Time series analysis plays a crucial role in fields like finance, allowing for the prediction of future trends using sequences of observations collected over time. Deep learning models have shown effectiveness in capturing nonlinear relationships and handling long-term dependencies in time series data. The MSFformer model introduces a multi-scale feature extraction approach, efficiently integrating long-term and short-term dependencies. Key components include the CSCM module, which constructs multi-level temporal information, and the Skip-PAM mechanism that processes input data at varying time intervals. These improvements enhance time series forecasting accuracy by effectively managing complex temporal relationships at multiple scales.
#MQL5 #MT5 #TimeSeries #DeepLearning

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In the context of technical indicators, the Two Moving Average (MA) system involves analyzing 'Fast' and 'Slow' MA lines in the main window. These lines provide insights into market trends by tracking price momentum over different periods. A visual representation, known as a color histogram (DRAW_COLOR_HISTOGRAM2 style), can be configured to display differences between various price points and MA lines.

Specifically, the histogram can compare Price to Fast MA, Price to Slow MA, or Fast to Slow MA. For greater clarity, the type of price compared can be adjusted using the 'Price' settings. Informational labels can be activated or deactivated, providing real-time values for both 'Fast' and 'Slow' MA indicators. Such indicators can be instrumental for traders seeking trend direction and potential entry or exit points in their trading strategies.
#MQL5 #MT5 #Indicator #AlgoTrading

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Analyzing a trading Expert Advisor's system design reveals challenges in balancing speed and reliability. The goal is to adapt to market volatility without sacrificing system performance. Utilizing MQL5 functions and platform resources can enhance security and efficiency without reinventing the wheel. Code improvements, such as transitioning functions to macros and refining object modeling, contribute to better performance.

The system's approach includes reducing redundant object creation and ensuring the chart is updated, allowing for accurate order and position management. Implementing checks ensures objects are only created when necessary, optimizing EA reliability without unnecessary computational overhead. This streamlined efficiency fosters a robust trading environment adaptable to platform updates.
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An expert advisor is designed around the 'DeMarker' indicator, utilizing pending orders either as 'Limit' or 'Stop'. Signals only trigger upon the formation of a new bar. Users have the flexibility to toggle 'Stop Loss', 'Take Profit', and 'Trailing' functions. A newly integrated parameter allows trailing to activate only when profit surpasses a set level in points, mimicking a breakeven strategy. Pending orders are positioned a specified distance from the current price. If the current spread exceeds the defined maximum, the signal is invalidated, and no order is placed. Should the target profit, defined in monetary terms, be achieved, all open positions are closed, and all pending orders are cancelled.
#MQL5 #MT5 #EA #Indicator

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Optimization algorithms have evolved significantly over centuries. Historically, the pursuit of finding maximum or minimum values in function domains, like the Greeks and later Bernoulli's brachistochrone problem, laid foundational principles. Variational calculus introduced by Euler and Lagrange further refined these concepts, leading to advanced methods widely used in solving dynamic and static optimization problems.

In modern times, the introduction of computing technologies has enabled the implementation of complex optimization algorithms, including the development of stochastic optimization algorithms in the 1980s. These metaheuristic algorithms do not require prior function formulas and are influenced by natural models such as particle swarms or ant colonies, handling multiple solutions simultaneously.

To evaluate and compare different opt...
#MQL5 #MT5 #Algorithm #Optimization

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This technical indicator employs two buffers using OBJ_ARROW for visual cues. It assigns a value based on the relationship between the 'Close' price and the iMA indicator. If the 'Close' price exceeds the iMA, a value of '2' is assigned. Conversely, if it is lower, the value '-2' is used.

