Programming templates can enhance code efficiency and flexibility, particularly in MQL5. While not always common in this language, templates simplify implementation and help avoid complex errors. Previous discussions have highlighted the utility of function and procedure templates for handling various data types. Even routine tasks, like calculating averages, can benefit from template usage by ensuring consistency and reducing redundancy.

Incorporating templates with unions presents additional opportunities, allowing dynamic configurations that adjust byte sizes based on data types. A properly implemented template accounts for data type variations, assisting both novice and experienced developers in creating scalable and maintainable code structures. Further exploration into union templates will provide more comprehensive insights.

Careful attenti...

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Discover the power of Firebase for app development without the backend complexity. Firebase offers two robust NoSQL databases: Realtime Database and Cloud Firestore. Realtime Database excels in low-latency, real-time syncing for chat apps or live dashboards. Meanwhile, Cloud Firestore is optimized for extensive scalability and complex data querying, ideal for ambitious projects. Both databases support secure data operations through advanced security rules, and are perfect for algorithmic trading applications. By integrating MQL5, traders can seamlessly manage and visualize trading data on custom dashboards, enhancing clarity and decision-making capabilities. Embrace Firebase to build dynamic, data-driven applications effortlessly.

👉 Read | AlgoBook | @mql5dev

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A novel zigzag indicator, now available for MQL5, shifts focus from timeframe dependency to pure asset price percentage changes from extremums. Unlike its Autoscale predecessor, this version integrates seamlessly across all securities through its use of price percentage input. Developed to streamline the analysis process, it's an adaptation of Evgeniy Chumakov's original MQL4 structure.

This indicator is designed strictly around price movements, excluding time considerations. Its structure enhances trend monitoring similar to a Renko chart. It is crucial to note that this tool should not be used in isolation for trade decisions. Combining it with a momentum oscillator may provide better insights into optimal entry points in the market.

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The BotCilento Expert Advisor for MetaTrader 5 is designed for strategy testers and simulation environments. It focuses on grid trading with trend-following signals and adaptive risk management. Implementing the Dual MA Strategy, it operates on crossover signals of fast and slow SMAs on the H1 timeframe, filtering activity through volume and volatility measures. The smart grid management system permits position additions using dynamic intervals, either fixed pips or ATR-based. It offers Martingale or Arithmetic lot sizing strategies and configurable profit or breakeven targets for closing entire grids.

Risk safeguards include auto-stop at daily loss limits and emergency drawdown-based closures. Position expiration and spread/slippage filters are integral. With features like "hold through reversals" mode and stagnation detection, the EA displays real-time pro...

👉 Read | AlgoBook | @mql5dev

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Part 37 in the Price Action Analysis Toolkit Development series introduces the Sentiment Tilt Meter (STM), designed to tackle cluttered data visualizations in trading. STM aggregates candle-level metrics across configurable timeframes, producing a scalar score from -100 to +100. Displayed on a compact dashboard, it maintains clarity through persistent annotations anchored to timestamps and price levels. It enables precise rule-based settings for tuning sensitivity, ensuring a balance between noise suppression and responsiveness.

In the MQL5 implementation, the STM incorporates a methodical process, starting with environment setup in MetaTrader 5 and MetaEditor. The EA template initializes with strict property declarations, constant settings, and essential libraries like Trade.mqh. User-configurable inputs determine timeframe signals, smoothing, and alert pa...

👉 Read | NeuroBook | @mql5dev

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Explore the potential of supervised learning to enhance underperforming trading signal patterns using a Convolutional Neural Network (CNN) with kernel regression and dot product similarity. This innovative approach extracts hidden value from seemingly weak signals. The model utilizes kernel regression to measure similarity, refining CNN performance by capturing long-range dependencies. This enhances predictive capabilities in time series data, offering practical benefits for algorithmic trading. Considerations like local-attention-windows mitigate computational costs. The method is adjustable, capable of tuning kernel sizes and channel depths to match data-driven needs, leading to an architecture that is both comprehensive and responsive to trading indicators.

👉 Read | Forum | @mql5dev

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In the latest installment of the MetaTrader 5 Machine Learning Blueprint series, focus is placed on improving labeling techniques for trading algorithms. Common fixed-time horizon labeling methods are critiqued for their inadequacies in reflecting real-world trading behaviors. Instead, dynamic approaches like the triple-barrier method and meta-labeling are explored, which mirror actual trading conditions by incorporating profit targets, stop-losses, and time limits. These methods align labeling with risk management and adaptation to market volatility, crucial for building robust and reliable machine-learning models in finance. Understanding these methods is essential for effective model implementation and successful trading strategies.

