In Part 38, we build a production-grade MT5 monitoring panel that converts raw ticks into actionable signals. The EA buffers tick data to compute tick-level VWAP, a short-window imbalance (flow) metric, and ATR-based position sizing. It then visualizes spread, ATR, and flow with low-flicker bars. The system calculates a suggested lot size and a 1R stop, and issues configurable alerts for tight spreads, strong flow, and edge conditions. Auto-trading is intentionally disabled; the focus remains on robust signal generation and a clean user experience.
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.
For many traders, the gap between knowing a risk rule and following it consistently is where accounts go to die. Emotional overrides, revenge trading, and simple oversight can dismantle even the best strategy. Today, we will transform the MetaTrader 5 platform into an unwavering enforcer of your trading rules by developing a Risk Enforcement Expert Advisor. Join this discussion to find out more.
This article will show you how to visualize candle data obtained via the WebRequest function and API in candle format. We'll use MQL5 to read the candle data from a CSV file and display it as custom candles on the chart, since indicators cannot directly use the WebRequest function.
In this article, we create a Pivot-Based Trend Indicator in MQL5 that calculates fast and slow pivot lines over user-defined periods, detects trend directions based on price relative to these lines, and signals trend starts with arrows while optionally extending lines beyond the current bar. The indicator supports dynamic visualization with separate up/down lines in customizable colors, dotted fast lines that change color on trend shifts, and optional gradient filling between lines, using a canvas object for enhanced trend-area highlighting.
A practical guide to building a Larry Williams–style market structure indicator in MQL5, covering buffer setup, swing-point detection, plot configuration, and how traders can apply the indicator in technical market analysis.
MQL5 lacks built-in asymmetric cryptography, making secure data exchange over insecure channels like HTTP difficult. This article presents a pure MQL5 implementation of RSA using PKCS#1 v1.5 padding, enabling safe transmission of AES session keys and small data blocks without external libraries. This approach provides HTTPS-like security over standard HTTP and even more, it fills an important gap in secure communication for MQL5 applications.
In this article, we develop a gauge-style RSI indicator in MQL5 that visualizes Relative Strength Index values on a circular scale with a dynamic needle, color-coded ranges for overbought and oversold levels, and customizable legends. We utilize the Canvas class to draw elements like arcs, ticks, and pies, ensuring smooth updates on new RSI data.
Market rules are continuously evolving, and many once-reliable principles gradually lose their effectiveness. What worked in the past no longer works consistently over time. Today’s discussion focuses on probability ranges and how they can be used to navigate market irregularities. We will leverage MQL5 to develop an algorithm capable of trading effectively even in the choppiest market conditions. Join this discussion to find out more.
This article demonstrates how the stochastic oscillator, a classical technical indicator, can be repurposed beyond its conventional use as a mean-reversion tool. By viewing the indicator through a different analytical lens, we show how familiar strategies can yield new value and support alternative trading rules, including trend-following interpretations. Ultimately, the article highlights how every technical indicator in the MetaTrader 5 terminal holds untapped potential, and how thoughtful trial and error can uncover meaningful interpretations hidden from view.
The article presents a new metaheuristic algorithm, Chaos Game Optimization (CGO), which demonstrates a unique ability to maintain high efficiency when dealing with high-dimensional problems. Unlike most optimization algorithms, CGO not only does not lose, but sometimes even increases performance when scaling a problem, which is its key feature.
This article proposes an asset screening process for a statistical arbitrage trading strategy through cointegrated stocks. The system starts with the regular filtering by economic factors, like asset sector and industry, and finishes with a list of criteria for a scoring system. For each statistical test used in the screening, a respective Python class was developed: Pearson correlation, Engle-Granger cointegration, Johansen cointegration, and ADF/KPSS stationarity. These Python classes are provided along with a personal note from the author about the use of AI assistants for software development.
Many traders have experienced this situation, often stick to their entry criteria but struggle with trade management. Even with the right setups, emotional decision-making—such as panic exits before trades reach their take-profit or stop-loss levels—can lead to a declining equity curve. How can traders overcome this issue and improve their results? This article will address these questions by examining random win-rates and demonstrating, through Monte Carlo simulation, how traders can refine their strategies by taking profits at reasonable levels before the original target is reached.
