For developers and IT experts, here's a new expert advisor that may be worth your attention. The advisor uses three indicators - a simple moving average, standard deviation, and RSI - to identify price reversals.

Consider a buy signal. This is issued when the opening price falls below the moving average (diminished by 2-standard deviations) and the RSI is below oversold value. Further, the closing price must exceed the moving average (less 2-standard deviations) and the RSI must ascend past the oversold signal. Here, the take profit equates to the asking price increased by 2-standard deviations, and the stop loss is the asking price less the standard deviation.

Conversely, a sell signal arises when the opening price overshoots the moving average (plus 2-standard deviations) and the RSI is overbought. Additionally, the closing price must be less than the moving average (increased by ...

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Understand the mechanism behind the creation of custom indicators with drawings, made using standard library's CCanvas class. Uncover the reasons why CCanvas surpasses other methods, the essential chart properties needed for coordinates calculation, and the transparent indicator creation process. Addressed topics include the enhanced adaptability of Canvas, the necessity of chart window properties for custom charts, and DRAW_FILLING with the added transparency feature.

Examine the properties of CHART_WIDTH_IN_PIXELS, CHART_HEIGTH _IN_PIXELS, CHART_PRICE_MAX, CHART_PRICE_MIN, CHART_SCALE, CHART_FISRT_VISIBLE_BAR and CHART_VISIBLE_BARS to comprehend their role in forming a custom chart. Witness how chart changes impact these properties.

Accomplish a deeper comprehension of coordinates conversion and its role in the chart window properties through practical examples. Further, the asso...

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In the expanding realm of financial technology, flexibility and adaptability are key. The discussion revolves around a versatile tool that seamlessly integrates with your expert advisor (EA). It operates across all assets and markets with ease. The encompassing scope of this tool allows it to function universally. Once plugged into your EA, it streamlines operations and optimizes trading strategies. In a fluctuating financial ecosystem, having a versatile tool can make a substantial difference. A well-integrated system presupposes efficient management of trades across varied asset classes and markets. This tool exemplifies innovation and efficiency, signifying a new benchmark for trading algorithms. Remember, in the world of digital finance, adaptability is everything.

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Reinforcement learning algorithms are reliant on reward policies but in reality, rewards do not follow every action. In trading, for instance, a reward is only received once a position is closed. Therefore, establishing how each action contributes to the overall result can be complex. Looking towards human behavior and the role of curiosity in learning could provide new approaches to structuring reward policies and model training processes. A method based on incorporating "curiosity," or the error in the model's ability to predict actions consequences, was applied in a study in 2017. This “intrinsic” reward encourages exploration and can help overcome challenges such as rare extrinsic rewards, training without rewards, and generalization to new scenarios. This approach has shown effectiveness in computer games and demonstrates the potential for transferability to trading. By applying ...

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An innovation in trading indicators has arrived, adding new methods to the conventional indicators on MetaTrader5. Building on the Bollinger Bands indicator, the new addition brings on board exponential, smoothed, and linearWeighted methods to the existing simple moving average technique.

To use this new tool, it has to be placed in a specific directory. For Windows users, this will be a path similar to C:\Users\lucas\AppData\Roaming\MetaQuotes\Terminal\Indicators\Examples.

One of its unique features is its default setting at zero. There's also an example of execution which opts for LinearWeighted average. The code extends the capabilities of standard indicators, offering users a more comprehensive approach to data analysis.

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Leveraging the classic trading fundamentals, the strategy uses various refined indicators for different markets and exchanges. The approach relies on the combined usage of several indicators, some modified for better performance. Owl Smart Levels, an indicator fusing all crucial parameters, unveils the interaction of the trading system's components. The utilitarian design of this comprehensive trading system makes it a ready-to-use tool for following this profitable strategy.

Break-up of the system:

1. Profitable trading involves correct trend determination.
2. Understanding the difference between global and local trends.
3. Choosing the most suitable trend-following trading practices.
4. Detailed understanding of the tools of the Owl strategy.
5. Execution of the strategy and determination of stop loss, take profit levels.
6. Usage of additional tools and determining the entry poi...

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Unraveling the significance of geometric mean in mathematics: It serves as an average that expresses the central tendency of a finite set of real numbers. Unlike the arithmetic mean that utilizes the sum of these numbers, the geometric counterpart deploys their product.

Primarily, geometric mean is quite beneficial for computing sets of numbers either designated for multiplication or exhibit an exponential character, for instance, population growth figures or financial investment's fluctuating interest rates.

In terms of benchmarking, it excels in calculating speedup ratio means. To understand better, consider a combination of 0.5x (half as fast) and 2x (twice as fast), the mean derived will be 1 (implying null speedup).

