Discover the new way to harness MetaTrader 5's reporting capabilities with a custom MQL5-based system complemented by Python. This innovative approach enables the automatic generation and distribution of comprehensive trading reports, enhancing informed decision-making for traders. The system integrates programmatically controlled exports to provide detailed insights into trading performance, encompassing metrics like ROI, Sharpe Ratio, and Profit Factor. The integrated use of EAs allows for seamless data handling, transforming manual tasks into efficient, automated processes. With accessible open‑source tools, this solution paves the way for a streamlined workflow, offering traders precision control over their report schedule and content.

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Explore the transformative potential of matrix factorization for trading on MetaTrader 5. This article addresses initial issues in building linear regression models using MQL5 and demonstrates how matrix factorization, notably Singular Value Decomposition (SVD), offers more stable and insightful predictive capabilities. Learn how to implement OpenBLAS to enhance computational efficiency and speed in backtesting, making it a valuable tool for both traders and developers. The focus is on using these techniques to reveal underlying market forces and improve predictions, proving beneficial for developing robust, data-driven trading strategies. Gain insights into applying advanced linear algebra for financial market analysis.

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The RVI crossover indicator enhances analysis by identifying potential market shifts. Based on the Relative Vigor Index, it compares closing price changes to price trading ranges. The crossover occurs when the RVI line crosses the signal line, potentially indicating shifts in momentum. Traders use this indicator to identify possible entry or exit points in trading strategies. This tool requires an understanding of signal line movements and market conditions. Effective use involves integrating it with other indicators for improved accuracy and precision in trading decisions. Understanding of these elements is crucial for optimizing trading strategies.

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In trading, understanding historical price movements is vital for predicting future trends. Price action analysis is key in this regard, focusing on support and resistance levels created from past swings. For effective Boom-and-Crash trading, a systematic methodology to process historical patterns is essential. The "Price Action Analysis Toolkit" offers an approach to convert MetaTrader 5 data into trading signals using machine learning. MQL5's script slices data into JSON payloads, while Python's backend processes it into a feature matrix for model training. This integration ensures a consistent history feed, enabling models to detect price spikes efficiently, crucial for staying ahead in dynamic markets.

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Recent analysis of 3D bars and "yellow" clusters in market data reveals a robust structure prior to trend reversals. This research discovered a significant 97% occurrence of "yellow" clusters within ±3 bars of pivot points and an 82% direction prediction accuracy. These clusters, characterized by a specific mathematical structure and notable in 3D visualizations, exhibit an energy accumulation process that foreshadows reversals. The implementation of a trading system leveraging this pattern resulted in consistent profits during backtesting. The integration of mathematical techniques like tensor analysis has proven crucial, indicating a fundamental property in market microstructure previously undetectable by conventional methods.

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In MetaTrader 5 build 5200, we have significantly expanded support for the OpenBLAS linear algebra library in MQL5, adding nearly thirty new functions. These enhancements provide more capabilities for developing Expert Advisors using machine learning.

In addition, MQL5 now features stronger control to ensure the quality of developed programs. New checks and restrictions in the compiler will help avoid potential errors in the operation of applications.

The desktop platform also introduces automatic interface switching based on your operating system settings – eliminating the need to adjust it manually.

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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.

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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.

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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...

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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.

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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.

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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...

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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...

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