Introducing a revised Typical Unassuming Volumes indicator which now includes an option to divide volume by spread. This update simplifies the indicator by removing the previously incorporated color-changing feature, which became redundant after the modification. Further enhancing its utility, we've added the functionality to toggle the use of 'Spread' according to user preferences. Primarily designed for those involved in EA building and seeking new avenues for optimization, this tool could add a unique dimension to trading strategy development. Its efficacy is yet to be fully determined, opening a field for experimentation and analysis by developers and strategists.

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In the evolving world of algorithmic trading, integrating external data through APIs is revolutionizing how traders make decisions and develop strategies. Leveraging APIs, traders gain access to a diverse array of data sources including real-time market information, economic indicators beyond traditional calendars, and advanced analytics like sentiment analysis through natural language processing.

For developers and traders using platforms like MetaTrader, APIs provide an avenue to enhance the functionality of trading systems. Developers can access comprehensive market data through APIs to use advanced technical indicators and sophisticated charting tools, improving the accuracy of trading signals. This integration not only extends the capabilities of trading platforms but also adds layers of valuable market intelligence, supporting more informed decision-making and dynamic strategy ...

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In this detailed exploration of the "angle of attack" concept adapted for financial trading, the analysis begins with the traditional approach of identifying the trajectory of a moving average to depict the strength and direction of a market trend. This method calculates the slope 'm' from two selected points and, if needed, converts the value into degrees using the arc-tangent function.

However, a significant issue arises due to the volatility of price scales and the potential for misleading results across different trading intervals. For instance, different forex pairs exhibit vast discrepancies in angle measurements, like a steep 45 degrees for a yen pair versus a near-flat 0.57 degrees for GBPUSD, despite similar market movements.

To address these inconsistencies, a novel method is proposed: normalizing the time and price scales to uniform units, thereby harmonizing the dimensio...

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In a recent delve into AI advancements for time series predictions, PatchTSt emerged as a notable algorithm on Huggingface.co. This implementation, excelling in rapid training and ease of use with MQL, is tailored for time series analysis using transformers. Central to PatchTST's functionality is its use of 'patches' which enhances pattern recognition in data like open, high, low, and close prices.

A key feature includes minimal data-preprocessing requirements, facilitated by a technique named 'RevIn' or reverse instance normalization, contributing significantly to its efficiency in handling distribution shifts within time series forecasting. This makes PatchTST highly suitable for algorithmic trading environments where market conditions are frequently volatile.

For technical specifics, PatchTST employs a transformer architecture that segments data into patches, incorporating positi...

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Principal Component Analysis (PCA) is often associated with dimensionality reduction but its utility in discerning hidden relationships in data is profound. The key elements, eigenvalues and eigenvectors, help identify underlying relationships that contribute to understanding complex data patterns. Applying these concepts, the article discusses factor analysis, which assists in identifying latent variables, thereby revealing redundancies and interrelations among variables which otherwise appear independent.

Factor analysis differs from PCA; it helps identify underlying factors influencing observed variables, unlike PCA which transforms a large set of variables into a lesser number of uncorrelated variables capturing maximum variance. This is achieved without necessarily reducing dimensionality but by identifying influences on observed variables through eigenstructure analysis.

Furth...

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A new tool is available for traders who need immediate visibility of their potential financial outcomes in their deposit currency. This indicator, originally developed for the MQL5 community, assists by showing the value of the defined stop loss and the take profit settings directly in terms of deposit currency. While it offers quick approximations, users should consider that it provides estimates that do not include brokerage commissions. This makes it an essential, yet basic, tool for traders in strategizing their positions without the need for complex manual calculations.

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The Fibonacci Retracement Indicator is an advanced technical tool that incorporates the principles of the ZigZag indicator to enhance its utility in identifying crucial support and resistance levels in financial markets. This tool, originally developed for Metatrader 4, automates the integration of ZigZag extremes with Fibonacci gridlines, thereby facilitating more precise trading decisions based on key retracements.