Geometric mean is indeed more effective than arithmetic mean in defining proportional growth, be it constant or varying. Within the business sector, geometric m...

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Continuing from the previous insight into the potential of category theory in complex systems, the concepts of spans, experiments, and compositions emerge as significant tools. These elements offer intensified, adaptable reasoning about systems and can drive the development of advanced trading strategies.

In-depth knowledge of the underlying financial market structure through category theory can equip traders with novel insights, enabling the crafting of intricate portfolios and efficient risk management strategies.

In terms of category theory, a span is a construction that interconnects three objects and two transitions between them. It facilitates the comparison of two paths or viewpoints and allows a broader analysis of the system. Pullbacks, a type of limit where objects are interconnected by a pair of morphisms, play an essential role in various areas of mathematics and scien...

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Focusing on order handling, there is an implementation in place that utilizes 'CArrayInt' which keeps track of ticket numbers affiliated with trades. In connection to this, changes to the list of tickets are monitored within the 'onTimer' function. As modifications occur, these trigger specific event handlers. Please be aware this is a fundamental approach, lacking comprehensive features found in MQL5's prestigious 'onTradeTransaction' handler. In order to provide transparency on the operation, a visual output log from the Expert Advisor demonstrates the function in action. This model is intended to illustrate the basic structure of the EA's operations.

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Dive into the world of Autoencoders, an unsupervised form of artificial neural networks. This form of neural network works by compressing input data into a lower dimension and attempting to use this lower dimensional representation to recreate the original input.

For instance, imagine passing a blurred cat image through an autoencoder; it will compress the image, decompress it back to its original state, all while losing some of its noisy or blurred pixels to leave a clearer image behind. This process cues to the financial space where an autoencoder neural network can filter out market noise and unearth trading opportunities.

Explore the intricate parts of an Autoencoder, which consists of an encoder that compresses the input data into a lower-dimensional latent representation, while the decoder takes the latent representation and tries to reconstruct the original data. Consider th...

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Presenting a variant of the Bollinger Bands, which leverages linear weighted average and weighted deviation. This innovative approach provides a nuanced methodology to understanding price volatility in the market. This new tool promises to enhance the manner in which deviations are analyzed.

Usage guidelines: This tool can be utilized in the same way as any Bollinger type indicator. Relying on statistical calculations, it offers a comprehensive understanding of price behavior. It is rooted in cogent methodologies, aimed at enabling a more complex interpretation of market trends.

References to the ins and outs of the weighted deviation principle can be looked up. Focus on an analytical approach towards a better understanding of market dynamics for a successful implementation of this model.

Remember: A robust analysis means better decision-making power. An amplified understanding o...

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The importance of interaction between an Expert Advisor (EA) that trades automatically and the trade server cannot be overstated. Analyzing EA automation begins with understanding the flow of messages between them. Typically, a simple system would involve transactional communication bound by the direction of messages. However, for complete automation or minimal automation, certain extra features and details need to be added throughout the EA and the C_Manager class.

Addressing control and accessibility issues, some functions have been added to the C_Manager class. These will either release the EA or inform what the EA intends to do. This aids in increasing the robustness and reliability of the code. One of the functions removes pending ticket value when its value is equal to the pending ticket. It is crucial to note that a pending order usually does not get removed by the EA, but by ...

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Shifting the focus on technical indicators, there's an innovation in analyzing signals from the renowned RSI (Relative Strength Index). The distinguishing aspect of RSI is level crossing signals, often pegged at 30 and 70. However, the connection to real market conditions could be seen as suboptimal.

For addressing this, dynamic levels have been incorporated in the RSI indicator. The introduction of these dynamic levels enhances the efficiency of these signals, making them more impactful. With this development, expect the signal analysis from the RSI to reach a more proficient level in identifying potential market opportunities.

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Unveiling the potentials of MetaTrader 5 indicators: the moving average and relative strength index. Both essential tools aid traders in spotting market trends and entry and exit points. The moving average simplifies data by creating a single, consistent line, aiding trend identification. In contrast, the relative strength index measures the speed and change of price movements and determines market over-saturation. Together, these tools can enhance your trades.

Delving into historical data can provide useful insights. Candlestick charts provide key details about price relationships over each period and highlight potential support and resistance levels. Past price movements offer intelligence about trend direction and strength. Historical patterns can guide traders in future market movements and strategy develops.

Issues with off-trend signals have been noticed in the system. The Tre...