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SuperTrend identified as 0: Used for trend direction and reversals in trading strategies. Direnc marked as 1: Indicates areas where price action may face resistance. Support designated as 2: Highlights zones where price declines may halt. Trend listed as 3: Represents general direction of the market or asset price movement over time. Understanding these elements can enhance technical analysis and improve decision-making in trading environments. Properly utilizing such indicators can assist in developing robust trading strategies tailored to various market conditions. Ensuring consistency in analyzing these features may contribute to better forecasting and market insights.

👉 Read | Freelance | @mql5dev

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Asynchronous execution of algorithms is crucial for enhancing performance in software systems. An interface similar to JavaScript Promises offers these capabilities, allowing asynchronous operations to be handled efficiently in various programming environments. The provided `timer.mqh` serves as an illustration of asynchronous execution, showcasing the potential of this approach. Developers can leverage this concept by utilizing npm package manager to manage dependencies and implement similar async functions in their projects.

Promise in JavaScript enables executing concurrent operations without blocking the main thread. Functions like `Promise.all`, `Promise.race`, and `Promise.any` provide robust mechanisms for handling multiple asynchronous tasks with ease, enhancing control and flexibility in software design. Deploying these techniques across var...

👉 Read | Freelance | @mql5dev

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Introducing Tarantella EA, an advanced adaptive grid trading system designed for strategy testing. This Expert Advisor integrates traditional grid methodologies with contemporary risk management strategies. Its foundation lies in executing buy/sell orders at Fibonacci-based intervals, adjusting position sizes dynamically in response to market conditions.

Key features include Market Profile Integration, confining trades to high-probability Value Areas while avoiding Point of Control zones. Multi-layer trend filtering utilizes ADX for trend strength alongside MA/MACD for directional approvals across timeframes. Adaptive Grid Management employs Fibonacci ratios for grid spacing adjustments.

Tarantella EA implements comprehensive risk management, with position limits and equity-based closure rules, incorporating advanced entry conditions requiring volume fil...

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Dive into the intricacies of logistic regression, a fundamental tool for binary classification, pivotal in algorithmic trading. Unlike its linear counterpart, logistic regression utilizes the sigmoid function to predict probabilities in binary outcomes, essential for decisions like Buy/Sell. Delve into practical steps such as data cleaning, handling NaN values, and feature encoding, crucial for effective model training. Explore the role of the confusion matrix in evaluating model accuracy, ensuring reliable predictions. This introduces a foundation for dynamic logistic regression models in trading, enhancing decision-making by accurately classifying outcomes, a key skill for MetaTrader 5 developers.

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#MQL5 #MT5 #Algorithm

An indicator is available that calculates target levels based on the average of price movements for specified timeframes such as annually, monthly, weekly, or every 4 hours. Calculation of the current levels involves defining several buffers: OpenBuffer[i] holds the opening price for the period. HighBuffer[i] is established at the period's opening price plus half the average daily range (adr/2). LowBuffer[i] is set at the period's opening price minus half the average daily range (adr/2). MaxHighBuffer[i] anchors at the opening price plus the full average daily range (adr). MinLowBuffer[i] fixes at the opening price minus the full average daily range (adr). This structure aids in identifying strategic price targets.

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The Multi-Divergence EA capitalizes on market divergence to automate trading strategies by identifying potential reversal points using three key oscillators: RSI, MACD, and Stochastic Oscillator. The strategy focuses on high-probability signals through a robust confirmation process, minimizing market noise interference.

Key detection methods include:
- Bullish Divergence: Signaled by a price making a lower low against an indicator's higher low.
- Bearish Divergence: Occurs with a higher high price but a lower high indicator reading.

The EA's strength lies in its reliance on indicator confluence rather than a single indicator. It requires multiple indicators to confirm divergence before executing trades, enhancing signal reliability. To further refine accuracy, optional filters such as a Trend Filter and Volume Filter are available. The Trend Filter utiliz...

