The WPR crossover indicator generates signals based on the Williams Percent Range, which is useful for identifying potential entry and exit points in trading. This tool analyzes WPR values to determine when crossing the threshold signals a shift in market momentum. It detects overbought and oversold conditions by evaluating the percentage range against predefined levels. Practical for both beginners and experienced traders, this indicator aids in developing well-informed trading strategies. In implementation, it's crucial to combine with additional analysis for more accurate results as standalone indicators may not account for all market variables. Suitable for integration in various trading platforms.

👉 Read | AlgoBook | @mql5dev

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Discover how an innovative approach transforms Forex portfolio optimization by merging Markowitz's theory with the VaR methodology. Traditional Markowitz optimization struggles with Forex due to currency pair correlations, necessitating an adaptation. By integrating VaR constraints, the system dynamically controls risk through diverse market conditions. Leveraging Python with MetaTrader 5, the model accommodates the idiosyncrasies of Forex volatility and correlation, enhancing decision-making for position sizing. The cohesive use of historical, parametric, and Monte Carlo methods ensures a robust VaR calculation, pivotal to pristine risk assessment, ultimately refining trade execution by visually augmented analysis and comprehensive backtesting strategies.

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Introducing the ADX Crossover Indicator, designed to generate precise buy and sell signals. This tool utilizes the Average Directional Index (ADX) to evaluate the strength of a trend, aiding in strategic decision-making. The indicator identifies crossovers between the ADX line and its signal threshold to mark potential entry and exit points in trading. By assessing market momentum, it provides a clear framework to gauge trend reliability. This tool is suited for traders looking to enhance their technical analysis by incorporating trend strength validation into their trading strategies, offering a structured approach to optimize trading performance.

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In this installment, we enhance our MetaTrader 5 trading dashboard with an innovative Informational Dashboard. Designed for efficient performance tracking, it consolidates multi-symbol positions, trade counts, and vital account metrics like balance and equity into a single, sortable interface. Key features include customizable columns, real-time data updates, and CSV export, focusing on optimizing workflow and swift problem identification like excessive drawdowns or imbalanced positions. Our implementation in MQL5, using structured data handling and modular design, ensures a lightweight, responsive system suitable for live trading. Ideal for developers and traders seeking improved oversight and seamless customization options.

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Neural networks are often perceived as complex systems. However, at its core, a neural network is a structured composition of functions. Each layer consists of a linear transformation followed by a nonlinear activation function. Multilayer Perceptrons (MLP) are among the simplest neural networks, capable of performing approximation and classification tasks by transforming input data through nonlinear functions.

These MLPs are particularly useful in trading systems, where they can convert raw market data into actionable trading signals. Understanding neural network architecture allows us to write the operation in an analytical form, with activation functions in neurons serving as nonlinear transformers processing the data.

The capability of the MLP as a universal approximator means it can be integrated into trading systems as an independent component...

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A new system, MultiStrategyEA, combines the code of seven distinct experts into one: AC_Expert, ADX_Expert, AO_Expert, DeM_Expert, ForceBB_Expert, MFI_Expert, and MS_Expert. This integrated solution offers numerous adjustable parameters to tailor to various investment profiles. It is capable of operating on 28 different currency pairs, with one pair per chart. Default parameter settings are indicative and may not suit all users. Individuals are encouraged to experiment with the available settings to customize their trading strategy effectively. This flexibility allows users to adapt the system to their specific trading preferences, potentially enhancing their overall trading experience.

👉 Read | Signals | @mql5dev

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Implementing multiple indicators on a single chart without clutter requires dividing the system into two files using Object-Oriented Programming for sustainability. Starting with a custom indicator, we create a subwindow without extra logic. The indicator's header setup prevents tracking irrelevant data types and creates a subwindow on a symbol chart. MQL5's similarity to C++ allows streamlined coding with directives for including header files. We set default values for indicator commands, enabling easier initialization.

