Binary classification models are useful for predicting whether tomorrow's closing price will be higher than today's. Logit and probit regression, common techniques within supervised learning, provide a foundation for this analysis. These models utilize price patterns and standardized price increments as predictors to form a training dataset. The resulting trained classifiers are implemented within trading algorithms like LogitExpert EA.

The process begins with data preparation, where features are defined, standardized, and structured for optimal parameter estimation. Maximum likelihood estimation, often combined with methods like L-BFGS optimization and L2 regularization, helps minimize the loss function, mitigating overfitting risks.

Once parameters and covariance matrices are estimated, prediction occurs, generating buy or sell signals. These signals determine ...

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Explore the latest insights into refining the MetaTrader 5 control indicator system. Delve into optimization challenges and improve code efficiency by refining object handling through the C_DrawImage class. Leverage embedded resources to reduce code complexity and enhance long-term usability by passing pointers wisely. Uncover practical improvements for handling bitmap images, ensuring robust initialization procedures, and adopting memory-efficient practices. Boost the sustainability of trading applications with safer inheritance structures, allowing seamless extensions. Understand the intricacies of utilizing standard MQL5 library functions, optimizing processes without compromising on performance. Aimed at traders and developers eager to advance their algorithmic trading strategy development skills.

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A robust Stochastic Crossover Strategy efficiently enters trades based on %K and %D line movements. Users can adjust periods for %K, %D, and slowing to align with their strategy. The Entry Cooldown feature minimizes impulsive entries and whipsaws, with the default cooldown preventing new trades shortly after the last one.

Standardized risk measures include adjustable Stop Loss and Take Profit settings, currently set at 300 points each. Trades use a fixed lot size of 0.1 for simplified risk management. The strategy incorporates automatic position closure upon opposing signals and prevents duplicate positions in the same direction. By leveraging the Trade.mqh library, execution is seamless.

Users can customize various input parameters related to risk and the Stochastic calculations. It's imperative to place the EA file correctly within the MT5 platfo...

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The combination of the DeMarker indicator with Envelopes in Python provides a strategic edge in market analysis. By converting these MQL5 indicators into Python, leveraging libraries like MetaTrader 5 and pandas, traders can seamlessly access price data and implement technical strategies. One approach involves constructing custom functions for each indicator, optimizing speed and module dependency. This method allows for the creation of robust trading systems that benefit from reduced computational overhead.

The DeMarker indicator measures momentum and provides insights into asset overbought or oversold conditions. With Python, implementing features like DeMax and DeMin offers enhanced modularity and reusability in technical analysis. Price fluctuations over specified periods reveal potential market trends, with values normalized to facilitate straightforward inte...

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The enhancement of the Trade Assistant Tool in MetaQuotes Language 5 (MQL5) introduces dynamic visual feedback mechanisms for MetaTrader 5, optimizing pending order placement efficiency. Enhancements include a draggable control panel, intuitive hover effects, and real-time order validation capabilities. These features improve interface interaction, reduce navigation errors, and ensure order precision by logically aligning entry, stop-loss, and take-profit levels with current market prices.

Implementation in MQL5 involves defining panel objects and user interaction variables. Advanced functions such as "isOrderValid" and "updateRectangleColors" are incorporated to ensure trade alignment and visual feedback accuracy. The "updateButtonHoverState" manages interactivity, enhancing user experience through clear button and element feedbacks.

Enhanced OnChartEve...

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Optimizing code for algorithmic trading in MetaTrader 5 enhances back-test accuracy and execution. Key improvements include modular code design, efficient use of technical indicators, and an event-driven execution model. These strategies reduce computational load and prevent false signals, improving the Expert Advisor's performance under heavy back-test conditions. Integrating RSI and MACD indicators with candlestick patterns aligns trade decisions with broader market trends, improving consistency in back-tests. Further enhancements suggest dynamic SL/TP adjustments and multi-timeframe analysis to refine trade accuracy and robustness. This comprehensive approach creates a disciplined, context-aware trading system.

