Creating dashboards and trading panels in Expert Advisors and indicators is a valuable skill. The provided code offers a comprehensive framework to develop custom dashboards for MetaTrader 5. This foundation allows for the display of essential trading data, enhancing the overall trading experience. For detailed guidance on constructing this panel, refer to the linked YouTube video. This resource will walk you through the process step-by-step, ensuring a thorough understanding. Enhance your trading interface by mastering the creation of informative and fully functional dashboards.
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#MQL5 #MT5 #EA #Indicator
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Developing a risk manager class for algorithmic trading offers significant benefits for trader protection and strategy enhancement. This article will extend previous work by incorporating risk control mechanisms into a class for implementation in intraday trading systems.
Key concepts such as Stop Loss, Take Profit, and Slippage are crucial. Adding a subclass for algorithmic trading leverages the RiskManagerBase class and introduces new functionalities. The subclass will integrate core principles like encapsulation and inheritance to maintain and extend pre-existing functionalities without redundant code.
Key components will include interfaces for slippage and spread control, getter methods, and handling algorithmic trading specifics. The article will also discuss practical aspects, such as initialization, class destructors, and implementation ...
#MQL5 #MT5 #RiskManager #AlgoTrading
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Key concepts such as Stop Loss, Take Profit, and Slippage are crucial. Adding a subclass for algorithmic trading leverages the RiskManagerBase class and introduces new functionalities. The subclass will integrate core principles like encapsulation and inheritance to maintain and extend pre-existing functionalities without redundant code.
Key components will include interfaces for slippage and spread control, getter methods, and handling algorithmic trading specifics. The article will also discuss practical aspects, such as initialization, class destructors, and implementation ...
#MQL5 #MT5 #RiskManager #AlgoTrading
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The previous article laid the foundation for utilizing optimization results to build an Expert Advisor (EA) with multiple trading strategies. This process no longer requires manually entering parameters into the code or EA inputs. By saving the initialization string in a specific format to a file or source code, the EA can use these configurations directly.
Initial implementation involved manually generating the initialization string. The current task is to automate this process based on optimization results. Although a fully automated solution might not be achieved within this scope, significant progress is expected.
The objectives include creating an EA that performs optimization on multiple symbols and timeframes (EURGBP, EURUSD, GBPUSD, H1, M30, M15). The EA will select the best results, group them, and determine a group multiplier manually. This will ...
#MQL5 #MT5 #EA #Algorithm
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Initial implementation involved manually generating the initialization string. The current task is to automate this process based on optimization results. Although a fully automated solution might not be achieved within this scope, significant progress is expected.
The objectives include creating an EA that performs optimization on multiple symbols and timeframes (EURGBP, EURUSD, GBPUSD, H1, M30, M15). The EA will select the best results, group them, and determine a group multiplier manually. This will ...
#MQL5 #MT5 #EA #Algorithm
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Introducing the combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for stock market predictions. Here are some insights:
CNNs excel at pattern and feature recognition in time series data, identifying trends, seasonal effects, and anomalies.
RNNs, particularly architectures like LSTM and GRU, specialize in understanding temporal dependencies and maintaining sequences over time.
To build a robust predictive model, integrate feature extraction (CNNs) and temporal modeling (RNNs).
Steps for implementation involve:
1. Feature extraction using CNNs
2. Temporal modeling with RNNs
3. Training and prediction
The combination of these models provides comprehensive insights into market behavior, applicable in trading strategies.
Stay tuned for further developments.
#MQL5 #MT5 #AI #ML
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CNNs excel at pattern and feature recognition in time series data, identifying trends, seasonal effects, and anomalies.
RNNs, particularly architectures like LSTM and GRU, specialize in understanding temporal dependencies and maintaining sequences over time.
To build a robust predictive model, integrate feature extraction (CNNs) and temporal modeling (RNNs).
Steps for implementation involve:
1. Feature extraction using CNNs
2. Temporal modeling with RNNs
3. Training and prediction
The combination of these models provides comprehensive insights into market behavior, applicable in trading strategies.
Stay tuned for further developments.
#MQL5 #MT5 #AI #ML
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Navigating the landscape of alternative financial data is crucial for modern investors. With the increasing availability of data sets, the challenge lies in selecting the most resourceful ones for trading strategies. This series guides you in making informed decisions on which alternative datasets to incorporate and which to dismiss.
Highlighting correlation, a fundamental principle in finance, we examine the Federal Reserveβs Nominal Broad Dollar Daily Index (NBDD). Established in 2006, the index's trends, such as record lows in 2008 and all-time highs in 2022, are discussed.