Key functions in the custom indicator involve 'SetBase' for displaying data objects and 'decode' for processing commands. Controls to add new indicators ensure symbols are present in Market Watch. 'Resize' maintains data within subwindow boundaries. Private variables enhance data security while only exposing necessary methods to external a...

👉 Read | Calendar | @mql5dev

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Automating the process of capturing screenshots within a trading environment can provide significant insights. Implementing a function within your Expert Advisor (EA) to automatically take a screenshot when a trade is placed allows traders to review exact market conditions at that time. By integrating this feature, traders can capture and store relevant chart images for future analysis. This becomes highly beneficial for post-trade reviews, allowing for a better understanding of the context in which trading decisions were made. This approach enhances accountability and provides a visual reference to the conditions considered during trade execution. It ensures comprehensive documentation and assists in refining trading strategies over time.

👉 Read | Forum | @mql5dev

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Incorporating the Average Directional Index (ADX) into trading strategies requires understanding its calculation and application. Developed by Welles Wilder, the ADX measures trend strength, distinguishing between trending and non-trending periods.

Begin by manually calculating +DM and -DM for directional movements, followed by determining the True Range (TR). Summarize these over 14 periods to compute +DI and -DI. The subsequent steps involve deriving the ADX through averaging the directional index (DX).

Once calculated, implement the ADX using MetaTrader 5 by selecting "Average Directional Movement Index" under the indicators section. Tailor strategies around ADX trends, considering value movements and DI comparisons for decision-making. This integration fosters a structured trading system.

👉 Read | AppStore | @mql5dev

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An Expert Advisor utilizes a straightforward MACD strategy, integrating multiple technical conditions to assess performance in live trading scenarios. Key components include a standard MACD crossover strategy with parameters: Fast EMA set at 12, Slow EMA at 26, and a Signal line using a 200-period moving average for trend filtering. Trading is contingent on price position relative to this moving average, allowing buys above and sells below it.

Additionally, this strategy employs price action by requiring a touch of support levels for buy trades and a breach of resistance for sells, facilitated by a custom Box.mq4 indicator. Subjectivity in support and resistance levels is acknowledged.

Risk management involves placing stop-loss positions relative to moving averages, determined by the SLPointDistanceFromMA parameter, with a take profit target at 1.5 times the s...

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Introducing a tool optimized for precise market monitoring by displaying server time, not local time. This functionality provides an accurate frame of reference essential for traders. The tool allows custom text color and font size adjustments, enhancing seamless integration with existing chart setups. A feature for optional daily symbol change visualization, displayed as a percentage, is included for those requiring detailed data insights.

The tool is engineered for minimal CPU usage, maintaining system performance without compromise. Input parameters such as enabling or disabling daily change display, font size adjustments, and text color preferences ensure tailored user experiences. This solution is fitting for traders focused on reliable server time tracking, offering a clean, efficient design that supports trading activities without unnecessa...

👉 Read | AlgoBook | @mql5dev

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An Expert Advisor has been structured around a MACD crossover strategy, complemented by trend filtering and support/resistance verification. This is developed via common interpretations utilizing standard indicators. The codebase is comprehensive, highlighting a step-by-step commentary for clear understanding.

Key elements entail MACD Crossovers with standard settings (Fast: 12, Slow: 26, Signal: 9) to identify momentum changes. A 200-period moving average acts as a trend filter: purchases occur only when prices are above and sales when below this average.

Additionally, trades are conditional on price interactions with key support or resistance levels, identified via a custom SupportResistance indicator, referencing relative highs/lows over the past 10 and 20 candles.

To retain signal validity, a configurable window is employed (SignalValidity). For ris...

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In this technical walkthrough, a robust pipeline is developed for integrating MQL5 and Python to advance market analysis. The process initiates with historical data extraction from MetaTrader 5 into Python, followed by storage in Parquet format for subsequent model training. Crucially, this pipeline transitions from basic data ingestion to sophisticated predictive model training using Python's machine-learning libraries, ensuring models are packaged and accessible via a lightweight REST API in real-time.