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.
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.
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.
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.
In high-probability support and resistance zones, valid entry confirmation signals are always present once the zone has been correctly identified. In this discussion, we build an intelligent MQL5 program that automatically detects entry conditions within these zones. We leverage well-known candlestick patterns alongside native confirmation indicators to validate trade decisions. Click to read further.
In this article, we will attempt to predict the market with a decent model for time series forecasting named DeepAR. A model that is a combination of deep neural networks and autoregressive properties found in models like ARIMA and Vector Autoregressive (VAR).
This article proposes another original approach to creating trading systems based on machine learning, using clustering and trade labeling for mean reversion strategies.
In this article, we enhance the gauge-based indicator in MQL5 to support multiple oscillators, allowing user selection through an enumeration for single or combined displays. We introduce sector and round gauge styles via derived classes from a base gauge framework, improving case rendering with arcs, lines, and polygons for a more refined visual appearance.
In this article, we will share insights on how to leverage MQL5 programming to pinpoint market levels—differentiating between weaker and strongest price levels. We will fully develop a working, Support and Resistance Strength Indicator (SRSI).
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.
News trading often requires managing multiple positions and symbols within a very short time due to heightened volatility. In today’s discussion, we address the challenges of multi-symbol trading by integrating this feature into our News Headline EA. Join us as we explore how algorithmic trading with MQL5 makes multi-symbol trading more efficient and powerful.
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.
Global market sessions shape the rhythm of the trading day, and understanding their overlap is vital to timing entries and exits. In this article, we’ll build an interactive trading sessions EA that brings those global hours to life directly on your chart. The EA automatically plots color‑coded rectangles for the Asia, Tokyo, London, and New York sessions, updating in real time as each market opens or closes. It features on‑chart toggle buttons, a dynamic information panel, and a scrolling ticker headline that streams live status and breakout messages. Tested on different brokers, this EA combines precision with style—helping traders see volatility transitions, identify cross‑session breakouts, and stay visually connected to the global market’s pulse.
The UT BOT ATR Trailing Indicator is a personal and customizable indicator that is very effective for traders who like to make quick decisions and make money from differences in price referred to as short-term trading (scalpers) and also proves to be vital and very effective for long-term traders (positional traders).
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.
Successful Restaurateur Algorithm (SRA) is an innovative optimization method inspired by restaurant business management principles. Unlike traditional approaches, SRA does not discard weak solutions, but improves them by combining with elements of successful ones. The algorithm shows competitive results and offers a fresh perspective on balancing exploration and exploitation in optimization problems.
In this article, we enhance the AI-powered trading system in MQL5 with user interface improvements, including loading animations for request preparation and thinking phases, as well as timing metrics displayed in responses for better feedback. We add response management tools like regenerate buttons to re-query the AI and export options to save the last response to a file, streamlining interaction.
This article demonstrates how to integrate the Google Generative AI API with MetaTrader 5 using MQL5. You will learn how to structure API requests, handle server responses, extract AI-generated content, manage rate limits, and save the results to a text file for easy access.
Master the automation of Larry Williams’ short-term swing patterns using MQL5. In this guide, we develop a fully configurable Expert Advisor (EA) that leverages non-random market structures. We’ll cover how to integrate robust risk management and flexible exit logic, providing a solid foundation for systematic strategy development and backtesting.
Build a practical MetaTrader 5 “Sigma Score” indicator from scratch and learn what it really measures: The z-score of log returns (how many standard deviations the latest move is from the recent average). The article walks through every code block in OnInit(), OnCalculate(), and OnDeinit(), then shows how to interpret thresholds (e.g., ±2) and apply the Sigma Score as a simple “market stress meter” for mean-reversion and momentum trading.
In this article, we develop a custom indicator in MQL5 called Smart WaveTrend Crossover, utilizing dual WaveTrend oscillators—one for generating crossover signals and another for trend filtering—with customizable parameters for channel, average, and moving average lengths. The indicator plots colored candles based on the trend direction, displays buy and sell arrow signals on crossovers, and includes options to enable trend confirmation and adjust visual elements like colors and offsets.