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Continuing the journey with MQL5 programming, part 7 of the series is ready to take theory to practice through real-world examples. The significance of pseudocode as a link between the code implementation and abstract algorithmic concepts will be a crucial investigation point. Things will move towards understanding how AI-generated code can enhance rather than replace conventional coding knowledge and the importance of code understanding. This article will reveal ways to incorporate AI-generated code into programming projects seamlessly.

Trading the high seas of the technological landscape, MQL5 programming now diverges towards project-based learning, focusing on mastering core concepts before developing Expert Advisors or custom indicators. This journey will show that step-by-step tackling of MQL5 complexities can lead to proficiency over time. The hands-on example will demonstrate...

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Deepening the examination of MQL5 wizard implementation, this post delves into the critical role of Eigen Vectors in enhancing the efficiency of Neural Architecture Search (NAS). Neurological networks, mirroring curve-fitting to data, derive a formulaic expression that when applied to the input data yields a target value. This attribute has fueled the ascent of neural networks, owing mostly to their multi-dimensionality.

Addressing the question of network settings, various network types imply a broad spectrum of designs and settings. For the current discussion, focus is pinpointed on the multi-layer perceptron's hidden layer numbers and sizes. Although omitted in present discussions due to heavy computing requirements, factors like activation type, initial weights and biases can deeply impact network performance.

To mitigate exhaustive training, this post involves a single forward...

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Stay up-to-date with the latest addition to the realm of code on MQL5. A fundamentally advanced concept elaborated by an esteemed contributor, this piece of code takes a holistic approach.

Emanating from the mind of a seasoned programmer, the code's intricacies invite closer inspection from discerning tech enthusiasts. The beauty of this code lies in the logical intricacies of its design. Truly, it is a valuable gem for those who deal in the cutting edge domain of development.

Remember, familiarity breeds contempt. Constantly updating your familiarity with evolving code architectures ensures maintaining the edge in this advancing sphere. After all, navigating the dynamic realm of programming requires perpetual upskilling.

This piece borrows from the rich expertise of its author and extends an opportunity for comprehensive understanding. Note, the value lies not just in applying ...

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In an era of unsupervised machine learning, Kohonen Maps or Self-Organizing Maps (SOM) present an innovative approach to visualizing and analyzing high-dimensional data sets. Developed by Finnish mathematician Teuvo Kohonen in the 1980s, these maps reproduce a low-dimensional representation of a data set while maintaining the topological structure of the data, enabling easier data interpretation and trend spotting.

The algorithm behind SOM involves four key steps: initialization of weights, calculation of Euclidean distances between inputs and their corresponding weights, identification of the winning unit, and weight updates through the Kohonen learning rule. This competitive learning fosters a topological ordering of neurons in the map and facilitates the discovery of complex relationships in the data.

However, as potentially beneficial as Kohonen maps are, certain limitations are...

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The Commodity Channel Index (CCI) with dynamic channels provides an efficient way to exclude unnecessary signals produced by level crossings. This robust code iteration caters to the need of displaying indicators in a separate window.

The indicator comes with five buffers, all initialized and set to indicator data. The indicators are of DRAW_LINE type, and come in different colors for better visual analysis. The indicator_width5, set to 2, provides more thickness to the indicator line for easy visibility.

The input parameters for this CCI are: CCI period, price, overbought and oversold levels, upper and lower neutral levels, and a smoothing period. The parameters allow fine tuning the CCI indicator to adapt to specific market situations.

Buffers are set to accommodate the 'bupu', 'bupd', 'bdnu', 'bdnd', and 'cci' variables, preparing the system for rapid data entry and retrieval.

...

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Dive deep into the distributed Q-learning algorithms along with the implications of Implicit Quantile Networks (IQN) and the Fully Parameterized Quantile Function (FQF). Discussing the theoretical aspects of each method, the technical breakdown of how they function is detailed. Explore these parameters in the context of implementation in the MQL5 programming language. Review how to build the feed forward process using already implemented operations of neural networks and how to create quantile embedding using the aforementioned formula. Understanding these functions can significantly augment the credibility and minimize the prediction error of your model. Delve into the specifics of the CNeuronFQF class and its feed forward process for a broader understanding of this topic.

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A new script on the tech horizon has been designed to identify the closure of a trading day. This timely innovation will facilitate traders by generating a message box notification at the completion of each trading day. The presence of a user-friendly and efficient system marks a streak of progress in the field.

The script comes with features devised to ensure the smooth operation of facilitating transactions without the trader needing to constantly monitor their system. It aims to further simplify the process by allowing the end-user to be informed of the trading conclusion, effectively streamlining the trading procedure.

In a nutshell, with the implementation of this novel tool, traders can focus on their work without worrying about missing the closing of a trading day. The enhancement provided by this script is expected to significantly contribute towards advancing efficiency in...

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