In this setup, MQL5 handles real-time market data collection and execution, while Python performs feature engineering and model predictions. The modular Python service supports periodic model retraining, exploiting frameworks like scikit-learn for classical techniques or TensorFlow for deep learning. This architecture facilitates dynamic interaction betw...

👉 Read | AlgoBook | @mql5dev

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Explore cutting-edge strategies for MetaTrader 5 with our latest insights into signal patterns using the Gator and Accumulation/Distribution oscillators. Pattern-5 leverages the Gator's trend signals and verifies them with volume insights from the AD line, helping to navigate false breakouts. Pattern-6 reveals latent accumulation through volume signals during range-bound markets, predicting breakout direction with accuracy. Pattern-7 offers robust trend continuation signals by identifying hidden volume divergences. These methodologies integrate volume data with price action for enhanced decision-making, offering paramount insight for developers in crafting profitable algorithmic trading strategies.

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#MQL5 #MT5 #Algorithm

Oma Ally's BBMA strategy focuses on combining Bollinger Bands and moving averages to forecast potential market movements. Widely adopted in Malaysia, Indonesia, and Brunei, it serves as a robust tool in the forex community. The strategy offers a "Reentry" setup aimed at capturing market entries post-correction, aligning with the main trend to outline precise entry and exit points. Within the BBMA framework, the "Zero Loss Zone" is crucial for highlighting areas with minimized risk. This concept emphasizes strong trend candles and strategic moving average positioning, allowing traders to undertake positions with reduced loss risk. Its structured criteria make it a significant strategy for forex traders seeking accuracy and safety in their trading activities.

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This indicator provides a detailed breakdown of trading volume into buyer and seller categories. The visual representation is achieved through overlaying histograms, which clearly display the volume pressure. This tool was initially a component of the Amibroker trading software and has been actively discussed within trading forums. Initially intended for conversion to mql4, this tool has been transitioned to mql5, aligning with modern platform capabilities while retaining the essence of the original version. Credit is given to Nick Molchanoff for the initial conceptual design. This adaptation aims to provide a more comprehensive market analysis for users.

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Matrix factorization in MQL5 offers significant power via its API, providing crucial tools for machine learning in trading. Understanding linear algebra is essential to effectively use this API. A systematic introduction to these mathematical principles allows developers to create more precise and efficient trading algorithms, enhancing model performance.

By coding with linear algebra in mind, developers reduce complexity and improve maintainability. Efficient code structure reduces processing time and increases scalability. The application of matrix operations instead of loops minimizes error accumulation and memory strain.

Linear algebra aids traders in effectively processing large data sets while minimizing execution time, allowing for ongoing model refinement and optimization. Precision and proper utilization of defined methods are key to ...

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The CTradingButtons class, integral to the News Headline Expert Advisor (EA), offers a user-friendly, chart-based interface for manual trading. It provides rapid trade execution and position management during news events and scalping. The class encompasses key segments: class declaration, initialization, button creation, and event handling.

Class Declaration: This section establishes CTradingButtons with private and public members for managing the button panel and trade operations, offering a modular foundation with configurable risk parameters.

Initialization (Init): This function sets up trading and GUI components, distinguishing manual trades with a unique magic number and ensuring interface readiness upon EA loading.

Button Creation (CreateButtons): It constructs and arranges eight critical trade execution buttons, facilitating immediate interaction, un...

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A method for analyzing historical equity through one-time open virtual positions assists in evaluating spread and equity trading performance. The main goal is to determine volatility-flattening constructs and recognize entry and exit points for equity trades. Volume and direction are fixed per active pair, with cumulative profit or loss calculated, then represented as an equity line. Implementation involves setting volumes, directions, and pair activations, with optional initial balances. Note that the calculations disregard spread, commission, and swaps. The current version limits symbol counts and may lack optimization. This model serves as an initial experiment to refine profit extraction techniques from spread/equity trading. Future steps include enhancing visual analysis using indicators like Bollinger Bands and developing a customizable dashbo...

👉 Read | Signals | @mql5dev

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