Understanding human emotions' dynamics like fear and greed is fundamental in developing algorithms for trading. This newly developed script introduces a class that mathematically models these emotions, as well as other psychological factors like motivation, stress, confidence, and activity. Each of these has a defined range and interacts differently under market conditions.

The model assigns numerical values to each emotion, for instance, fear ranging from -1 to infinity, and greed from 0 to infinity. These values adjust based on market performance, potentially affecting trading decisions when programmed into expert advisors or indicators. Such models are crucial for enhancing decision-making processes in automated trading systems by simulating human psychological responses to market fluctuations.

Additionally, the script offers flexibility in customization, allowing users to adjust...

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In the latest discourse on Category Theory and its application in MQL5, the focus shifts toward understanding the foundational axioms of a category, specifically identity and association, along with the utility of commutative diagrams. Category Theory, a branch of mathematics, is utilized for systemizing and classifying data, proving particularly beneficial in financial time series analysis.

The narrative begins by dissecting what defines a category through the exploration of identity and association axioms, enriched by practical examples and situational analysis. Identity isomorphism and its critical role in ensuring structural preservation within and across domains are detailed, explaining how the morphisms uphold algebraic integrity during data mappings.

The article advances by illustrating the applicative nature of isomorphism within MQL5 environments, showcasing coded examples ...

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The Raymond Cloudy Day indicator integrates an innovative calculation method designed to enhance the accuracy of trend predictions and decision-making in trading. This tool not only identifies potential trend extensions for buy or sell decisions but also marks these as strategic points that can revert to a primary calculated position, aiding traders in plotting their next steps.

Each calculated point in the indicator doubles as a support or resistance line, providing traders clear insights into possible price movements and market dynamics. Additionally, Take Profit (TP) points are designated, which can be utilized to secure profits or as routine strategic points, thereby permitting adaptability in trading approaches.

This indicator is engineered to benefit traders of all levels of experience through its combination of new calculation techniques and sophisticated algorithms, leading ...

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Learn how to program trading robots with our book "MQL5 Programming for Traders". This is the most comprehensive guide available, covering everything you need to create your own trading robots for the MetaTrader 5 platform:

✓ The fundamentals of programming and the necessary tools, gradually advancing to more complex concepts
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Bollinger Bands are renowned in trading due to their efficacy in following trends and spotting potential reversals. Essentially, they consist of an exponential moving average surrounded by two bands spaced at two standard deviations. This configuration helps in identifying overbought or oversold conditions—the usual cue for traders that a price might revert to the mean.

Recent tests challenging the Bollinger Bands' predictive ability by using AI models have surfaced. Utilizing Linear Discriminant Analysis, two AI models were compared; one predicting price direction and the other forecasting movements across Bollinger Band-defined zones. Findings suggest that direct price prediction may be more beneficial than anticipating zone transitions, highlighting the sounds of direct approaches.

Moreover, these insights are reinforced by practical implementations in MQL5, suggesting strategies...

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In a recent investigation into hybrid optimization strategies, a focus was placed on blending various algorithms to leverage their unique capabilities for enhanced problem-solving efficiency. Hybridizing these algorithms involves several methods including integrating their search strategies to utilize their diverse strengths and establishing a sequential or parallel operational structure to facilitate smooth transitions and knowledge sharing.

For instance, the integration of the Bacterial Foraging Optimization with the genetic algorithm illustrates the potential of combining different logic structures to achieve optimal solutions. Furthermore, experimenting with the Grey Wolf Optimizer (GWO) and the Cuckoo Optimization Algorithm (COAm) highlighted innovative approaches. These include dividing iterations, which allows one algorithm to initiate the process and pass on the improved resu...

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In trading, the integration of fundamental and technical analysis is often debated. While some view these methods as mutually exclusive, combining them could potentially offer a balanced trading strategy. This discussion delays the groundwork for utilizing both analyses by leveraging the strengths of each to inform trading decisions.

Fundamental analysis offers insights into market dynamics and economic indicators, providing a deeper understanding of why markets move. By examining factors like GDP growth, interest rates, and political events, traders can anticipate market trends that are not immediately apparent from charts alone.