For developers interested in integrating similar functionalities or enhancing their financial analysis tools, the underlying logic can be adapted from existing code found at a prominent financial technology site. This offers an opportunity for technical refinement and customization in various trading platforms.

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The Metatrader 4 indicator for Linear Regression Value is an adaptation from the original Metatrader 5 version. This tool is designed to assist traders in gauging the linear regression value, thereby providing insights into market trends and potential price directions.

For those using Metatrader 4 platforms, this indicator can be a significant addition to their trading toolkit by enabling enhanced analysis of market dynamics. Further details about the usage and features of this indicator can be found in the original detailed post on the Linear Regression Value for Metatrader 5.

Traders are encouraged to review the original post to fully understand the capabilities and implementation of this analytical tool in their trading strategies.

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This article presents a comprehensive guide on implementing the Cascade Order Trading Strategy using Forex Trading Expert Advisor (EA) in MetaQuotes Language 5 (MQL5) for MetaTrader 5. It highlights the use of moving average crossovers to automate trading decisions on the MetaTrader 5 platform, which takes advantage of the Trade.mqh library for effective order management.

The strategy employs two exponential moving averages (EMA) to generate buy or sell signals based on their crossover direction. Detailed steps include setting orders with specified take profit and stop loss, adjusted dynamically according to market conditions. Furthermore, the script intelligently identifies new bars, ensuring that decisions are based on completed candle formations, and adjusts holdings when profit targets are reached.

The article also details the use of MQL5's CTrade class for streamlined order p...

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Exploring the integration of MetaTrader 5 with Python, C++, and Java opens new horizons in algorithmic trading and financial analysis. This project showcases a CGI character, "Price Man," whose size alters based on real-time market data, reflecting the prevalent market emotions such as optimism and fear. This creative approach uses CGI animations to depict market dynamics in an intuitive manner, making it accessible for various audiences.

The backbone of this concept involves key technologies and tools: MetaTrader 5 for extracting price data, and Python for data manipulation. Exporting price data involves a straightforward process in MetaTrader 5, where data is extracted and normalized using Python scripts to ensure uniformity and precision in analyses. Tools like Pandas play a crucial role here, facilitating efficient data handling and transformations necessary for subsequent visual...

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In the latest development for MetaQuotes Language 5 (MQL5) users, significant advancements have been made in GUI panel automation enhancing interaction and dynamism. Moving forward from a static setup, the article discusses transitioning to a responsive and interactive panel that accommodates live updates and mobility of components. Key enhancements include relative positioning and flexible layouts, real-time data integrations, and interactive features such as clickable and draggable elements.

The implementation reviews start with the closure of the panel upon interaction with a designated button and extend to dynamic updating of trading volumes and price quotes in real-time. Additionally, components now react to mouse hover effects, modifying visibility as an interaction cue. These improvements aim to refine user experience by ensuring the interface is not only visually engaging but...

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The new Risk/Reward Box Indicator automatically generates a visual tool on any open chart, utilizing historical candle high and low values. Originating from a reliable source, this tool has been adapted for ease of use. Users can adjust the box size and reposition it to match specific trading strategies and price targets. This allows traders to better assess potential risks and gains on trades, enhancing decision-making efficiency in active markets. A useful addition for traders looking for precise control and analysis in their trading environment.

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Machine learning in financial markets heavily relies on the quality of training data to predict market behavior accurately. Sophisticated tools often struggle due to noisy data masking valuable information. The quality of indicators, acting as data purveyors, significantly impacts model performance. Timothy Masters' book, Testing and Tuning Market Trading Systems, discusses using entropy to evaluate the informational content of indicators.

Entropy measures the unpredictability in the data, providing insight into its usefulness for model training. By employing entropy, strategy developers can objectively assess which indicators bring the most value and which might mislead due to insufficient information content. Such methodologies uphold the importance of data cleanliness and relevance in developing effective market strategies using machine learning technologies.