Furthermore, it incorporates the stochastic oscillator's 'main' line for additional assessment. If this line falls below 'Value Level #1', the indicator assigns '1'. Should it surpass 'Value Level #2', the value '-1' is designated. This method facilitates a straightforward visualization technique for traders to interpret market conditions when used alongside manually added iMA and iStochastic indicators.
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In the realm of AI, Transfer Learning is emerging as a transformative method for enhancing neural network efficiency and reducing training costs. By reusing the knowledge of pre-trained models, this approach facilitates quicker and more effective problem-solving in new domains. This article delves into the creation of a specialized tool for leveraging Transfer Learning. Key highlights include the design and implementation of a user-friendly interface for managing neural layers, ensuring that only new layers are trained while preserving the donor model's learned knowledge. Through meticulous design and functionality, this tool promises to streamline workflow for traders and developers optimizing algorithmic models in MetaTrader 5.
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The updated ZigZag indicator now includes automatic step size determination for changing wave direction, eliminating complexity with only one configurable parameter: Scale. The default Scale setting is 1.0, providing a balanced sensitivity. Adjusting the Scale to 0.5 reduces sensitivity, leading to fewer zigzag wave reversals. Conversely, setting the Scale to 2.0 increases sensitivity, resulting in more frequent reversals. The step size for changing wave direction is dynamically determined by the price itself, allowing the user to define the Scale at which to identify zigzag extremes. This flexibility helps in adapting to various market conditions and enhances pattern recognition on price charts.
#MQL4 #MT4 #Indicator #Strategy

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In recent development of the TabControl WinForms object, a challenge was encountered with graphical element name length that limited the object creation process. The prior naming convention incorporated a full hierarchical reference in each child element's name, often exceeding the 63-character limit. A new naming methodology was implemented to resolve this by using the program name, element type, and an index indicating the number of similar elements already present.

This change allows unlimited nesting without exceeding name length restrictions. To further enhance clarity on the purpose and identification of elements, a new "description" property was introduced. This property provides a meaningful description, making it easier to reference these elements directly in the program.

In parallel, continued development of the TabControl included the crea...
#MQL5 #MT5 #Algorithm #WinForms

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The new update for the trading strategy Version 2 includes significant enhancements to risk management and signal control. Two new features, 'Stop Loss' and 'Take Profit', have been added for better regulation of trade exits. A 'Trailing Activate' parameter is introduced to improve stop-loss adjustments. Unlike 'Trailing Stop' or 'Trailing Step', 'Trailing Activate' acts as a breakeven function, securing profits as positions turn favorable. Once a position gains 'Trailing Activate' points, the stop loss shifts to this new level, allowing standard trailing functions to proceed.

Additionally, a new 'Time Control' parameter group is introduced to refine signal detection. This feature permits setting a specific time range using the 'Use Time Control' toggle, with start and end times down to the minute. This allows for precise signal searches across daily transi...
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The article focuses on designing a trading system using the Alligator indicator in MetaTrader 5 using MQL5. The Alligator, developed by Bill Williams, utilizes three smoothed moving averages known as the Jaw, Teeth, and Lips. These components help identify market trends, determining whether they are up, down, or sideways.

The piece outlines three strategies: Trend Identifier, Signals System, and Enhanced Signals System. Each strategy generates trading signals based on the position of Alligator lines.

Development of these systems involves implementing MQL5 code to automate signal generation. The code examples show how an EA can be configured to display market signals directly on the chart.
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A new and straightforward indicator has been developed that plots a channel with a constant range, calculated as twice the step-size parameter. This range is violated when the lower quote price surpasses the channel's upper boundary or when the higher quote price falls below the lower boundary. The indicator is built using conditional compilation, ensuring compatibility with both MQL4 and MQL5. For those interested in accessing the source code, it's now available under "Public Projects" in the MetaEditor, labeled with the name "FMIC". Ensure to review and integrate as needed for your technical advancements.
#MQL5 #MT5 #Indicator #FMIC

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Refine your MetaTrader 5 algorithmic trading library with improvements to graphical library classes that address mouse interaction issues and optimize color handling. The article discusses enhancements including new sorting properties for graphical elements and a restructured naming convention to prevent excessive name lengths that can lead to resource handling issues. It introduces the development of the TabControl WinForms object, outlining preparations for its full implementation. These adjustments cater to improved layout flexibility and performance, highlighting the significance of efficient UI interaction handling and clean system resource management, essential for both traders and developers refining their trading algorithms.
#MQL5 #MT5 #WinForms #GUI