On the other hand, technical analysis focuses on price movements and trading volumes to identify patterns that can suggest future activity. By applying tools such as the Moving Average Convergence Divergence (MACD) and the Money Flow Index (MFI), traders c...

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A modified Detrended Price Oscillator (DPO) integrates enhanced control over moving average parameters, including period, mode, and applied price. This adjustment allows for the transformation of the moving average curve into a horizontal line, which can serve as a support or resistance level. The calculation formula for this advanced DPO is defined as: DPO = close - MA (mode, price, DPO_period). Here, 'close' refers to the closing price of the bar. The 'mode' specifies the method used for calculating the moving average, which could be Simple Moving Average (SMA), Exponential Moving Average (EMA), Smoothed Moving Average (SMMA), or Linear Weighted Moving Average (LWMA). 'Price' denotes the price point applied in the moving average calculation, and 'DPO_period' is the duration over which the MA is calculated. This modified DPO tool is designed to offer more precise technical analysis c...

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Got a programming question? Ask the community! Visit the largest Forum for algorithmic traders, where developers like you share their expertise and discuss topics related to the creation of robots and indicators:

• Undocumented methods of working with MQL5
• Real-world examples of particular tasks
• Frequent errors and solutions
• Advanced specifics of the MetaTrader 5 platform operation

Join the discussion, share your knowledge and get new experiences.

Go to the thread "MQL5 features, subtleties and tricks"...

In the realm of architectural design, combining the strengths of graph convolution networks and Transformers can yield more effective results than using each technique in isolation. This synergy is exemplified in the recently published algorithm, Graph Transformer Generative Adversarial Model (GTGAN). GTGAN excels in generating realistic architectural layouts from input graphs by merging the advantages of message passing convolutional neural networks (Conv-MPN), a Graph Transformer encoder (GTE), and a generation head. This method successfully illustrates the generation of architecturally coherent house layouts by synthesizing local vertex information and global room relationships.

The GTGAN's innovative approach has demonstrated significant improvements over existing algorithms in both qualitative and quantitative evaluations, proving its capability to handle complex graphically co...

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For developers interested in managing chart objects on MetaTrader 5, particularly when dealing with indicators that do not use buffers, manipulating these objects directly can be crucial. In scenarios where indicators operate solely with objects, modifying these can be quite challenging since iCustom inputs, which facilitate changes in buffer-utilizing indicators, provide no assistance here.

A practical example involves using the "PZ Multidiagonals MT5" indicator, available on the MT5 market. This indicator, primarily used for trend analysis, draws excessive trend lines automatically - an issue if a more streamlined visualization is desired. Before attempting any modifications, installation of the indicator is required.

The process starts with identifying the objects created by the indicator using a unique object prefix. By implementing the LogChartObjectNames() function, develope...

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In the realm of advanced trading platforms, understanding the structural and operational nuances of MQL5 libraries becomes imperative for developing robust trading tools. This post delves into the intricate process of importing and utilizing EX5 libraries within MQL5 code, which is essential for creating sophisticated Expert Advisors (EAs) and other trading scripts.

The procedure commences by applying the directive beneath the section in your MQL5 source code. This directive should include the specific path where the .EX5 library is stored, primarily in the default "MQL5/Libraries" or alongside the algorithm's source code. Once the path and file are accurately specified, the subsequent step involves detailing the exported function prototypes to ensure seamless integration within the user’s trading strategy.

Moreover, handling multiple EX5 libraries simultaneously follows a simila...

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The Detrended Price Oscillator (DPO) provides traders with the ability to identify cycles in price movements, pinpointing the distance between peaks and troughs with greater precision. In this updated version of the DPO, a unique feature has been introduced: the oscillator is represented in blue with an accompanying smoothed histogram. This histogram aids in visualizing the length of the price cycles, simplifying cycle analysis for better strategic planning in trading activities. This innovation offers enhanced clarity and ease of analysis for traders looking to optimize their market timing based on cyclical patterns in price movements.

