develop a Candle Range Theory (CRT) trading system in MQL5 that identifies accumulation ranges on a specified timeframe, detects breaches with manipulation depth filtering, and confirms reversals for entry trades in the distribution phase. The system supports dynamic or static stop-loss and take-profit calculations based on risk-reward ratios, optional trailing stops, and limits on positions per direction for controlled risk management.
The relentless quest to decode market rhythms has led traders and quantitative analysts to develop countless mathematical models. This article has introduced the Flower Volatility Index (FVI), a novel approach that transforms the mathematical elegance of Rose Curves into a functional trading tool. Through this work, we have shown how mathematical models can be adapted into practical trading mechanisms capable of supporting both analysis and decision-making in real market conditions.
In this article, we look to explore how a complimentary indicator pairing can be used to analyze the recent 5-year history of Vanguard Information Technology Index Fund ETF. By considering two options of algorithms, Kendall’s Tau and Distance-Correlation, we look to select not just an ideal indicator pair for trading the VGT, but also suitable signal-pattern pairings of these two indicators.
Learn how to build a complete Kagi Chart engine in MQL5—constructing price reversals, generating dynamic line segments, and updating Kagi structures in real time. This first part teaches you how to render Kagi charts directly on MetaTrader 5, giving traders a clear view of trend shifts and market strength while preparing for automated Kagi-based trading logic in Part 2.
This article shows how to configure a black-box model to automatically uncover strong trading strategies using a data-driven approach. By using Mutual Information to prioritize the most learnable signals, we can build smarter and more adaptive models that outperform conventional methods. Readers will also learn to avoid common pitfalls like overreliance on surface-level metrics, and instead develop strategies rooted in meaningful statistical insight.
We aim to create a system for automatic periodic optimization of trading strategies used in one final EA. As the system evolves, it becomes increasingly complex, so it is necessary to look at it as a whole from time to time in order to identify bottlenecks and suboptimal solutions.
Today, we use the MQL5 Standard Library to build custom signal classes and let the MQL5 Wizard assemble a professional Expert Advisor for us. This approach simplifies development so that even beginner programmers can create robust EAs without in-depth coding knowledge, focusing instead on tuning inputs and optimizing performance. Join this discussion as we explore the process step by step.
In this article, we create a fully customizable session-based Opening Range Breakout (ORB) system in MQL5 that lets us set any desired session start time and range duration, automatically calculates the high and low of that opening period, and trades only confirmed breakouts in the direction of the move.
In this article, we continue mastering API and WebRequest in MQL5 by retrieving candlestick data from an external source. We focus on splitting the server response, cleaning the data, and extracting essential elements such as opening time and OHLC values for multiple daily candles, preparing the data for further analysis.
This article introduces the Pattern Density Heatmap, a price‑action mapping tool that transforms repeated candlestick pattern detections into statistically significant support and resistance zones. Rather than treating each signal in isolation, the EA aggregates detections into fixed price bins, scores their density with optional recency weighting, and confirms levels against higher‑timeframe data. The resulting heatmap reveals where the market has historically reacted—levels that can be used proactively for trade timing, risk management, and strategy confidence across any trading style.
In this session, we will build a sophisticated, multi-signal Expert Advisor using the MQL5 Standard Library. This approach allows us to seamlessly blend built-in signals with our own custom logic, demonstrating how to construct a powerful and flexible trading algorithm. For more, click to read further.
In this article, we implement an adaptive Linear Regression Channel system in MQL5 that automatically calculates the regression line and standard deviation channel over a user-defined period, only activates when the slope exceeds a minimum threshold to confirm a clear trend, and dynamically recreates or extends the channel when the price breaks out by a configurable percentage of channel width.
In this article, we develop a Change of Character (CHoCH) detection system in MQL5 that identifies swing highs and lows over a user-defined bar length, labels them as HH/LH for highs or LL/HL for lows to determine trend direction, and triggers trades on breaks of these swing points, indicating a potential reversal, and trades the breaks when the structure changes.