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Reversing candle patterns are used to identify potential trend reversals in charts. Start by setting the input for the sequence of bulls and bears. Determine how many consecutive bullish candles are required before a bearish pattern can indicate a reversal, or vice versa. Configure the Stop Loss (SL) and Take Profit (TP) levels to manage risk and maximize potential gains. Ensure the input of row mode instructions allows for flexibility in pattern recognition. Implement these setups within a full Expert environment to automate the identification. This can improve efficiency in tracking potential trade opportunities based on trend reversals and help in making data-driven trading decisions.

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Custom indicators can transform a MetaTrader 5 Expert Advisor from a rigid tool into a dynamic trading assistant. By planning and coding effectively, developers can access custom data like the VWAP directly within an EA. This approach requires naming custom indicators correctly and using functions like iCustom and CopyBuffer to integrate them into the EA. This allows for tailored trading strategies beyond standard indicators. Developers can program indicators with parameters, like moving averages, and enable the EA to handle multiple scenarios, leveraging MetaTrader 5's capabilities. This flexibility paves the way for more refined and powerful algorithmic trading systems.

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A newly developed Expert Advisor offers enhanced risk management for traders by adjusting position sizes in response to market volatility fluctuations measured by the Average True Range (ATR) indicator. It maintains consistent risk exposure regardless of market dynamics. Essential features include dynamic position sizing based on user-defined risk percentages and ATR values. An optional ATR-based stop-loss adapts to volatility. The EA executes trades using a moving average crossover strategy while prioritizing risk control.

To employ the EA, attach it to the desired chart in MetaTrader 5, configure the risk percentage, ATR period, multiplier, and stop-loss settings. It calculates position size and places trades on moving average signals. By maintaining uniform risk exposure and protecting against large losses during volatile periods, this EA is customizable...

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Explore the world of decision trees, a supervised machine learning technique aiding data categorization and prediction. Decision trees utilize algorithms like ID3 to split nodes and maximize data homogeneity. By determining features that yield the highest information gain, they expertly separate data to enhance decision-making. Key concepts include entropy, a measure of data uncertainty, and information gain, which assesses how well features classify target classes. Through step-by-step processes, decision trees illuminate intricate data patterns, offering practical solutions for developers in algorithmic trading. Master these tools in MQL5 to innovate and solve complex trading challenges efficiently.

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Starting July 1, 2025, the minimum supported versions of the trading platforms will be:

• MetaTrader 4 — Build 1440 released on February 21, 2025
• MetaTrader 5 — Build 4755 released on December 13, 2024

After this date, older versions of desktop terminals will no longer be able to connect to broker servers.

Over the past few months, three major updates have been released for MetaTrader 5:

• Build 4620: Extended OpenBLAS support, fixed tick history queries.
• Build 4730: Extended OpenBLAS support and general performance optimization.
• Build 4755: Strategy Tester bug fixes and general improvements.

Each MetaTrader 4 version comes with performance improvements and bug fixes.

Download the latest version of the platform and access all the new features

David Varadi's work offers insightful perspectives on quantitative analysis and financial modeling. He is recognized for pioneering strategies and methods that have influenced traders and analysts globally. His blog is a resource for those looking to understand market dynamics through a data-driven lens. It includes discussions about market anomalies, algorithmic trading techniques, and advanced statistical methods. Professionals interested in enhancing their analytical skills might find his writings beneficial. The blog emphasizes practical applications and deep analysis, making it a valuable tool for anyone in the field of finance and trading technology.

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#MQL4 #MT4 #Algorithm

Explore how to create a custom formatting function to display binary values using MQL5 in MetaTrader 5. The article provides a step-by-step guide on implementing a solution that compensates for the absence of built-in string formatting capabilities for binary representation. Gain insights into using loops, and the shift operator to manipulate data, offering practical applications for traders and developers. It also delves into the concept of pangrams and anagrams, exploring how these can be applied for creating stronger passwords. Unveil the potential of programming with a creative, problem-solving approach, enhancing your arsenal for algorithmic trading.

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The African Buffalo Optimization (ABO) algorithm, introduced in 2015, is a metaheuristic strategy inspired by the social behaviors and adaptive capabilities of African buffaloes. The algorithm incorporates key buffalo behaviors such as group communication and learning, adapting these principles for optimization processes. It starts by initializing a population of buffaloes, with each buffalo acting as a potential solution. Solutions are evaluated using fitness functions, and positions are updated based on signals "maaa" for exploitation and "waaa" for exploration, guided by specific equations.