The article explores using macroeconomic indicators from the Federal Reserve Economic Database (FRED) to algorithmically predict the EURUSD exchange rate. Tests involved daily EURUSD data, interest rates, expected inflation, and the Broad Dollar Index. Although initial results show...
#MQL5 #MT5 #AlgoTrading #AI
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Highlighting correlation, a fundamental principle in finance, we examine the Federal Reserveβs Nominal Broad Dollar Daily Index (NBDD). Established in 2006, the index's trends, such as record lows in 2008 and all-time highs in 2022, are discussed.
The article explores using macroeconomic indicators from the Federal Reserve Economic Database (FRED) to algorithmically predict the EURUSD exchange rate. Tests involved daily EURUSD data, interest rates, expected inflation, and the Broad Dollar Index. Although initial results show...
#MQL5 #MT5 #AlgoTrading #AI
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The Strategy Tester report for the Euro vs. Great Britain Pound (EURGBP) under the Trickerless RHMP InstaForex-Europe.com system has concluded. The testing period spanned from October 15, 2021, to September 7, 2022, utilizing the "Every tick" model for precision. The test involved 1235 bars and 5326472 ticks with a modelling quality of 58.38%.
Key parameter settings: Tools, Safe, Signal, Time, News, Profit, Growth, Stop, History, Trend, Back_System, Margin, Trade, ATR, ADX, and MA indicators.
Financial Results:
- Initial deposit: $10.00
- Total net profit: $216.31
- Gross profit: $216.40
- Gross loss: -$0.09
- Profit factor: 2361.41
- Expected payoff: $3.93
- Absolute drawdown: $4.88
- Maximum relative drawdown: 77.54%
Trade Performance:
- Total trades: 55
- Short positions (won %): 27 (96.30%)
- Long positions (won %): 28 (100.00%)
- Profit trades (% of ...
#MQL4 #MT4 #Strategy #EA
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Key parameter settings: Tools, Safe, Signal, Time, News, Profit, Growth, Stop, History, Trend, Back_System, Margin, Trade, ATR, ADX, and MA indicators.
Financial Results:
- Initial deposit: $10.00
- Total net profit: $216.31
- Gross profit: $216.40
- Gross loss: -$0.09
- Profit factor: 2361.41
- Expected payoff: $3.93
- Absolute drawdown: $4.88
- Maximum relative drawdown: 77.54%
Trade Performance:
- Total trades: 55
- Short positions (won %): 27 (96.30%)
- Long positions (won %): 28 (100.00%)
- Profit trades (% of ...
#MQL4 #MT4 #Strategy #EA
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π30β€19π¨βπ»19π5π4β‘2π2
The article delves into the technical implementation and enhancement of library classes for MetaTrader 5, specifically focusing on the interaction of graphical elements with the mouse cursor. It introduces an event model for handling cursor actions within active and control areas of graphical objects, facilitating functionality like resizing and moving elements in a user interface. Changes to constants, methods for cursor position detection, and new event handlers are described to improve accuracy and prevent interaction issues. The article also discusses refining the handling of visibility scopes in graphical hierarchy and optimizing the separator behavior in the SplitContainer control for better user experience.
#MQL5 #MT5 #Algorithm #Indicator
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#MQL5 #MT5 #Algorithm #Indicator
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The LineNotify function sends messages to the LINE app via the Line Notify service using web requests. This method is applicable in EA or Script programs but not in Indicator programs. The function is useful for integrating notifications in automated trading systems. Users can extend the function to include additional features like sending messages with stickers or images through URLs. For further guidance on implementation and customization, consider watching tutorials that provide detailed demonstrations. These resources offer practical insights into how to enhance the functionality of this notification system within trading applications.
#MQL4 #MT4 #script #LineNotify
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#MQL4 #MT4 #script #LineNotify
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The SplitContainer control within the current library uses default settings that do not reflect property changes unless redrawn. The article discusses the enhancement of property-setting methods, ensuring any property change updates the appearance immediately. Two new events are added for WinForms controls in Defines.mqh, enhancing control responsiveness. For example, a SplitContainer's separator now properly interacts with mouse events, improves its appearance when moved, and correctly signals movement to the parent element.
The BringToTop() method fix resolves visibility issues seen with the hidden separator. Flag checks are introduced in various involved methods to control when the element drawing is necessary and restrict unintended displays.
Moreover, new methods for collapsing and expanding panels have been introduced in SplitContainer.mqh. Th...
#MQL5 #MT5 #Algorithm #Controls
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The BringToTop() method fix resolves visibility issues seen with the hidden separator. Flag checks are introduced in various involved methods to control when the element drawing is necessary and restrict unintended displays.
Moreover, new methods for collapsing and expanding panels have been introduced in SplitContainer.mqh. Th...
