Discover the power to manage multiple trades with ease with MoveStoploss. This robust tool is the key to order placements and dynamic strategy adjustments on various trading platforms. Supervise any manual trade, set preferences on tailing distance as stipulated or commanded by the Expert Advisor (EA), and cater to fluctuating market scenarios effectively.

Applicable to currencies, commodities, cryptocurrency, and stock trades across all time frames, MoveStoploss provides unparalleled versatility to traders at all skill levels. The Auto Trail function maintains the specified distance ensuring you can concentrate on strategic decision-making.

Switch off the Auto trail and set the preferred distance for situations that call for more control. Say goodbye to time-consuming, stress-inducing manual stop loss adjustments. Let MoveStoploss automate this process, providing greater command of...

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With the introduction of matrices, vectors, and the ONNX to Metatrader5, the MQL5 community now has the exciting capability to build artificial intelligence (AI) trading models of any complexity. This new addition offers promising potential across industries, from entertainment to healthcare.

Artificial Intelligence, currently being honed by tech giants Google and Microsoft among others, may sound complex but with a solid understanding of AI's fundamental components, it becomes manageable. Today's focus? Optimization algorithms.

What's their function? To fine-tune the parameters of your neural network as they train - reducing the loss function and improving overall performance. The key influencers here are the optimizers. These compute the mismatch between actual values and network predictions, progressively minimizing this error through parameter modification at each training itera...

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In initiating the program, remember to input the current trader and investor password in the allocated "CUR" field. To auto-generate a fresh password, utilize the "NEW" option, or specify the account type, be it "TRADER" or "INVESTOR," in the "NEW" row. Increment your point in the storage history and manifest new passwords using the "NEXT" function. Secure passwords can be archived and stored in the terminal files directory by deploying the "SAVE" action. Remember, this tool is designed to aid password management, ensuring ease of access and heightened security. Remember to leverage these features for optimal account protection.

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In a previous piece, the concept of creating a breakeven and trailing stop system with two operable modes for automated Expert Advisor (EA) settings was discussed. Currently, the system isn't fully automated, instead operating in a "semi-manual" way, reliant on the user-specified settings for the placement of the line or stop order. The following will introduce a further level of automation to the system.

This new level of automation relieves traders from constant activity. Users assign a time during which the EA can send orders or open positions. However, as a stipulation for the system's success, user behaviour must align with the system they set. Automation includes the introduction of a time slot control class, a class constructor, a StringSplit function, and a StringToTime function.

Simultaneously, the dangers of an unsupervised EA is underlined. Users will also need to pay c...

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Recently released reports delve into the emerging trends within the software development industry. Highlighting innovations, these studies are an insightful resource for those with an eagerness to be at the forefront of technical expertise.

One of the key findings is the shift towards microservices architecture. Fuelled by the drive for greater flexibility and scalability within applications, the widespread adoption of this architectural style signals its proven value. More organizations, recognizing the potential for greater speed and lower cost in the development and deployment of applications, are choosing a microservices strategy.

Increasing complexity in projects also underlines the growing requisite for DevOps practices. Teams seeking to streamline processes and methodologies whilst enhancing productivity and efficiency cannot overlook this integration. Collaborative efforts a...

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Delving into the depths of the intricacies of automated trading and the challenges it presents, critical issues such as the efficient triggering of breakeven and trailing stop are discussed in detail. Distinctions are made between mechanisms suitable for HEDGING and NETTING accounts. Simple systems using OCO orders are considered the start-point. In practice, these models revolve around the assertion that take profit or stop loss orders are created the moment the EA sends an order to the trade server and cease to exist once the position is closed due to one of the limits being reached.

The C_Manager class is instrumental in creating the triggering mechanism for breakeven and trailing stop. Examining the function’s code reveals a simple yet dynamic process, capable of generating the breakeven level for positions. The operation revolves around comparing the latest financial value obtai...

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Addressing developers, a new approach has been identified, namely for buy stops, offering a unique solution for pending orders. This methodology may appear advantageous to those in the programming community, as it provides a streamlined solution to a recurring issue. The developers' feedback can be invaluable here, aiding in the identification of any potential bugs or inconsistencies.

