MQL5 Cloud Network has reached 16 billion completed tasks. Algo traders use it every day to test their strategies faster.

We have upgraded it for even higher performance:

• 7 upgraded servers that manage connected agents and distribute tasks more efficiently
• 60 Gbps increased network bandwidth for fast data transfer between algo traders and agents
• Improved server "hot cache" with historical data for quicker data distribution
• No more slow agents from virtual environments to make sure you always get the best CPUs for your tasks

Try cloud calculations and spend your time developing strategies instead of waiting for test results.

We also invite you to join the network. Daily activities like browsing, office work or watching videos require a tiny portion of your PC power, leaving the rest unused. Why not share it and earn? Just download the Strategy Tester Manager and run a quick setup.

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Understanding the complexities of fish schooling behavior has led to the development of the Fish School Search (FSS) algorithm, a notable contribution to the field of swarm intelligence. Introduced by Bastos Filho and Lima Neto in 2008, the FSS is designed primarily for continuous optimization problems in multimodal search spaces and focuses on mimicking the collective movement patterns observed in fish schools.

The algorithm features distinct components such as the feeding and swimming operators, which handle the simulation of feeding success and coordinated school movement. The feeding mechanism encourages fish to move towards regions of higher yield, impacting their 'weight', which in turn influences the school's collective movement. The swimming operators divided into individual, instinctive-collective, and collective-volitional movements, facilitate exploration and exploitation ...

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A useful adaptation has been made to a common trading code available at MQL5's Codebase. The developer responsible for the tweak has added functionality that filters out Volume levels in conditions where Spreads are notably high. This adjustment could bring considerable advantage by enhancing the strategy's execution and efficacy during volatile market conditions. The revised code is accessible for further review, allowing other developers in the community to assess and potentially adopt this updated method for optimizing trade performance.

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Explore a sophisticated hybrid trading strategy for the S&P 500 using MQL5, blending AI and technical analysis to enhance trading decisions. This approach leverages vast datasets from MetaTrader 5, utilizing linear algebra for efficient analysis and pattern recognition. Understand how traditional indicators like the Commodity Channel Index and Relative Strength Index integrate with AI models to predict market movements.

The strategy involves constructing a dynamic Expert Advisor in MQL5 that combines the strengths of artificial intelligence with the reliability of technical analysis. The article includes detailed insights into the programming principles needed to analyze large datasets and techniques for uncovering non-obvious patterns in market data.

Further, the strategy is outlined with a step-by-step guide on setting up indicators, importing libraries, and handling data within t...

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Understanding and Implementing a Consolidation Range Breakout Strategy Using MQL5

In financial markets, strategies based on market consolidation and breakout are key for capturing major price movements post low volatility periods. This post discusses building an Expert Advisor (EA) with a Consolidation Range Breakout strategy using MQL5 for the MT5 platform.

A consolidation range marks a period where price oscillates horizontally and volatility is low, with distinct upper and lower boundaries referred to as resistance and support levels. Breaking these levels often results in substantial price moves. Traders can leverage these movements through a systematic approach that includes identifying and trading breakouts—both key components of the discussed strategy.

To implement, traders first identify the consolidation range through historical price data analysis, watching for breakouts ...

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Understanding the daily initialization process in automated trading systems is critical for bot performance. In this strategy, the bot starts each day by erasing all previous orders. It then determines the highest and lowest values from the previous day's bar to set up new pending orders: a BUY_STOP and a SELL_STOP. Noticeably, there is no TakeProfit set for these orders.

Key functions include:
- Initial setup where local variables are initialized.
- Regular checks and updates through the main code, which include:
1. Generating trading signals and executing new orders.
2. Implementing a trailing StopLoss that adjusts as the price moves beneficially, thereby potentially increasing profits.
3. Daily management of old orders to ensure the strategy starts fresh each day.

Additional factors monitored by the bot to optimize trade executions are volume values, account's free margin l...

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Introducing GIT—a vital tool for every programmer seeking to streamline their development process and safeguard their work from potential errors that might only surface after extensive modifications. GIT, initially designed for LINUX users, proves to be beneficial across other operating systems, although Windows 11 users might face some challenges in its deployment. For optimal use, Windows 10 is recommended.

