The Candle Size Calculation indicator efficiently measures the amplitude of the candles, displaying the difference between high and low in points or pips. The tool uses different colors to differentiate between bullish and bearish candles, aiding in better visual interpretation. Users can customize analysis based on specific days of the week. Each day is assigned a unique color, facilitating the observation of daily patterns.

Dynamic text positioning ensures that the information does not overlap with other crucial chart elements. Text attributes such as font, size, color, and anchor type are customizable. Support for different line styles enhances visualization. The auto-clean feature removes all objects when the indicator is detached, maintaining chart clarity.

Configurable variables include the number of candles to process, calculation types (opt...

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MARSI-EA provides a strategic approach for newcomers seeking to understand trading in financial markets. It integrates the logic of the Relative Strength Index (RSI) with a Simple Moving Average (SMA), executing trades on assets like XAUUSD. The EA emphasizes dynamic lot sizing in line with account risk, entry conditions based on RSI levels, and trend filtering via moving averages. Stop Loss and Take Profit are precisely adjusted to the symbol's tick precision.

Core entry rules include initiating a buy when the price is bullish and the RSI indicates an oversold market. Similarly, a sell occurs when the price is bearish with an overbought RSI reading. Proper understanding of these conditions is pivotal for effective trade execution.

Key variable inputs encompass maPeriod, rsiPeriod, rsiOverbought, rsiOversold, riskPercent, stopLoss, takeProfit, and slippa...

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The recent development aimed at enhancing library functionality has targeted improvements in handling both extended and standard graphical objects on the canvas. The objective was to rectify flaws in object interaction, mainly regarding object visibility and interaction order. Implementing the ZOrder property for GUI elements proves pivotal, establishing the correct layering of interactive elements on the chart.

With the ZOrder property now integrated, graphical components can be arranged correctly, ensuring GUI elements remain interactive and prominent. Virtual methods have been refined across object classes to effectively manage ZOrder handling, crucial for event-driven graphical operations.

The development has focused on incorporating methods to determine graphical object boundaries, enhancing interaction precision. Object properties and calc...

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Version 1.04 introduces cursor synchronization across multiple charts, although this feature does not extend to detached charts. Users can achieve synchronous chart movement by stretching the terminal across two monitors. In version 1.05, the update includes the option to synchronize only symbols that share the same name, enhancing targeted analysis and reducing clutter. By 31.05.2025, version 1.08 has incorporated several practical enhancements, boosting overall functionality and improving the user experience. These incremental updates reflect ongoing optimization efforts, focusing on synchronization features and user interface improvements.

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CryptoTrend 1.00 is an automated Expert Advisor specifically designed for Bitcoin trading and optimized for the 2024 market landscape. This system leverages several key components for effective market analysis and decision-making.

The Bollinger Indicator is employed to identify market extremes. When the price crosses beyond specified Bollinger lines, this generates an entry signal. A Filter by Order Block refines entry signals by using historical data to define support and resistance zones, reducing false entries.

A self-learning mechanism is incorporated to dynamically adjust the TrendThreshold entry levels based on ongoing trade performance. This allows for adaptation to shifting market conditions. For risk management, Stop Loss and Take Profit levels are set according to the difference between entry prices and support/resistance zones, with an aggressive risk...

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Leveraging the RAD system in MetaTrader 5 can significantly streamline the development of Expert Advisors by enhancing functionality with ease. By planning and organizing your project structure, introducing new features like asset name and daily accumulated value becomes a straightforward task. Modifying the enumeration efficiently and placing code strategically avoids complications that arise from disorganized updates.

When expanding your IDE, focus on reusing code and adding necessary control points, ensuring a consistent and robust integration process. Consider implementing enhancements in separate classes, such as C_OrderView for handling order functions, to maintain organization and prevent code bloat.

Optimize performance by refining functions like OnTrade and UpdateRoof, reducing execution time, especially during open positions. This optimization is ...

