Dive into the world of Receiver Operating Characteristic (ROC) graphs and discover how they enhance classifier performance evaluation. Explore how ROC curves provide critical insights into the trade-offs between true positive rates and false positive rates, particularly in financial datasets with skewed distributions. Learn about the importance of confusion matrices in encapsulating classification outcomes and calculating metrics like sensitivity and specificity. Understand how ROC curves visualize model performance across different thresholds, and leverage the area under the ROC curve (AUC) for simplified performance comparisons. This knowledge is invaluable for MetaTrader 5 developers and algorithmic traders seeking to refine their predictive models.

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Part 11 focuses on implementing a multi-level grid trading system in MQL5. This system strategically places buy and sell orders at various price intervals, profiting from market volatility without predicting direction. The architecture separates signal detection, order execution, and risk management. Key parameters like moving averages guide trade signals, while a basket structure manages trade details like lot sizes and grid spacing.

The implementation involves setting up in MetaEditor, declaring metadata and global variables, using the "CTrade" object for trade execution, and defining functions for basket initialization and position management. The system dynamically adjusts to market conditions, with Moving Averages filtering trade opportunities. The structured plan ensures robust backtesting and trading deployment, leveraging market fluctuatio...

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Seasonal decomposition is a powerful MQL5 tool to dissect time series data, revealing trend, seasonality, and residual components. By isolating these elements, traders gain insights into market behavior and remove seasonal noise for clearer trend analysis. Implementing this in MQL5 involves using moving averages for trend extraction and separating seasonal patterns through additive or multiplicative models. This technique is invaluable for identifying recurrent market patterns, applicable in trading strategy development. Practical applications include analyzing stocks like Apple's, unearthing seasonal trends in intraday, monthly, or long-term data. This method enhances algorithmic trading by informing machine learning-based forecasts and strategies.

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An analysis of indicator line shapes using three distinct formulas reveals consistent patterns across variations in values. Applying the Fast-Slow, Fast/Slow, and (Fast-Slow)/Slow Rice formulas, each method deploys two moving averages within its structure. When reviewing data visually, three distinct indicators on a chart may appear similar in form, but they convey different value sets due to their underlying calculations. Each technique provides unique insights despite their visual congruence, contributing to a more comprehensive understanding of market dynamics for informed decision making in technical analysis.

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Efficient coding practices demand an understanding of passing by value and by reference. The concept of passing by value involves a function receiving a variable copy, ensuring the original data remains unchanged. This is often safer and crucial in scenarios where data integrity is a priority.

In contrast, passing by reference involves providing a function direct access to a variable. While this method is powerful, it carries the risk of unintentional data modifications, often leading to complex debugging challenges.

For programmers, especially those using C or C++ inspired languages like MQL5, mastering these distinctions is key to writing clean, efficient, and error-free code. Prioritize value passing unless modification is absolutely necessary.

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The article explores the Traj-LLM algorithm, designed to enhance trajectory prediction using Large Language Models. Developed initially for autonomous vehicle applications, Traj-LLM harnesses Sparse Contextual Joint Encoding, high-level interaction modeling, and Lane-aware probabilistic learning, ensuring improved prediction accuracy. By utilizing pre-trained LLMs, the model overcomes traditional constraints of feature engineering, providing a robust approach to model temporal dependencies and interactions among traffic elements. The article also discusses implementing Traj-LLM in algorithmic trading using MQL5, highlighting modifications to existing neural network components for improved data processing efficiency and accuracy.

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Enhancements to expert advisors (EAs) for real account trading are essential, focusing on factors beyond acceptable strategy tester results. Key improvements include restoring EA operation post-terminal restart, handling varied trading instrument names, and automating trading upon indicator thresholds.

A robust system involves retrieving necessary initialization strings from a multi-gigabyte database storing optimization results. Only vital information is extracted for efficient EA execution, avoiding full database integration.

Optimization involves three stages: single trading strategy optimization, group parameter selection, and final group selection across symbols and timeframes. Optimizing group sizes and forming a strategy group library entail detailed study. Automation streamlines results entry and library formation in databases, eliminating manual ef...

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Experience the enhanced functionality in MT4 with the Chaikin Oscillator, now featuring a smoothing option. This tool easily integrates into your technical analysis toolkit, offering a smoothed version of the traditional Chaikin Oscillator. It maintains the core purpose of measuring the momentum of the Accumulation/Distribution line over time, providing traders with the ability to better visualize trends and potential divergence.

With this addition, users can eliminate some of the noise that often accompanies volatile market movements, leading to clearer, more actionable insights. This improvement is particularly useful for those requiring precise analysis in dynamic trading environments. The oscillator remains an essential indicator in volume-based analysis, and the smoothing option enhances its utility, particularly for long-term strategy evaluations.

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CCommunicationDialog to create it, giving it m_chatId and m_botToken so it knows about Telegram. After creation, we call m_commPanel->Create() with parameters to place it on the screen.

Once we have a communication panel, we call m_commPanel->Toggle() every time the button is clicked. This shows or hides the panel, letting you interact with it as needed.

This method ties the communication dialog to the home dialog, ensuring it appears and disappears when requested, integrating into the overall system behavior fluidly, and handling user interactions cleanly within the modular structure.

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TrendLoom EA supports traders by generating quick, reliable signals across multiple timeframes without manual switching. Its graphical interface features buttons linked to distinct trading strategies such as Short-Term Focus and Long-Term Trend. Each strategy employs SMAs to determine market signals. By pressing a button, the EA compares the closing price against SMAs, summing signals to issue a BUY, SELL, or NEUTRAL outcome. This aids in confirming overall trends and reducing entry mistakes. Efficient signal updates ensure timely decision-making and enhance analytical precision. The EA's coded framework utilizes MQL5 elements for an interactive yet maintainable interface, streamlining market analysis for traders.