The core components of the ABO algorithm are the S_Buffalo structure and C_AO_ABO class. S_Buffalo represents each buffalo as an agent with a movement vector, and allows parameter adjustments through its constructor and SetParams method. The C_AO_ABO class m...

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The recent advancements in point cloud processing are exemplified by the development of the Mask-Attention-Free Transformer (MATF). The method reframes traditional Transformer-based approaches by eliminating the mask attention design. Instead, it incorporates an auxiliary center regression task to enhance the convergence speed and accuracy of object segmentation. This novel approach effectively uses positional queries and contextual relative position encoding in the cross-attention mechanism, addressing the challenges of slow convergence and poor initial mask quality. The MATF approach shows superior performance across various datasets and effectively reduces training complexity while maintaining flexibility and robustness in 3D instance segmentation.

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Discover comprehensive details about MetaTrader 4 build 2140 and MetaTrader 5 build 3802. Staying updated with the latest builds ensures access to new features and platform security enhancements. System stability has been prioritized with optimized performance metrics. Important attention to bug fixes and improved interface adaptability for a seamless user experience. Developers and traders should review changelogs for essential updates impacting algorithmic trading scripts and indicators integration. Recognizing the necessity of maintaining updated programming environments is crucial for enduring operational productivity and achieving effective trading results.

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This text lays out the framework for developing a custom logging system designed to enhance the debugging and profiling of trading algorithms in MQL5. Traditional methods like using Print() lack robustness, failing in larger projects due to their inability to manage log severity, context, outputs, and filters. This makes custom toolkits invaluable.

The proposed framework begins with building a logging system composed of LogLevels to classify severity, an ILogHandler interface to manage different outputs, and a singleton CLogger orchestrator. These components work together to improve message clarity, filtering, and contextual information.

Implementing specific log handlers, such as ConsoleLogHandler and FileLogHandler, allows for flexible logging solutions like writing logs to console or files. These handlers provide customization options for log severity lev...

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In the automation of the Grid-Mart Scalping Strategy using MetaQuotes Language 5 (MQL5), the development of an Expert Advisor focuses on executing grid-based Martingale trades. This task involves creating a dynamic dashboard for real-time monitoring. The strategy utilizes a grid-based Martingale approach to capture market fluctuations, requiring precise configuration and risk management. Implementation involves declaring global variables, managing trade execution using the CTrade object, and defining inputs for grid intervals and lot scaling. Risk controls include drawdown limits and grid size restrictions. The dashboard visualizes trading metrics with interactive elements, providing an intuitive user interface for monitoring and decision-making.

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The intersection of financial news and AI presents significant trading opportunities. Begin by gathering comprehensive news data, ensuring factors like timeframe, symbols, and handling NaN values are considered. Utilize MetaTrader 5 for collecting relevant data, including OHLC values around news events.

Prepare the dataset by cleaning and structuring it, focus on encoding categorical variables to support machine learning processes. Splitting data into training and testing sets is crucial. LightGBM emerges as an efficient choice for modeling due to its decision tree base, handling both categorical data and bias effectively.

The model shows strong predictive capabilities. Use tools like SHAP to analyze feature impacts. Export the model to ONNX format for practical deployment. Combining AI models with news can enhance algorithmic trading strategies but...

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#MQL5 #MT5 #Trading

Explore the intriguing Artificial Ecosystem-based Optimization (AEO) algorithm, inspired by natural ecosystems and their intricate interactions. AEO mimics ecosystems with a diverse population of solutions, each adapting to its niche, using energy transfer through simulated agents like "herbivores", "carnivores", and "omnivores". This method optimizes solution quality by updating decisions through competition and cooperation strategies. It balances exploration and exploitation by incorporating stochastic and deterministic elements, utilizing techniques such as Gaussian and Levy distributions. Perfect for algorithmic traders and developers, AEO provides novel techniques for solving complex optimization problems with practical applications in trading systems.

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