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A new MetaTrader 4 (MT4) indicator has been developed, providing insights based on the crossover between Bollinger Bands and price movements. This tool uses the Bollinger Bands technique, a well-known method in technical analysis for identifying potential entry and exit points. The indicator tracks the crossover events where price crosses the upper or lower bands, signaling possible trend reversals or continuations. This can be beneficial for traders looking to optimize their trades based on volatility and market momentum. The inclusion of such indicators can aid in more informed decision-making for those trading within the MT4 platform.

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Analyzing the Boom & Crash Interceptor EA reveals a methodical approach to trading spikes akin to missile defense systems. The EA is structured with multiple confirmation layers, utilizing velocity thresholds, ATR surge detectors, trend alignment checks, and optional pivot-zone filters to identify high-probability market moves.

Each layer operates independently but in cohesion, ensuring signals are generated only when conditions precisely align. This disciplined strategy reduces noise and enhances focus on genuine threats in market dynamics.

Comprehensive testing and refinement are recommended before live deployment. Through strategic backtesting and calibration, traders can optimize performance and integrate this tool effectively within broader trading frameworks.

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Moving averages are foundational in technical analysis, widely applied in areas beyond finance, such as meteorology and machine learning. Simple Moving Average (SMA) uses an arithmetic mean to indicate trend direction, offering ease of calculation but can trail price changes, especially with longer periods. Implementing this in MetaTrader 5 involves looping calculations, starting not from zero to ensure enough historical bars on the left for averaging.

Optimizations involve recalculating only with new ticks, avoiding redundant history reevaluation. Exponential Moving Average (EMA) adds a portion of the current closing price to its previous value, ensuring recent data impact is preserved, thus reacting more swiftly to price changes compared to SMA. Medium on trend detection, EMAs are popular in crossover setups to indicate trade opportunities.

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A compound interest calculator is now available for traders within the MetaTrader 5 terminal. This tool evaluates your risk of ruin and determines the optimal risk per trade based on your specified parameters. It provides forecasts of your capital size over various time frames, including monthly, annually, and at the end of your selected term. This allows for precise planning and informed risk management within the familiar trading environment of MetaTrader 5. Traders can now integrate these insights directly into their strategy development and execution process, facilitating a more comprehensive approach to trading.

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A new on-chart feature is being introduced for MetaTrader 5, aimed at optimizing trading interface efficiency. This rule-based insights lane addresses the clutter caused by the traditional layout where oscillators appear in separate sub-windows. By utilizing the CCanvas class and the MQL5 API, indicator signals will be integrated directly into the main chart, providing a streamlined and clear display.

A consolidated lane will showcase insights from RSI, CCI, Stochastic, and MACD, preventing chart compression and ensuring better visibility of price movements. The approach offers traders options to expand indicator details, maintaining a focus on clean, efficient design.

The implementation relies on a handle-buffer-release method for indicator management, optimized for seamless integration into existing systems. This ensures robust, on-demand retrie...

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The Prophet model, developed by Meta, is an open-source forecasting tool for time series data, particularly effective for data exhibiting strong seasonality and trends. It uses a piecewise-linear or logistic growth trend, Fourier series for seasonality, and incorporates holidays for adjustments in forecasts. Implementing Prophet in Python requires two key features from the Pandas DataFrame: the date stamp ("ds") and the target variable ("y"). Unlike models like ARIMA, Prophet handles non-stationary data efficiently. Adding holidays as dummy variables enhances predictive accuracy by accounting for historical events that cause deviations. Despite complexity, Prophet forecasts offer a comprehensive view through automated predictions and confidence intervals.

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Delve into the enhancements made to the Atomic Orbital Search algorithm, which are designed to unlock new potentials in optimization tasks. By refining its method of electron movement and improving solution search efficiency, the algorithm now exhibits superior adaptability and performance. The modified version, AOSm, intelligently incorporates lognormal distribution and adjusts its parameters for optimized solution discovery. Through rigorous testing, AOSm demonstrates significant improvements in handling complex, multi-layered search spaces, effectively clumping around potential solutions. As a result, AOSm achieves a remarkable 55% of the maximum possible rating, showcasing notable advancements in algorithmic optimization strategies suitable for algorithmic trading and development.