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An indicator displays signals in the main window using Arrow objects based on the iCHO indicator (Chaikin Oscillator, CHO) and its smoothed variant, iCHO Rice. The main feature is the CHO Smoothed Arrow which guides users through visual signal cues. Please remember that indicators in the subwindow are manually added solely for visualization purposes, aiding in interpreting the data. This setup allows for a clear representation of market trends and helps in making informed trading decisions. The combination of these elements can streamline analysis for technical analysts and traders.

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Explore the potential of MetaTrader 5 by leveraging MQL wizard to experiment with simple trading patterns efficiently. By combining the Moving Average with the Stochastic Oscillator, traders can generate high-probability trading signals. Delve into the three machine learning phases: Supervised Learning for model training, Reinforcement Learning for optimizing decision-making, and Inference for applying learned insights to new data. Advanced Python integration with neural networks offers significant efficiency gains, enabling cross-validation and forward testing of predictive models. These methods enhance automated trading strategies, providing traders and developers with robust, data-driven decision-making tools for financial markets.

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The article delves into the innovative adaptation of the Tabu Search algorithm for optimizing continuous search spaces. Initially renowned for efficiently solving combinatorial problems using adaptive memory, this modified version introduces a discretization technique, categorizing search parameters into sectors managed by "white" and "black" lists. This structure enhances adaptive exploration by dynamically adjusting search priorities based on previous successes or failures, thereby preventing redundant cycles and promoting diversification. Practical applications include optimizing complex algorithmic trading strategies, offering developers a robust tool to explore diverse solution spaces without excessive parameter tuning, while ensuring efficiency in finding optimal trading strategies.

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Optimizing algorithmic models demands precision and stability in parameter selection. The complexity increases with the integration of strict parameters from proprietary firms. Developing a Custom Criterion allows for targeted optimization without extensive manual analysis. However, caution is needed to avoid issues like the misuse of return(0) in optimization processes that could lead to discarding viable results.

Adapting principles from Neural Networks, such as Activation Functions, can refine parameter selection by offering structured ways to handle data ranges and improve scoring methods. Functions like Sigmoid and Tanh are particularly beneficial due to their constrained and stable output ranges, preventing issues like exploding or vanishing gradients.

This approach advances the capability to harness genetics-based algorithms for superior optimizatio...

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BreakRevertPro introduces a sophisticated set of features for modern trading environments. It emphasizes statistical analysis using Weibull, Poisson, and Exponential distributions to identify trades. The system is designed for validation with a built-in safety mechanism that adjusts position sizing, particularly for precious metals, ensuring robust performance.

The software supports multi-timeframe analysis, from M1 to H1, providing a thorough market overview. It utilizes dynamic stop loss and take profit validation, ensuring trades align with market conditions. BreakRevertPro automatically detects validation environments, incorporating a highly integrated validator class for broker compliance.

Risk management remains a priority with multiple margin safety checks. The system adapts execution based on real-time market conditions and uses persistent ...

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Dive into the technical realm of algorithmic trading with a detailed exploration of the Force Index indicator. Developed by Alexander Elder, this indicator uses price and volume to reveal market power and potential trend reversals. The article outlines strategies like trend identification and divergence detection, offering a blueprint to create a robust trading system. By leveraging MQL5 in MetaTrader 5, traders can automate these strategies, gaining precise market insight and decision-making capability. Ideal for seasoned developers or those eager to harness the power of algorithmic trading, this guide emphasizes practical application and strategy testing, ensuring it’s both educational and actionable.

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The development of a simple indicator that adds daily vertical lines to charts aids in clearly identifying the start of each trading day. Additionally, it labels each day with its name, allowing for easy recognition of daily patterns and cycles. This functionality is particularly useful for those who require precise daily segmentation in their trading analysis. By providing a clear visual demarcation, traders can better organize their analysis around daily open and close times, enhancing both strategic and operational planning. The incorporation of day labels ensures clarity in monitoring and evaluating daily market behavior, supporting more informed decision-making processes.

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Creating or modifying complex indicators with multiple buffers can be cumbersome. Initial setup involves declaring numerous double arrays, setting up buffers, configuring plot types, and ensuring all elements align correctly. Avoiding errors like 'Array Out Of Range' becomes challenging without careful planning. Handling data across multiple buffers, such as averages, often requires verbose, repetitive code. Strategies to minimize errors include organizing buffers in objects, simplifying data operations, and leveraging object-oriented programming.

Enhancing this approach involves delegating plot configuration to classes and using inheritance to refine data handling. Extending functionality needs flexible class structures to accommodate various plot types, maintaining ease of use and reusability without overwhelming complexity.

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The Assistant indicator is designed for chart analysis by marking price ranges. With the appearance of a rectangle named "Rice", it automatically projects two lines parallel to the rectangle's upper and lower price. These lines offer a visual aid in chart analysis, persisting even if the indicator is subsequently removed. This feature ensures continuity in analysis without disrupting user-set parameters. Attention must be paid to the naming convention, as only rectangles starting with "Rice" are considered. Monitor these lines for uninterrupted analysis even after the indicator's removal. This systematic approach aids in maintaining clear visual boundaries on price movements.

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Dive into the technical intricacies of calculating custom currency indices for algorithmic trading using MetaTrader 5's powerful environment. The article outlines the creation of synthetic instruments like the USDX and EURX through a comprehensive service program. It meticulously details setting up a robust system to continuously update currency indices using latest tick data from a basket of major global currencies. With a focus on practicality, the workflow ensures charts are dynamically updated, providing traders and developers with real-time insights into currency fluctuations. The innovative approach leverages advanced data structures and functional programming within MQL5, enabling the customization of indices with flexible parameters.

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