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.
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.
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.
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 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.
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.
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.
We continue our new series on Market-Positioning, where we study particular assets, with specific trade directions over manageable test windows. We started this by considering Nvidia Corp stock in the last article, where we covered 5 signal patterns from the complimentary pairing of the RSI and DeMarker oscillators. For this article, we cover the remaining 5 patterns and also delve into multi-pattern options that not only feature untethered combinations of all ten, but also specialized combinations of just a pair.
Generating new indicators from existing ones offers a powerful way to enhance trading analysis. By defining a mathematical function that integrates the outputs of existing indicators, traders can create hybrid indicators that consolidate multiple signals into a single, efficient tool. This article introduces a new indicator built from three oscillators using a modified version of the Pearson correlation function, which we call the Pseudo Pearson Correlation (PPC). The PPC indicator aims to quantify the dynamic relationship between oscillators and apply it within a practical trading strategy.
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.
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.
In this article, we build an MQL5 Expert Advisor for Fibonacci retracement trading, using either daily candle ranges or lookback arrays to calculate custom levels like 50% and 61.8% for entries, determining bullish or bearish setups based on close vs. open. The system triggers buys or sells on price crossings of levels with max trades per level, optional closure on new Fib calcs, points-based trailing stops after a min profit threshold, and SL/TP buffers as percentages of the range.
We commence a new article series that builds upon our earlier efforts laid out in the MQL5 Wizard series, by taking them further as we step up our approach to systematic trading and strategy testing. Within these new series, we’ll concentrate our focus on Expert Advisors that are coded to hold only a single type of position - primarily longs. Focusing on just one market trend can simplify analysis, lessen strategy complexity and expose some key insights, especially when dealing in assets beyond forex. Our series, therefore, will investigate if this is effective in equities and other non-forex assets, where long only systems usually correlate well with smart money or institution strategies.
Just because ticks are constantly flowing in doesn’t mean every moment is an opportunity to trade. Today, we take an in-depth study into the art of timing—focusing on developing a time isolation algorithm to help traders identify and trade within their most favorable market windows. Cultivating this discipline allows retail traders to synchronize more closely with institutional timing, where precision and patience often define success. Join this discussion as we explore the science of timing and selective trading through the analytical capabilities of MQL5.
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.
The MacroHFT framework for high-frequency cryptocurrency trading uses context-aware reinforcement learning and memory to adapt to dynamic market conditions. At the end of this article, we will test the implemented approaches on real historical data to assess their effectiveness.
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.
This article introduces the Triple Sine Mean Reversion Method, a trading strategy built upon a new mathematical indicator — the Triple Sine Oscillator (TSO). The TSO is derived from the sine cube function, which oscillates between –1 and +1, making it suitable for identifying overbought and oversold market conditions. Overall, the study demonstrates how mathematical functions can be transformed into practical trading tools.
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
This article considers the Rademacher and Walsh functions. We will explore ways to apply these functions to financial time series analysis and also consider various applications for them in trading.
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.
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.
Learn how to build a clean and professional on-chart control panel in MQL5 for a Risk-Based Trade Placement Expert Advisor. This step-by-step guide explains how to design a functional GUI that allows traders to input trade parameters, calculate lot size, and prepare for automated order placement.
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.
I invite you to explore the MacroHFT framework, which applies context-aware reinforcement learning and memory to improve high-frequency cryptocurrency trading decisions using macroeconomic data and adaptive agents.
In this article, we demonstrated how the fascinating mathematical concept of the Butterfly Curve can be transformed into a practical trading tool. We constructed the Butterfly Oscillator and built a foundational trading strategy around it. The strategy effectively combines the oscillator's unique cyclical signals with traditional trend confirmation from moving averages, creating a systematic approach for identifying potential market entries.
This is a high-timeframe-based EA that makes long-term analyses, trading decisions, and executions based on higher-timeframe analyses of W1, D1, and MN. This article will explore in detail an EA that is specifically designed for long-term traders who are patient enough to withstand and hold their positions during tumultuous lower time frame price action without changing their bias frequently until take-profit targets are hit.
In this article, we build an MQL5 EA that detects hidden RSI divergences via swing points with strength, bar ranges, tolerance, and slope angle filters for price and RSI lines. It executes buy/sell trades on validated signals with fixed lots, SL/TP in pips, and optional trailing stops for risk control.
In this discussion, we will develop an Expert Advisor using the CTrade and CChartObjectStdDevChannel classes, while applying several filters to enhance profitability. This stage puts our previous discussion into practical application. Additionally, I’ll introduce another simple approach to help you better understand the MQL5 Standard Library and its underlying codebase. Join the discussion to explore these concepts in action.
We continue to implement the approaches proposed by the authors of the FinCon framework. FinCon is a multi-agent system based on Large Language Models (LLMs). Today, we will implement the necessary modules and conduct comprehensive testing of the model on real historical data.
Linear system identifcation may be coupled to learn to correct the error in a supervised learning algorithm. This allows us to build applications that depend on statistical modelling techniques without necessarily inheriting the fragility of the model's restrictive assumptions. Classical supervised learning algorithms have many needs that may be supplemented by pairing these models with a feedback controller that can correct the model to keep up with current market conditions.
We invite you to explore the FinCon framework, which is a a Large Language Model (LLM)-based multi-agent system. The framework uses conceptual verbal reinforcement to improve decision making and risk management, enabling effective performance on a variety of financial tasks.
In this discussion, we take a step forward to uncover the underlying price action hidden within candlestick wicks. By integrating a wick visualization feature into the Market Periods Synchronizer, we enhance the tool with greater analytical depth and interactivity. This upgraded system allows traders to visualize higher-timeframe price rejections directly on lower-timeframe charts, revealing detailed structures that were once concealed within the shadows.
This article introduces how to use the WebRequest() function and APIs in MQL5 to communicate with external platforms. You’ll learn how to create a Telegram bot, obtain chat and group IDs, and send, edit, and delete messages directly from MT5, building a strong foundation for mastering API integration in your future MQL5 projects.
In this article, we build an MQL5 EA that detects regular RSI divergences using swing points with strength, bar limits, and tolerance checks. It executes trades on bullish or bearish signals with fixed lots, SL/TP in pips, and optional trailing stops. Visuals include colored lines on charts and labeled swings for better strategy insights.
Learn how to build a Smart Trade Manager Expert Advisor in MQL5 that automates trade management with break-even, trailing stop, and partial close features. A practical, step-by-step guide for traders who want to save time and improve consistency through automation.
This article teaches you how to build an MQL5 Expert Advisor that automatically detects support and resistance zones and executes trades based on them. You’ll learn how to program your EA to identify these key market levels, monitor price reactions, and make trading decisions without manual intervention.
Imagine transforming the traditional EA or indicator input properties into a real-time, on-chart control interface. This discussion builds upon our foundational work in the Market Periods Synchronizer indicator, marking a significant evolution in how we visualize and manage higher-timeframe (HTF) market structures. Here, we turn that concept into a fully interactive utility—a dashboard that brings dynamic control and enhanced multi-period price action visualization directly onto the chart. Join us as we explore how this innovation reshapes the way traders interact with their tools.
We continue to develop the algorithms for FinAgent, a multimodal financial trading agent designed to analyze multimodal market dynamics data and historical trading patterns.