In regard to requests for a code accommodating sell stops and other pending orders, please express interest. Given the proven interest, the attempt to construct an equally efficient solution in spare time, will happen. Unification of shared knowledge and problem-solving skills can be instrumental in ironing out potential issues in the provided code.

The pool of knowledge and expertise from the developer community is always welcome. Thus, assistance in spotting bugs or anomalies in the operational flo...

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Assessing the intricate interplay of shapes and angles in a neural network requires technical precision and substantial computational aptitude. Taking into account past experimentations with the DeepNeuralNetwork.mqh library, fundamental issues have been identified that point to an inefficiency in the optimization methods at play and the low performance of the neural network itself in comparison to a simple perceptron. It is speculated that the root of the issue may lie in the type of data being fed into the neural network.

Responding to this situation, newer experiments have been forged, taking into account the critique of the TakeProfit to StopLoss ratio. These have made use of a pattern tracking algorithm, coined as "template technology", which tracks the price effect on pattern recognition in determining entry points.

The crux of the latest experiments has been to evaluate the ...

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Keeping in line with the latest advancements in the realm of technology, a new indicator has been developed, incorporating aspects of a basic slope moving average and a cloud. Maintaining a user-friendly interface, the indicator showcases two central types of signals for the convenience of users. These are labelled as 'preparing' and 'entry', represented visually by a dot and an arrow respectively.

Framing a layout that simplifies complex computations, this indicator stands as a bridge between users and intricate data analysis. The 'preparing' signal serves as an alert for imminent events, letting users foresee the occurrence of possible shifts. On the other hand, the 'entry' signal functions as a prompt to take definitive action, guided by the fluctuating patterns indicated by the arrow.

Compact in its design yet multidimensional in its performance metrics, this development is a...

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The recent strides in behavior cloning methods, grounded in supervised learning principles, have paved the way for impressive results. A central challenge, however, remains in identifying optimal role models which can be cumbersome and time-consuming to gather. Meanwhile, reinforcement learning methods possess the ability to function with non-optimal raw data, and in the process of searching for optimal policies, they can unveil suboptimal approaches to a set goal. Yet, while theoretically advantageous, in practice they often traverse into complex optimization problems, a fact that becomes starkly evident in higher-dimensional and stochastic environments.

A group of researchers proposed a solution to merge these two methodologies with the conception of the Distance Weighted Supervised Learning (DWSL) method. Designed as an offline supervised learning algorithm for goal-conditioned po...

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Implications of Spatial Temporal Fusion (STF) are vast. It tackles forecasting with a bifocal perception, tracking both demand and supply in collaborative platforms like Uber, Amazon or Airbnb. The dual prognostication of these two elements, previously unconventional, was crystallized in a research study that presented the causaltrans framework. Collaborative relationships present in demand and supply were demonstrated via a matrix G, with all forecasts made through a transformer network. It inspired a similar approach in tracking supply and demand for traded securities. To accomplish this, proxies for demand (bullishness) and supply (bearishness) were utilized. Drawing upon this, added dimensions of a spatial matrix and time were incorporated into the forecasting process. Instead of employing transformer networks, forecasts were done utilizing custom coded multi-layer perceptron. Bes...

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The efficacy of optimization algorithms based on elements found in nature is widely penned in scientific literature. Swarm intelligence algorithms, for example, leverage intelligence to find salient solutions, which are particularly efficient for global search and adaptability to change. Conversely, physics-based algorithms model natural phenomena to solve optimization problems, a notable advantage being their ease of comprehension and general efficiency.

The Spiral Dynamics Optimization (SDO) provides a viable tool for solving complex optimization problems utilizing the logarithmic spiral phenomenon present in nature. The logarithmic spiral phenomenon has multiple occurrences in nature and often results in efficient search behavior in metaheuristics, which spawned the development of SDO.

However, several limitations and disadvantages plague the SDO algorithm when searching for so...

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Presenting a comprehensive understanding on how to optimize trading performance using an Expert Advisor (EA). The EA's operation is reliant on personal technical analysis, with users having full control over trading directions - either long or short positions. However, please note hedging is unsuitable in this scenario.