#MQL5 #MT5 #Algorithm #Controls
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MetaTrader 5 build 4585 follows the previous update, which featured significant web terminal improvements and introduced new machine learning functions in MQL5. In this latest release, we have addressed several issues to further improve your experience with the platform.
β’ In the desktop platform, we've fixed crashes that could occur when stopping the profiling of MQL5 programs.
β’ MetaEditor update provides fixes to memory leaks during MQL5 program compilation and when using intelligent code management functions.
β’ We've also fixed crashes in the tester that could occur when re-running a single test run.
Discuss the update...
β’ In the desktop platform, we've fixed crashes that could occur when stopping the profiling of MQL5 programs.
β’ MetaEditor update provides fixes to memory leaks during MQL5 program compilation and when using intelligent code management functions.
β’ We've also fixed crashes in the tester that could occur when re-running a single test run.
Discuss the update...
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The MACD Four Colors Arrow indicator provides an enhancement to the existing 'Four Colors' series by visualizing MACD signals directly in the main window as arrows. This design aids in improving clarity and signal interpretation. The conventional MACD histogram and signal line, referred to as the "progenitor," are still visible on the graph, ensuring both traditional and arrow-based signal confirmation. By integrating arrows, this approach allows for straightforward visual recognition of signal changes, making it easier for developers and traders to analyze market conditions and potential trends efficiently. This tool can be valuable for those looking to streamline their market analysis process.
#MQL5 #MT5 #Indicator #MACD
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#MQL5 #MT5 #Indicator #MACD
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Deep Q-Networks (DQNs) leverage neural networks to improve reinforcement learning for trading, enhancing traditional Q-Learning by predicting future actions and rewards in complex, high-dimensional markets. Key advancements include using a neural network for mapping Q-values, enabling DQNs to handle dynamic environments and adapt to new data. The target network offers stability, reducing oscillations by periodically syncing with the main network. Experience Replay is employed to train DQNs with diverse, randomized environment samples, thus mitigating overfitting. These techniques help traders develop robust algorithmic strategies and adapt to the fast-changing financial market landscape.
#MQL5 #MT5 #ReinforcementLearning #DQN
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#MQL5 #MT5 #ReinforcementLearning #DQN
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Debugging is an integral part of software development, and its importance is magnified in MQL5 programming. Debugging involves identifying and fixing errors to ensure code executes correctly across various conditions. Common bug types in MQL5 include syntax, logical, and runtime errors. Syntax errors hinder compilation, logical errors produce incorrect outputs, and runtime errors occur during execution.
Key tools for debugging in MQL5 are MetaEditor, print statements, and error logs. MetaEditor provides syntax highlighting and error indication. Print statements are helpful for tracing variable values, and logs can provide execution insights.
Effective debugging strategies include revisiting documentation for error clarification and using MetaEditorβs debugger for step-through execution. These processes enhance code reliability and performance, fostering ...
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Key tools for debugging in MQL5 are MetaEditor, print statements, and error logs. MetaEditor provides syntax highlighting and error indication. Print statements are helpful for tracing variable values, and logs can provide execution insights.
Effective debugging strategies include revisiting documentation for error clarification and using MetaEditorβs debugger for step-through execution. These processes enhance code reliability and performance, fostering ...
#MQL5 #MT5 #Debugging #MQL5
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With the native Python integration, you can apply the full range of mathematical and statistical libraries from this language to analyze market data within MetaTrader 5.
For those interested in Python, we have collected over 200 useful videos, including:
β Installing Python and creating your first script
β Developing useful scripts for automating manual tasks
β Collecting and analyzing price data
β Building trading robots
β Integrating with AI, including ChatGPT
You can discuss your ideas or ask questions in a dedicated forum thread with other Python developers.
Share your experiences and expand your knowledge
For those interested in Python, we have collected over 200 useful videos, including:
β Installing Python and creating your first script
β Developing useful scripts for automating manual tasks
β Collecting and analyzing price data
β Building trading robots
β Integrating with AI, including ChatGPT
You can discuss your ideas or ask questions in a dedicated forum thread with other Python developers.
Share your experiences and expand your knowledge
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An advanced indicator is now available that calculates and plots the exponential moving average (EMA) of the true range. Additionally, it computes an offset of the EMA of its deviation. This tool is compatible with both MQL4 and MQL5 due to its use of conditional compilation. Developers can find the source code for this indicator, along with others, in the "Public Projects" section of MetaEditor under the name "FMIC". This resource provides an opportunity to enhance programming skills and indicator comprehension in both MQL4 and MQL5 environments.