The essence of GIT revolves around its capacity to manage versions of an application efficiently. Although it doesn't integrate directly with MQL5, adjustments can be made to ensure compatibility.

For those new to GIT or unfamiliar with its operation, it offers significant advantages by allowing tracking of changes within the programming code. This feature is particularly helpful in identifying unintended changes that may occur during the coding process.

Moreover, the setup an...

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Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are both types of recurrent neural networks that play a crucial role in handling sequence prediction problems. LSTMs are designed to circumvent the limitations posed by simple recurrent neural networks, primarily addressing the challenges associated with the vanishing gradient problem. By integrating gates that control the flow of information, LSTMs can preserve long-term dependencies in the data, which enhances their ability to process complex sequences.

On the other hand, GRUs provide a streamlined alternative to LSTMs. With a simpler architecture that includes fewer gates, GRUs facilitate quicker training times and reduced memory usage, which can be advantageous in certain applications. Despite their simplicity, GRU models often deliver performance comparable to that of LSTMs, especially in tasks where the sequence lengt...

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The DoEasy library continues its evolution as a robust tool for developers engaged in financial markets programming, specifically targeting those who work with trading algorithms. The library facilitates access to a broad range of data from trading environments, simplifying the sorting and handling of data lists based on specific parameters. A significant update introduces functionalities for the detection and graphical representation of price patterns on timeseries data.

The latest enhancements include the capability to associate discovered patterns with specific timeseries bars, fostering more intuitive analysis and decision-making based on the visual patterns observed. Furthermore, pattern management has been refined with the introduction of abstract and inherited pattern classes, along with expanded pattern control classes, ensuring a flexible, scalable approach to pattern identi...

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In the latest series on MQL5 programming, we are advancing towards creating an Expert Advisor (EA) that leverages candlestick patterns from the previous day to make trading decisions. The focus will be on crafting an EA that reacts to bullish and bearish signals by initiating corresponding buy or sell trades based on the analysis of the day’s first 1-hour candlestick close price. This project-based approach not only aims to clarify common queries for beginners but ensures the methodologies are practically applicable in real-world settings.

The EA will strictly enforce one open position at a time, with a cap of two trades per day. It will operate only during specified trading hours from Monday to Wednesday, adhering to predefined trade limits to optimize performance and risk management.

This project delves into essential aspects of MQL5 such as utilizing the Trade library for managin...

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In the financial markets, monitoring symbols with a positive swap in the Market Watch window is essential for identifying potentially profitable trades. The process involves filtering and identifying those symbols that offer favorable swap conditions. Upon detection, displaying this information on-screen can offer immediate insights for trading decisions. This technique not only aids in maximizing returns but also helps in strategic trade planning by revealing less apparent opportunities in the trading environment. Implementing this as part of a routine check can provide a considerable advantage in foreign exchange and commodity markets where swap rates play a significant role.

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Convolutional Neural Networks (CNNs) are widely recognized in the field of deep learning, particularly for their applications in image and video processing. The structured architecture of CNNs allows them to efficiently handle data with a grid-like topology, such as pixels in images. This is achieved through layers that perform convolutions, pooling, and classification.

The convolutional layers are crucial, utilizing filters to capture local pattern information from the input data, which could be anything from edges in images to specific features in time-series data. Pooling layers follow, reducing the dimensionality of the data, thus condensing the information and retaining essential features while reducing computation for subsequent layers.

In more advanced stages, fully connected layers integrate these features to perform classification or regression tasks. Dropout layers are als...

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In this article, the focus is on the impact of learning rates on the performance of Generative Adversarial Networks (GANs). GANs consist of two networks that train in conjunction—the discriminator and the generator. The discriminator learns to distinguish real data from generated data, while the generator learns to produce data that can fool the discriminator.

The article examines various types of learning rate schedules, their implementation, and their influence on GAN performance, specifically using the MQL5 platform. These include fixed, step decay, exponential decay, polynomial decay, inverse time decay, and cosine annealing learning rates. Each type is tested using the EURJPY currency pair over 2023, tracking total profit and recovery factor as performance metrics.