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A practical example illustrates the implementation of profit and loss labels for the most recently closed trades in an Expert Advisor. This tool simulates dummy trades allowing observation within the strategy tester environment. The labels appear only on new trades, excluding historical data. This solution utilizes both the Canvas library and the standard library for implementation. Two configurable inputs offer a choice between employing the Canvas method versus text and rectangle objects from the standard library. Users are encouraged to refine or enhance the code as needed to explore alternative approaches or improvements that might better fit your strategic requirements.
#MQL5 #MT5 #EA #coding

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Recurrent neural networks (RNNs) offer distinct advantages for tasks involving temporal patterns, such as stock market predictions or video sequence analysis. Unlike traditional networks, RNNs can remember previous inputs, thus reducing redundant data processing and enhancing predictive accuracy. Each neuron in an RNN retains a state representing compressed input history. Through the use of activation functions with values less than one, the impact of older data diminishes gradually, yielding a predictable memory horizon. Integration of RNNs in tasks like video analysis or language translation has shown promising results, supporting both supervised and unsupervised learning frameworks effectively.

Implementing RNNs requires attention to architecture, especially when leveraging technologies such as OpenCL for performance optimization. With advancements like increa...
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A straightforward channel indicator has been developed with a consistent range dictated by twice the step-size parameter. The indicator's range is breached when the low quote price surpasses the upper boundary or the high quote price exceeds the lower boundary. This code employs conditional compilation, allowing compatibility across both MQL4 and MQL5 platforms. Additionally, all related source code is now accessible in the "Public Projects" section of MetaEditor. The publication can be found under the name "FMIC" in CodeBase. This update ensures broader accessibility and utility for developers working with these platforms.
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The article delves into innovative adaptations of the Commodity Channel Index (CCI) to improve its analytical accuracy and application in trading. By altering the calculation logic, such as replacing division with multiplication, and considering new statistical methods to set indicator levels, the modifications aim to produce unique and reliable indicator values. The evaluation is further enhanced by using diverse window functions like triangular and flat-top, resulting in varied outcomes. Testing with expert advisors reveals significant differences in trading performance. The adaptations provide traders customizability and potentially more informed trading decisions, catering to both classic and modern trading strategies.
#MQL5 #MT5 #CCI #EA

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A straightforward trend-following strategy is introduced for daily (D1) time frames. Buy signals trigger when the closing price crosses above the EMA based on high prices, provided the RSI is above 55. Sell signals occur when the closing price crosses below the EMA based on low prices, with RSI below 45. Any existing trade will close upon an opposing signal. This strategy includes take profit and stop loss mechanisms to manage risk effectively. Decision-making relies on the calculated EMA and RSI indicators to ensure adherence to the defined trading logic. Suitable for traders seeking structured approaches within daily time frames.
#MQL4 #MT4 #Strategy #Trend

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Variational Autoencoders Unveiled"

The variational autoencoder (VAE) offers advanced unsupervised learning capabilities. It differs from standard autoencoders by incorporating a probabilistic approach to output distributed representations instead of single deterministic values. This addresses the interpolation gap issue seen in traditional autoencoders when reconstructing image data.

The VAE architecture uses an encoder to output distributions characterized by mean and standard deviation instead of discrete values. The reparameterization trick is key, allowing backpropagation compatibility despite non-differentiable elements like random generation by sampling from a standard normal distribution, and adjusting with learned parameters.

Regularization via Kullback–Leibler divergence encourages feature distribution alignment to a standard normal distribution, balan...
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This trading assistant is specifically designed for manual trading operations, operating exclusively on the current symbol. It utilizes a new parameter for trailing known as 'Trailing activate if profit is >= ', serving as a breakeven point for triggering the trailing mechanism. If a position reaches this set profit level, trailing will be activated. In instances where positions lack Stop Loss or Take Profit values, the assistant automatically applies the specified input parameters. Additionally, the assistant tracks target profits: once the target is reached, all positions are closed. Importantly, this Expert Advisor does not restrict the use of Magic numbers and ensures that, when modifying positions, the Magic number is preserved. This enhances flexibility and efficiency in managing trades effectively.
#MQL5 #MT5 #EA #Trading

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