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Algorithmic trading developers must adapt strategies as market conditions evolve, transitioning from mean-reverting strategies during stable times to trend-following strategies during directional shifts. A challenge arises when a single strategy is applied universally, which often fails to deliver consistent success. Instead, coding multiple strategies into a program allows for manual strategic selections tailored to current market behaviors.

To improve this methodology, developers can design systems that autonomously switch strategies based on quantitative assessments of market conditions. This adaptability can be achieved by implementing transition matrices used to measure market trends and behavior, guiding the strategy selection process.

The concept was pioneered by Andrey Markov, whose Markov Chains help model the randomness seen in market dynamics. By applying transition matri...

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The newly designed .mqh files facilitate the export and import of orders and trades between Expert Advisors (EAs), providing functionality for copying trades either within the same trading terminal or across terminals on the same computer. This system supports trading across different symbols, such as copying a spot market instrument's trades to corresponding futures.

For exporting, the `GRat_OrderExport.mqh` file must be included in your EA. Configuration options include setting Common to true for exporting to another terminal on the same machine, or false for the same terminal. The KeepSeconds parameter dictates the availability duration for exported trades, with a setting of 0 allowing indefinite availability. Implementation requires adding `ExportOrder()` where `OrderSend()` or `CTrade` methods execute orders.

For importing, integrate the `GRat_OrderImport.mqh` file into your EA...

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Transfer entropy is a powerful statistical measure used to determine the amount of information exchanged between time series and to analyze the direction of causal relationships. It is particularly valuable in discerning whether one variable can predict another. This approach leverages the concept of Granger causality, which is based on comparing prediction errors and enhancing model fit by including past values of a potentially causative variable in the regression model.

Significance tests such as p-values and z-scores are employed to ensure the robustness of transfer entropy calculations. These tests involve comparing results from original data with those from shuffled datasets to check if the observed causality is statistically significant or merely coincidental.

Programming-wise, an MQL5 implementation to measure transfer entropy has been described, titled `transfer_entropy.mqh`...

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This article discusses a trading bot that uses real-time sentiment analysis from social media to guide its trading decisions. Integrating MetaTrader 5 (MT5) with a Python-based sentiment analysis tool, the bot showcases a hybrid of quantitative finance and natural language processing. With client-server architecture, it utilizes socket communication to connect MT5's trading functionalities and Python's data parsing capabilities.

The bot analyzes Twitter sentiment concerning specific financial instruments and converts this data into actionable trading signals. This method not only taps into the synergy of diverse technologies in finance but also underscores the significance of alternative data in contemporary trading strategies. The in-depth analysis of the bot focuses on its functionality and code structure, processing social media data, and managing trade executions based on sentim...

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This discussion revisits the topic of how learning rate formats impact Expert Advisor performance, focusing particularly on adaptive learning rates and the one-cycle learning rate. Adaptive learning rates like adaptive gradient, adaptive RMS, adaptive mean exponential, and adaptive delta are explored, each tailoring the learning rate based on the training gradients of specific parameters within the model, thereby optimizing performance by adjusting the learning input precisely.

Furthermore, the article delves into the benefits of using the Sigmoid activation within a specified range for the NZDUSD forex pair in the context of the 2023 daily timeframe. This selective use along with non-batch normalized inputs, although risky, aims to simplify the evaluation of learning rate effectiveness under controlled output conditions. The article recognizes inherent risks in non-normalized data, ...

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Developing an Expert Advisor (EA) requires a nuanced understanding of both algorithms and market indicators. This article highlights two primary methods for integrating indicators into an EA's algorithm. One approach is direct coding of indicator conditions, which enhances efficiency and speed in Strategy Tester operations without separate indicators. The second method involves programming the EA to respond to the indicator's buffer states, which require specific setup within MetaTrader 5 directories to ensure operational functionality.

Challenges arise when attempting to publish an EA based on a custom indicator, as the MQL5 community's validation system may not recognize the custom setup, confining testing and use to personal computers. Preparing an indicator involves organizing buffer sequences and understanding buffer operations, a critical step for seamless EA development. Each ...

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