#MQL5 #MT5 #Indicator #EMA
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#MQL5 #MT5 #Indicator #EMA
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β€22π19π¨βπ»3π₯2β1
Code locks, also known as digital or combination locks, are keyless security mechanisms used for access control across various sectors, including residential and commercial buildings. They enable users to program unique codes and offer the flexibility of multiple user access. Key benefits include high security, given regular code changes and user convenience, eliminating the need for keys.
Incorporating code lock principles into optimization algorithms presents novel approaches to solving complex problems, such as machine learning and trading systems development. This involves representing optimized parameters as numerical combinations and iteratively evaluating them to identify optimal solutions. Understanding and implementing these principles can significantly enhance algorithm efficiency and application scope.
#MQL5 #MT5 #Algorithm #EA
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Incorporating code lock principles into optimization algorithms presents novel approaches to solving complex problems, such as machine learning and trading systems development. This involves representing optimized parameters as numerical combinations and iteratively evaluating them to identify optimal solutions. Understanding and implementing these principles can significantly enhance algorithm efficiency and application scope.
#MQL5 #MT5 #Algorithm #EA
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π34β€24π¨βπ»5π4β‘3π€1
A tool has been developed to calculate lot size based on risk percentage and stop-loss level. Users can set a virtual stop loss by clicking on the chart, which will calculate the lot size corresponding to the applied risk percentage. In the input section, users need to specify whether the calculation should be for a buy or sell position, determining if the risk is calculated from the ask or bid price. Longer stop-loss distances on higher timeframes carry more risk due to the larger scale of price points. This tool functions with all types of securities and directly calculates the lot size from the risk percentage and stop-loss distance. Note, it does not account for the maximum tradeable lots allowed by account size and leverage. For that, use the max trade volume script.
#MQL5 #MT5 #LotSize #RiskMgmt
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#MQL5 #MT5 #LotSize #RiskMgmt
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π33β€12π5π€£3π¨βπ»2
In a recent update on trading EA development, significant changes were made focusing on performance and reliability. The Chart Trade feature was removed from the EA, leaving a basic order system. Quick trading options are now available via MetaTrader 5, though they lack integration with the cross order system, underscoring the need for Chart Trade as an independent indicator rather than as part of the EA code.
To maintain system cohesion, Chart Trade is being developed as a separate indicator, enhancing order and position management without burdening the EA. This transition aims to streamline operations while retaining core functionalities. Modifications are underway to ensure compatibility, simplifying the system architecture while retaining essential capabilities.
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To maintain system cohesion, Chart Trade is being developed as a separate indicator, enhancing order and position management without burdening the EA. This transition aims to streamline operations while retaining core functionalities. Modifications are underway to ensure compatibility, simplifying the system architecture while retaining essential capabilities.
#MQL5 #MT5 #EA #Indicator
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π37β€25π¨βπ»9β‘1
An indicator is available that computes and displays the exponential moving average (EMA) of the true range, along with an offset of the EMA of its deviation. The implementation utilizes conditional compilation, ensuring compatibility with both MQL4 and MQL5 environments. Additionally, all source codes from previous CodeBase publications are now accessible in the "Public Projects" section of MetaEditor, identified by the name "FMIC". This resource offers developers a streamlined way to access, examine, and integrate advanced coding solutions into their projects, reinforcing efficiency in trading strategies.
#MQL4 #MT4 #Indicator #EMA
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#MQL4 #MT4 #Indicator #EMA
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π30β€13π¨βπ»7π3π€£3π1
Explore the use of evolutionary optimization methods in MetaTrader 5 for trading model development. Unlike gradient-descent, these methods don't require differentiable model functions. They are inspired by Darwin's evolution theory, utilizing concepts like heredity and natural selection. The genetic optimization algorithm is particularly effective for non-differentiable functions or those with explosive gradients. Practically, the MQL5 implementation allows for parallel model operations, enhancing resource management and optimization speed. This approach optimizes trading strategies by selecting top models and introducing probabilistic mutations, ensuring adaptability and innovation in algorithmic trading environments. Embrace advanced strategies for efficient, adaptive trading systems.
#MQL5 #MT5 #algorithm #AI
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#MQL5 #MT5 #algorithm #AI
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π61β€26β‘10π₯3π3π2π1
The Super Trend - Simple custom indicator has been enhanced to include multiple types of signal notifications. When the line color changes, users can now receive a sound alert, an email, or a push notification. This addition allows for timely updates and improved monitoring of market conditions. The ability to choose from various signal types offers flexibility in responding to market movements. This feature broadens the usability of the indicator by informing traders immediately of trend changes, thereby facilitating better decision-making and strategy adjustments. Adjust the notification settings according to personal preferences to optimize trading workflow.
#MQL5 #MT5 #Indicator #Alerts
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#MQL5 #MT5 #Indicator #Alerts
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