Understanding entr...

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Understanding the nuances of MT4 and MT5 trading platforms can significantly enhance your trading performance. MT4 suits forex specialists seeking robustness and simplicity, while MT5 offers advanced functionalities for assets beyond forex, accommodating strategies that involve futures or stocks. Both platforms support automated trading but remember, selecting the right platform aligns with specific trading goals and the market being tackled. Analyze your trading needs to choose effectively between MT4 and MT5.

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In Python programming, utility classes such as Utils play a significant role by providing reusable functions without needing to instantiate the class itself. One classic utility in technical computing involves handling matrices—essential data structures in machine learning, physics simulations, and more. Python's standard libraries offer foundational methods for matrix manipulation, including initialization, transformation, and other operations. However, there's always scope to expand these libraries to include additional functionalities tailored to specific application needs.

For developers handling data files and matrices, the ability to read matrices from CSV files is crucial. This function streamlines the import of data, converting CSV formatted data directly into matrices which can then be manipulated within Python. Similarly, encoding values directly from CSV files enhances fun...

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Introducing two cutting-edge portfolio optimization programs designed to enhance trading strategies and maximize returns while curbing risks. The first tool, based on Python, integrates seamlessly with MetaTrader 5 and utilizes advanced libraries including pandas, Numpy, and cvxpy for data analysis, asset allocation optimization, and visualization via Matplotlib. The second tool employs MQL5 and capitalizes on the native capabilities of MetaTrader 5, facilitating a seamless operational experience within the trading platform.

These tools are crucial in modern financial management, enabling a scientific approach to portfolio construction that mitigates human biases and adjusts to market dynamics effectively. Both programs utilize complex algorithms to examine vast datasets, analyze market trends, and explore asset correlations, thus delivering data-informed strategies that conform to v...

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Introspective Sort, also known as Introsort, is a hybrid sorting algorithm renowned for its efficiency and is implemented as a standard in many modern software libraries. Introsort incorporates the methodologies of three distinct sorting algorithms: Quicksort, Heapsort, and Insertion Sort, each selected based on specific conditions during the sorting process.

Quicksort operates on the divide and conquer principle, using a pivot to partition the array and achieving average time complexity of O(n log n). Heapsort, leveraging a binary heap structure, also manages average and worst-case scenarios in O(n log n) time but differs as it is an unstable sorting method. Insertion Sort, simplest of the three, optimally sorts small datasets or final pieces of larger collections with a time complexity ranging up to O(n^2) in the worst case.

The Introsort algorithm begins with Quicksover and shift...

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MQL5 Cloud Network has reached 16 billion completed tasks. Algo traders use it every day to test their strategies faster.

We have upgraded it for even higher performance:

• 7 upgraded servers that manage connected agents and distribute tasks more efficiently
• 60 Gbps increased network bandwidth for fast data transfer between algo traders and agents
• Improved server "hot cache" with historical data for quicker data distribution
• No more slow agents from virtual environments to make sure you always get the best CPUs for your tasks

Try cloud calculations and spend your time developing strategies instead of waiting for test results.

We also invite you to join the network. Daily activities like browsing, office work or watching videos require a tiny portion of your PC power, leaving the rest unused. Why not share it and earn? Just download the Strategy Tester Manager and run a quick setup.

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Understanding the complexities of fish schooling behavior has led to the development of the Fish School Search (FSS) algorithm, a notable contribution to the field of swarm intelligence. Introduced by Bastos Filho and Lima Neto in 2008, the FSS is designed primarily for continuous optimization problems in multimodal search spaces and focuses on mimicking the collective movement patterns observed in fish schools.

The algorithm features distinct components such as the feeding and swimming operators, which handle the simulation of feeding success and coordinated school movement. The feeding mechanism encourages fish to move towards regions of higher yield, impacting their 'weight', which in turn influences the school's collective movement. The swimming operators divided into individual, instinctive-collective, and collective-volitional movements, facilitate exploration and exploitation ...

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