An intriguing aspect of the EA is its intelligence in trade placement. Analysis of volume and levels sets the stage for strategic position designation. The parameters, Level and Length, are defining the depth of the pullback and the extent of scaling in trades respectively.

The EA operates with a maximum floating Profit/loss threshold, set by the Close PL, resulting in closure of all position once this limit is reached. Both profit and risk limit binding the profit/risk ratio are essential operational parameters. The 'capital' parameter represents your balance prior ...

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Discussing metaheuristic optimization methods and their distinct feature of exploring large search spaces to land global optima from functions with many local optima or those that are not continuously differentiable. Among them is the class of evolutionary algorithms which mimic natural evolution principles to solve complex problems. Dive deep into the simplicity and efficiency of differential evolution algorithm, a member of metaheuristic optimization methods, which uses a population of vectors that mutate and crossbreed to generate novel solutions and has the ability to find global optima without requiring knowledge of the gradient.

Moving onto the algorithm of differential evolution, learn about the straightforward combination of simplicity and effectiveness. The differential evolution algorithm uses a population of vectors each representing potential solutions. An iterative proc...

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Tracing back the development of the Random Forest method, it delves into the works of some notable scientists in the field of machine learning and statistics. Rooted from decision trees, Random Forest was developed as an amalgamation of decision trees using bagging and added randomness. Each decision tree uses a random subsample from the training dataset and a random set of features in each tree node. This technique makes each tree unique and reduces correlation between trees, enhancing their generalization ability.

With its high performance and versatility to handle both classification and regression problems, Random Forest has quickly become one of the most popular methods in machine learning. It's used in making decisions regarding classing of an object or predicting numerical values, utilized in fields like finance, medicine, and data analytics.

Representing a decision tree wit...

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Presenting a modified interpretation of Igor Kim's i-session indicator, exclusively modified for enhanced functionality. View original code at www.mql5.com/ru/code/7419.

The modifications applied aim at optimizing its functionalities. First, the tool is now adapted to cater solely to the Asian trading session, enhancing niche specificity. Additionally, user-centric modifications such as adjusting Asian time to local time, customizing box colors, and deciding on box filling have been facilitated, bringing greater convenience for the user.

Evolved from the original, the box names have been omitted in this version. Further, maintaining a clean trading platform, all drawings vanish from the trading chart once the indicator is removed. Offering transformations to simplify trading intricacies.

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Developed in 1965, The Nelder-Mead method was designed to work with functions that did not have derivatives or did not have analytical equations for derivatives. This optimization method was applicable where traditional gradient methods couldn't be applied. In this process, Nelder and Mead introduced the concept of using a simplex - a polyhedron in the space of function parameters.

The Nelder-Mead method received overwhelming acceptance by the scientific community and is widely utilized in the field requiring function optimization. Algorithmically, it is a deterministic algorithm and can handle multiple local minimums without needing function derivatives.

Highlighting the nature of the simplex, it consists of a set of points forming a polyhedron, each point represents a set of parameter values of an optimized function. This algorithm computes by iteratively shifting the simplex in ...

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In an effort to enhance market structure analysis, a new segment of code has been developed. This innovative code aims to pinpoint the latest fractal price value, providing results via on-chart commentary. As it stands, the code does not support the identification of fractal lows. Members of the programming community are invited to scrutinize this new development for potential flaws. Constructive feedback and tangible suggestions are welcomed and highly appreciated to refine and improve this promising code. Collaboration always leads to advancement.

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Dive deeper into the intricacies of MQL5 programming with Part Six focusing on advanced array functions. Enhance understanding of automated trading nuances regardless of experience level. This chapter unfolds detailed explanations and examples on various array functions including ArrayPrint, ArrayInsert, ArraySize, ArrayRange, ArrarRemove, ArraySwap and ArrayReverse.

Understand the debugging utility of ArrayPrint, get introduced to ArrayInsert that tactfully inserts elements from one array into another, comprehend the role of ArraySize to ascertain the element count in an array and much more. Demonstrative outputs and analogies are provided to break down the function complexities, promoting a comprehensive comprehension of the developers.

Extend the understanding of how the different array functions contribute to the functioning and manipulation of arrays in MQL5. From debugging t...

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