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An Expert Advisor (EA) is available that generates trading signals utilizing the Relative Strength Index (RSI) indicator. It functions by identifying when the RSI crosses above an upper threshold, signifying an overbought market condition, or below a lower threshold, indicating an oversold status. When these conditions are detected, the EA automatically initiates corresponding trade actions. This approach can help automate the trading process based on market momentum indicators, potentially optimizing decision-making by reacting promptly to RSI signal changes.

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In the previous discussion, a Multi-Symbol Strategy using Commodity Channel Index (CCI) and Awesome Oscillator (AO) was created for automated trend reversal trades. The focus now shifts to a neural network-based trading strategy utilizing adaptive learning rates to enhance prediction accuracy. This approach processes various market indicators and adjusts its learning process to optimize trade execution.

The strategy involves constructing a neural network comprising input, hidden, and output layers, complete with weights and biases. The key elements include forward propagation for prediction, backpropagation for refining accuracy, and an adaptive learning rate mechanism to manage weight updates effectively. This ensures stable learning, adapting swiftly to market volatility.

Implementation in MetaQuotes Language 5 (MQL5) begins by setting up neural netwo...

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In recent discussions, we examined integrating a CNN with a Rational Quadratic (RQ) Kernel to enhance signal interpretation and application for the MACD and On-Balance-Volume (OBV) indicators. While the classic forward walk test offered limited profitable patterns, exploring machine learning has shown potential in advancing indicator performance.

The RQ Kernel in CNN frameworks emphasizes patterns by adjusting kernel sizes and channel numbers. Adaptive attention masking and distance-aware pooling represent notable use cases. Despite fixed kernels offering robustness, hybrid approaches may offer better adaptability in complex scenarios, such as financial time series analysis.

Our Python-based implementation leverages the RQ-Kernel for a customizable architecture, reflecting data-driven dynamics for efficient feature extraction and analysis.

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Discover how an innovative MQL5 tool automates price-action trading. Utilizing a dual Commodity Channel Index (CCI), a 34-period EMA, and ATR, this Expert Advisor (EA) refines trading strategies. The system detects momentum shifts via zero-line CCI crossovers, enhanced by a trend-following EMA filter, and uses ATR for dynamic risk management. Practical features like session filters optimize trades during high-volume times, ensuring precise, rule-driven signals. Traders can adjust parameters to match market conditions, benefiting from refined entry/exit strategies. This comprehensive approach exemplifies robust algorithmic execution, offering a blend of innovation and accuracy for algorithmic trading strategies.

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A newly available script provides a streamlined method to compress OHLC data. This enhances data handling efficiency and allows for optimized storage and retrieval processes. The script is designed to convert conventional time-based data into a more concise format, which could be beneficial for high-frequency data analysis and trading strategies. Handling and analyzing financial data requires precision, and this tool meets these criteria by simplifying data complexity. This advancement in script functionality represents a significant step in improving the workflow for developers and traders dealing with extensive datasets. It's a noteworthy development for those looking to enhance their data processing capabilities.

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Explore the integration of the Python sqlite3 module principles into MQL5 for a streamlined database management experience. This approach simplifies interactions with SQLite databases in MQL5 by mimicking familiar Python functions like `execute()`, `fetchone()`, and `fetchall()`. Gain clarity on handling database connections, executing SQL statements, and managing transactions seamlessly. Learn efficient ways to work with text and binary data, ensuring robust data handling and storage. This guide offers a unique bridge for traders and developers familiar with Python, enhancing algorithmic trading capabilities within MetaTrader 5, while providing a comprehensive understanding of the structural adaptions required in MQL5.

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Explore the process of creating a seamless auto-optimization system for trading strategies with MetaTrader 5. A single optimizing EA navigates tasks by updating statuses in a structured hierarchy: projects, stages, jobs, and tasks, all centralized in a relational database. Implementing SQL triggers allows real-time updates, ensuring efficiency and clarity. Task management follows a meticulous order, optimized for concurrent projects while avoiding redundancy. Developers can gain insights into leveraging genetic optimizations and dynamically managing project workflows. This approach not only automates optimization but fosters smarter, data-driven algorithmic trading, streamlining for effective strategy deployment.

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