Articles on manual and algorithmic trading in MetaTrader 5

The Darvas Box Breakout Strategy, created by Nicolas Darvas, is a technical trading approach that spots potential buy signals when a stock’s price rises above a set "box" range, suggesting strong upward momentum. In this article, we will apply this strategy concept as an example to explore three advanced machine learning techniques. These include using a machine learning model to generate signals rather than to filter trades, employing continuous signals rather than discrete ones, and using models trained on different timeframes to confirm trades.

In this article, we take the first step in MQL5 programming, even for complete beginners. We'll show you how to transform familiar candlestick patterns into a fully functional custom indicator. Candlestick patterns are valuable as they reflect real price action and signal market shifts. Instead of manually scanning charts—an approach prone to errors and inefficiencies—we'll discuss how to automate the process with an indicator that identifies and labels patterns for you. Along the way, we’ll explore key concepts like indexing, time series, Average True Range (for accuracy in varying market volatility), and the development of a custom reusable Candlestick Pattern library for use in future projects.

The Multi-Currency Expert Advisor in this article is Expert Advisor or trading robot that can trade (open orders, close orders and manage orders for example: Trailing Stop Loss and Trailing Profit) for more than one symbol pair only from one symbol chart. This time we will use only 1 indicator, namely Triangular moving average in multi-timeframes or single timeframe.

In this article, we apply a relatively complex neural network algorithm released in 2023 called PatchTST for predicting the price action for the next 24 hours. We will use the official repository, make slight modifications, train a model for EURUSD, and apply it to making future predictions both in Python and MQL5.

This article continues the series for beginners. Here we will discuss the basic principles of developing Expert Advisors (EAs). We will create two EAs: the first one will trade without indicators, using pending orders, and the second one will be based on the standard MA indicator, opening deals at the current price. Here I assume that you are no longer a complete beginner and have a relatively good command of the material from the previous articles.

The article discusses the practical aspects of using optimization algorithms to find the best EA parameters on the fly, as well as virtualization of trading operations and EA logic. The article can be used as an instruction for implementing optimization algorithms into an EA.

In this article, we develop a Gartley Pattern system in MQL5 that identifies bullish and bearish Gartley harmonic patterns using pivot points and Fibonacci ratios, executing trades with precise entry, stop loss, and take-profit levels. We enhance trader insight with visual feedback through chart objects like triangles, trendlines, and labels to clearly display the XABCD pattern structure.

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.

The extent of liquidity zones and the magnitude of the breakout range are key variables that substantially affect the probability of a retest occurring. In this discussion, we outline the complete process for developing an indicator that incorporates these ratios.

In this article, we enhance a neural network trading strategy in MQL5 with an adaptive learning rate to boost accuracy. We design and implement this mechanism, then test its performance. The article concludes with optimization insights for algorithmic trading.

The Multi-Currency Expert Advisor in this article is an Expert Advisor or Trading Robot that can trade (open orders, close orders and manage orders for example: Trailing Stop Loss and Trailing Profit) for more than one symbol pair from only one symbol chart. In this article we will use signals from two indicators, in this case Bollinger Bands® on Keltner Channel.

Discover how to fix a critical flaw in financial machine learning that causes overfit models and poor live performance—label concurrency. When using the triple-barrier method, your training labels overlap in time, violating the core IID assumption of most ML algorithms. This article provides a hands-on solution through sample weighting. You will learn how to quantify temporal overlap between trading signals, calculate sample weights that reflect each observation's unique information, and implement these weights in scikit-learn to build more robust classifiers. Learning these essential techniques will make your trading models more robust, reliable and profitable.

This article outlines the steps to implement the Deus EA based on the RSI and Moving Average indicators for guiding automated trading.

In this discussion we will Automate Swing Extremes and the Pullback Indicator, which transforms raw lower-timeframe (LTF) price action into a structured map of market intent, precisely identifying swing highs, swing lows, and corrective phases in real time. By programmatically tracking microstructure shifts, it anticipates potential reversals before they fully unfold—turning noise into actionable insight.

The article takes a quick look at the ALGLIB 3.19 numerical analysis library, its applications and new algorithms that can improve the efficiency of financial data analysis.

In this article, we develop a 3 Drives Pattern system in MQL5 that identifies bullish and bearish 3 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.

In this article I will show the first part of the improvements that allowed me not only to close the entire automation chain for MetaTrader 4 and 5 trading, but also to do something much more interesting. From now on, this solution allows me to fully automate both creating EAs and optimization, as well as to minimize labor costs for finding effective trading configurations.

Metamodels in machine learning: Auto creation of trading systems with little or no human intervention — The model decides when and how to trade on its own.

The multi-currency expert advisor in this article is an expert advisor or automated trading that uses ZigZag indicator which are filtered with the Awesome Oscillator or filter each other's signals.

This article outlines the design of a double moving average crossover strategy that uses signals from a higher timeframe (D1) to guide entries on a lower timeframe (M15), with stop-loss levels calculated from an intermediate risk timeframe (H4). It introduces system constants, custom enumerations, and logic for trend-following and mean-reverting modes, while emphasizing modularity and future optimization using a genetic algorithm. The approach allows for flexible entry and exit conditions, aiming to reduce signal lag and improve trade timing by aligning lower-timeframe entries with higher-timeframe trends.

Decision trees imitate the way humans think to classify data. Let's see how to build trees and use them to classify and predict some data. The main goal of the decision trees algorithm is to separate the data with impurity and into pure or close to nodes.

There are quite a lot of different trading strategies. So, it might be useful to apply several strategies working in parallel to diversify risks and increase the stability of trading results. But if each strategy is implemented as a separate Expert Advisor (EA), then managing their work on one trading account becomes much more difficult. To solve this problem, it would be reasonable to implement the operation of different trading strategies within a single EA.

In this article, we will create an arbitration system that remains legal in the eyes of brokers, creates thousands of synthetic prices on the Forex market, analyzes them, and successfully trades for profit.

This phase fine-tunes your multi-pair EA to adapt trade size and risk in real time using volatility metrics like ATR boosting consistency, protection, and performance across diverse market conditions.

In this article, we explore the automation of the Cypher harmonic pattern in MQL5, detailing its detection and visualization on MetaTrader 5 charts. We implement an Expert Advisor that identifies swing points, validates Fibonacci-based patterns, and executes trades with clear graphical annotations. The article concludes with guidance on backtesting and optimizing the program for effective trading.