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The MQL5 JSON Library is crafted to handle JSON data efficiently within the MQL5 ecosystem. It provides functionalities similar to modern languages, such as JavaScript and Python, through its intuitive Document Object Model (DOM) API. This library supports data parsing, manipulation, and serialization in various tasks, from simple configurations to complex data exchanges.

Key features include reliable JSON parsing from strings or files, and seamless creation of JSON objects and arrays with concise APIs. Its flexible parser supports JSON5 features like comments and trailing commas. Users can access and manipulate data effortlessly with syntax akin to Python dictionaries or JavaScript objects, allowing safe type conversions and dynamic modifications.

For advanced processing, the library includes a robust query engine utilizing JSON Pointer and JSONPath for prec...

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Explore the potential of the Commodity Channel Index (CCI) with this comprehensive guide on designing effective trading systems using MetaTrader 5. Learn the CCI fundamentals, from its calculation to practical applications, enhancing your approach to spotting trends and making informed decisions. Dive into strategies that leverage CCI for both trending and sideways markets, and discover how to automate these strategies through precise MQL5 programming. Transform trading plans into disciplined execution, minimizing emotional interference. This article offers technical clarity, empowering traders and developers with actionable insights to optimize their algorithmic trading endeavors efficiently.

👉 Read | Calendar | @mql5dev

#MQL5 #MT5 #CCI

A spread monitoring Expert Advisor (EA) provides critical insights for traders dealing with varied broker spread rules, including fixed, ECN, and standard accounts. This tool is instrumental for those wishing to gauge spread fluctuations and optimize EA performance accordingly. It continuously tracks and records the minimum and maximum spread values per day, resetting at midnight based on the broker's terminal time.

For flexibility, users can toggle between pips and points in the settings and choose to sort instruments by the highest spread. The EA is assigned to a chart that isn't involved in real trading, displaying data for all instruments visible in the Market Watch. By utilizing a unique labeling system, it minimizes traditional comment formatting issues, allowing clean presentation without the influence of chart backgrounds or grids.

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Delving into the complex world of templates in MQL5, this article demystifies the challenge of type overloading, offering clarity to both novices and seasoned developers. It highlights the intricacies of template applications, emphasizing precise data type determination for seamless compiler execution. Common pitfalls such as incorrect variable declaration and type specification are addressed, illustrating how to enable seamless overloading in both procedures and functions. With practical examples and step-by-step solutions, it underscores the importance of understanding fundamental concepts, like the role of the typename, ensuring accurate code compilation and execution, ultimately aiding developers in crafting efficient algorithmic trading strategies.

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Machine learning models often make assumptions about data that may not hold true in real-world trading. Traditional statistical learning offers limited guidance on these relationships. Human traders, influenced by years of market experience, develop intuition-based strategies. These discretionary rules may provide a useful framework for machine learning applications.

A breakout trading strategy highlights the value of market logic. By observing price levels from previous days, this basic strategy reflects similar accuracy to complex deep neural networks. However, the strategy's volatility and aggressive nature necessitate adjustments for better control.

Testing and improving the strategy showed increased profits and reduced trading activity. Yet, it resulted in lower trading accuracy. Human intuition alone doesn't guarantee improvement, emphasizing the need...

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Traders face difficulties trading multiple symbols during high-volatility events using MetaTrader 5's default setup, which supports only one Expert Advisor (EA) per chart. We've devised a solution that integrates a multi-symbol trading feature within the News Headline EA.

This enhancement enables managing multiple pairs from a single chart, using intuitive trading buttons built upon the MQL5 Standard Library and custom trading classes. This sophisticated EA can handle multiple symbols seamlessly, crucial for quick reactions to market changes during news releases.

The integration involves modifying the CTradingButtons class to support multi-symbol trading and adapting the News Headline EA to include this new functionality, offering traders significant efficiency and control.

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The previous segment of this series discussed a backtested statistical arbitrage strategy involving cointegrated microprocessor sector stocks. It highlighted the volatility in cointegration due to external factors like company dynamics or economic shifts. The need for dynamic portfolio weight adjustments was also emphasized. The discussion moved towards the necessity of a persistent database setup to handle real-time updates efficiently, utilizing Metatrader 5 and Python integration.

For implementing a robust system, setting up a time-series specialized database, such as SQLite within Metatrader 5, is suggested. The database schema should include key structures like "symbol" and "market_data" tables. This system aims to enable seamless model and portfolio updates to adapt to market conditions continually. Adequate setup of this database infrastructure ...

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