In the world of technical analysis, price often takes center stage. Traders meticulously map out support, resistance, and patterns, yet frequently ignore the critical force that drives these movements: volume. This article delves into a novel approach to volume analysis: the Volume Boundary indicator. This transformation, utilizing sophisticated smoothing functions like the butterfly and triple sine curves, allows for clearer interpretation and the development of systematic trading strategies.
Integrating Python's logging module with MQL5 empowers traders with a systematic logging approach, simplifying the process of monitoring, debugging, and documenting trading activities. This article explains the adaptation process, offering traders a powerful tool for maintaining clarity and organization in trading software development.
This article revisits the classic moving average crossover strategy and examines why it often fails in noisy, fast-moving markets. It presents five alternative filtering methods designed to strengthen signal quality and remove weak or unprofitable trades. The discussion highlights how statistical models can learn and correct the errors that human intuition and traditional rules miss. Readers leave with a clearer understanding of how to modernize an outdated strategy and of the pitfalls of relying solely on metrics like RMSE in financial modeling.
In this article, we create an Inverse Fair Value Gap (IFVG) detection system in MQL5 that identifies bullish/bearish FVGs on recent bars with minimum gap size filtering, tracks their states as normal/mitigated/inverted based on price interactions (mitigation on far-side breaks, retracement on re-entry, inversion on close beyond far side from inside), and ignores overlaps while limiting tracked FVGs.
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.
Learn how to build a complete Kagi-based trading Expert Advisor in MQL5, from signal construction to order execution, visual markers, and a three-stage trailing stop. Includes full code, testing results, and a downloadable set file.
In this article, we will look at how to connect a new strategy to the auto optimization system we have created. Let's see what kind of EAs we need to create and whether it will be possible to do without changing the EA library files or minimize the necessary changes.
This topic explores how to build an Adaptive Smart Money Architecture (ASMA)—an intelligent Expert Advisor that merges Smart Money Concepts (Order Blocks, Break of Structure, Fair Value Gaps) with real-time market sentiment to automatically choose the best trading strategy depending on current market conditions.
We continue our look at how MetaTrader can be used outside its forex trading ‘comfort-zone’ by looking at another tradable asset in the form of the FXI ETF. Unlike in the last article where we tried to do ‘too-much’ by delving into not just indicator selection, but also considering indicator pattern combinations, for this article we will swim slightly upstream by focusing more on indicator selection. Our end product for this is intended as a form of pipeline that can help recommend indicators for various assets, provided we have a reasonable amount of their price history.
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 build a Liquidity Sweep on Break of Structure (BoS) system in MQL5 that detects swing highs/lows over a user-defined length, labels them as HH/HL/LH/LL to identify BOS (HH in uptrend or LL in downtrend), and spots liquidity sweeps when price wicks beyond the swing but closes back inside on a bullish/bearish candle.
In this article, we will explore practical techniques for trading the Relative Strength Index (RSI) oscillator with market structure. Our focus will be on channel price action patterns, how they are typically traded, and how MQL5 can be leveraged to enhance this process. By the end, you will have a rule-based, automated channel-trading system designed to capture trend continuation opportunities with greater precision and consistency.
In this article, we enhance the AI-powered trading system's modularity by separating UI components into a dedicated include file. The system now automates trade execution based on AI-generated signals, parsing JSON responses for BUY/SELL/NONE with entry/SL/TP, visualizing patterns like engulfing or divergences on charts with arrows, lines, and labels, and optional auto-signal checks on new bars.
We continue our look at how the selection of indicators can be pipelined when facing a ‘none-typical’ MetaTrader asset. MetaTrader 5 is primarily used to trade forex, and that is good given the liquidity on offer, however the case for trading outside of this ‘comfort-zone’, is growing bolder with not just the overnight rise of platforms like Robinhood, but also the relentless pursuit of an edge for most traders. We consider the XLF ETF for this article and also cap our revamped pipeline with a simple MLP.