Particular attention is given to how these learning rates affect training stability and convergence, essential for...

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In the evolving landscape of automated signal systems, merging two sophisticated programs into a unified signal system presents both challenges and strategic opportunities. This integration, focusing on combining functionalities from the Trend Constraint indicator's previous versions, underscores the meticulous nature needed in software development.

Key to this process is the merging of WhatsApp and Telegram notifications within the MetaTrader 5 environment. Dual integration ensures that notifications are managed efficiently, leveraging the power of both platforms without the need for redundant operations. The integration process meticulously retains selected elements from both systems to optimize functionality, avoiding simple code duplication.

The critical focus is on maintaining the integrity of command executions, ensuring that commands remain discreet without obstructing other ...

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Recent research sheds light on top-ranked population optimization algorithms, focusing on their robustness and ability to effectively solve complex problems by achieving global maxima. The study provides an analytical breakdown of each algorithm’s key features, advantages, and strategic applications that contribute to their high performance in overcoming complex optimization challenges.

Highlighted algorithms have shown significant resilience against local optimization traps, showcasing their potential in handling diverse and complex test functions. This allows for a better understanding of each algorithm's mechanism and the critical success factors behind their efficiency.

Continued investigations and detailed analysis of these leading algorithms will likely pave the way for enhanced optimization techniques, potentially integrating and hybridizing these methods to improve practical...

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The modified Detrended Price Oscillator (DPO) now allows for comprehensive control over the moving average parameters, including the period, mode, and applied price. This enhanced functionality transforms the moving average curve into a horizontal line, which can be effectively used as a level of support or resistance.

The calculation of the DPO is given by the formula: DPO = Close - MA(mode, price, DPO_period). Here, 'Close' refers to the closing price of the bar; 'mode' specifies the method used to calculate the moving average, such as SMA, EMA, SMMA, or LWMA; 'price' denotes the price applied to the moving average, and 'DPO_period' is the period over which the moving average is calculated. This update provides a more versatile tool for technical analysis in trading strategies.

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In the latest advancement in GUI development within the WinForms environment, focus has shifted towards enhancing scrollbar functionality. The development starts with the base ScrollBar object, creating a foundation for the two derivative objects, ScrollBarVertical and ScrollBarHorizontal. These objects, while static, serve as essential components for managing content navigation within a form.

The key aspect of this development is the scrollbar’s adaptability to content overflow. When form content exceeds the container's capacity, the scrollbar will automatically appear to facilitate content navigation. This functionality is crucial for improving user interaction and experience.

Further improvements include modifying color dynamics based on user interaction: mouse hover and selection trigger visual cues, enhancing usability. A method that dynamically changes scrollbar visibility bas...

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Understanding human emotions' dynamics like fear and greed is fundamental in developing algorithms for trading. This newly developed script introduces a class that mathematically models these emotions, as well as other psychological factors like motivation, stress, confidence, and activity. Each of these has a defined range and interacts differently under market conditions.

The model assigns numerical values to each emotion, for instance, fear ranging from -1 to infinity, and greed from 0 to infinity. These values adjust based on market performance, potentially affecting trading decisions when programmed into expert advisors or indicators. Such models are crucial for enhancing decision-making processes in automated trading systems by simulating human psychological responses to market fluctuations.

Additionally, the script offers flexibility in customization, allowing users to adjust...

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In the latest discourse on Category Theory and its application in MQL5, the focus shifts toward understanding the foundational axioms of a category, specifically identity and association, along with the utility of commutative diagrams. Category Theory, a branch of mathematics, is utilized for systemizing and classifying data, proving particularly beneficial in financial time series analysis.

The narrative begins by dissecting what defines a category through the exploration of identity and association axioms, enriched by practical examples and situational analysis. Identity isomorphism and its critical role in ensuring structural preservation within and across domains are detailed, explaining how the morphisms uphold algebraic integrity during data mappings.

The article advances by illustrating the applicative nature of isomorphism within MQL5 environments, showcasing coded examples ...

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