Understanding the exact requirements for adjusting stop losses in a high-frequency trading environment is a challenging task. The MoveStoploss tool streamlines this process by providing automatic monitoring of all manual trades. The tool, adaptable to various market trends, offers customization in terms of the trailing distance that can be preset or determined by expert advisors.

MoveStoploss is not restricted by the type of financial instrument or a specific timeframe. Its versatility makes it an invaluable asset for traders dealing in either currencies, commodities, cryptocurrencies, or stocks. By incorporating the auto trail feature, it removes the burden of manual trailing while generating accurate stop-loss points.

Furthermore, the tool allows users to switch off the auto trail feature and manually input a preferred stop-loss distance, offering a balance between automation an...

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Discover the world of Behavior Cloning (BC) methods with the Decision Transformer and its various modifications. BC techniques focus on imitating expert trajectories according to environmental states and desired outcomes. However, it should be noted that the expert assessments of similar environmental states could significantly differ, sometimes even going in completely opposite directions.

A reminder, past works did not involve experts to create our training set. Instead, there was a selection of best trajectories through various methods of sampling the Agent's actions. Admittedly, these trajectories were not always optimal. Due to the process of sampling in a continuous space of actions and episodes, it is impossible to capture all possible options. The chosen trajectories often satisfy only a portion of our requirements and often resemble outliers, which the model might simply dis...

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The comprehension of machine learning's capabilities has evolved sequentially over years. Emphasizing on time-series classification, an understanding that machine learning algorithms can predict and generalize well but struggle with discerning cause-and-effect connections has emerged. Machine learning perceives all training data as just that, training data, potentially creating blunders for novices attempting to use it for profitable trading with new data.

Our goal is to develop an algorithm that significantly reduces overfitting by autonomously locating patterns in data. This algorithm should not only generalize training examples but efficiently ascertain the existence of cause-and-effect relationships within them.

Considerations from artificial intelligence efforts in Forex have led to the idea of self-controlled machine learning models, which check their predictions for errors re...

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In light of developments surrounding chart reading in programming, technicalities around searches for Wick or Body based High/Low in visible bars have resurfaced. To elucidate, Wick based High, in essence, is a particular type of configuration within this scenario, acting as an example of high/high readings. In juxtaposition, the Body-based High exemplifies a different situation where the body of the chart, instead of the wick, defines the pinnacle.

Each method presents its unique interpretation of the data, running along the visible bar range. Analysts and developers should consider both methods’ distinct features for a more comprehensive understanding of chart readings. Remember, this encompasses but isn't limited to, the chart's highest and lowest points. A deeper grasp of Wick and Body-based High/Low readings can significantly enhance the interpretation of chart data.

Caution ...

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Learn how to fine-tune your data analytics skills with a sophisticated indicator tool that encompasses 14 distinct types of moving averages calculations. This tool, leveraging its operational foundation on Pine Script libraries, closely ties each computation to the price close data.

This powerful indicator includes a wide range of calculation types: namely SMA, EMA, WMA, VWMA, RMA, DEMA, TEMA, ZLEMA, HMA, ALMA, LSMA, SWMA, SMMA, and DONCHIAN. Enhance the accuracy and precision of data interpretation by utilising this comprehensive tool, specifically designed for ease of use and optimal efficiency.

Stay at the vanguard of data analysis by implementing this advanced indicator. Dive beyond basic calculation approaches and discover the potential of moving averages to provide more intensive and well-rounded insights.

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Struggling with the idea of creating your own Expert Advisor or programming indicators? Reading code doesn't translate to understanding it. The process might seem complex but with a step by step guide, even beginners can grasp how to automate trading.

Taking a look at the MQL5 language, which is not only for trade automation but extends to plotting indicators, creating files, working with databases, and much more. This guide is for traders eager to harness programming for their benefit - reducing dependency on programmers by acquiring MQL5 language proficiency.

Learn basic programming and technical terms; familiarize yourself with central processing units (CPU), random access memory (RAM), execution thread, bit, byte, and external drives. This guide simplifies the functionalities of each, sharpening understanding around the building blocks of programming.

Before launching into pro...

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Immerse in the scientific exploration of the Intelligent Water Drops optimization algorithm’s potential to model natural occurrences and their uses in solving intricate problems. The IWD algorithm, a brainchild of Hamed Shah-Hosseini, imitates the principle of self-organization and interaction between particles in the rivers, making it a significant tool in optimizing and resolving complicated issues.

The algorithm adopts three distinct aspects for optimization: speed, soil and time ratio. It captures soil from the riverbed and transports it along the river’s path, modifying the river’s trajectory to its advantage. As the less soil a path contains, the faster it is for the drops to traverse; the water drops intelligently manipulate their environment to create the most optimal course with the least resistance.

Since soil is a dynamic parameter and not proportionate to fitness, it req...

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Understanding your broker's daylight saving time (DST) observance can prove beneficial, particularly for those operating on precise scheduled tasks. In this task, a trailblazing script is employed to assess whether your broker abides by US, UK, or AU DST schedules. The methodology is simple and effective: examining H1 chart bars on expected DST change dates and contrasting the alterations in server times.

The DST_AU schedule marks the commencement of server DST on the first Sunday of October (+1), concluding on the initial Sunday of April (-1), easily verifiable at https://www.timeanddate.com/time/change/australia/sydney.

For the DST_UK schedule, server DST begins on the final Sunday in March (+1) and concludes on the last Sunday of October (-1), taken from https://www.timeanddate.com/time/change/uk/london.

The DST_US schedule starts server DST on the second Sunday of March (+1), e...

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Seasonality in trading refers to observed predictable changes in asset prices throughout the year, often due to factors like weather, holidays, quarterly earnings, tax-related events, and natural industry cycles. Understanding these patterns can offer valuable insight to investors who note the potential for predictable and profitable trends. However, another valuable tool in algorithmic trading is the low-pass filter, which helps diminish noise and emphasize longer-term trends, lower transaction costs, and better manage risk.

Data from the Forex market shows that seasonality does indeed have an impact. When data from February 2015-2023 was observed, some tendencies were noted, indicating that markets and seasonality can be considered good companions. While the idea of merging Expert Advisors into one seems plausible, caution is recommended to avoid the pitfalls of high-frequency cha...

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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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