The Nadaraya-Watson Envelope is a technical analysis tool that highlights price extremes over a chosen time frame. It operates by applying kernel smoothing techniques to estimate the price's underlying trend. Once this is established, the mean absolute deviations are calculated relative to the trend, creating an envelope that clearly marks the bounds of price fluctuations. This method provides traders with a visual representation of potential resistance and support levels, facilitating more informed decision-making in trading strategies. Developed by LuxAlgo, this technique has been implemented in MT5 for efficient trading analysis.
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In the realm of artificial intelligence and machine learning, feature selection stands out as a pivotal process for enhancing model efficiency by identifying and choosing relevant subsets of features from larger datasets. This method not only simplifies the model, leading to reduced computational costs, but also significantly boosts performance by focusing on significant features, thus improving accuracy and predictability. Notably, feature selection aids in model interpretability and handling noisy or less relevant data, thus safeguarding against overfitting.
Different methods facilitate this selection process. Filter methods, such as using a correlation matrix or conducting statistical tests (Chi-squared and ANOVA), evaluate features independently of any learning algorithms. Wrapper methods, like Recursive Feature Elimination (RFE) and Sequential Feature Selection (SFS), assess sub...
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Different methods facilitate this selection process. Filter methods, such as using a correlation matrix or conducting statistical tests (Chi-squared and ANOVA), evaluate features independently of any learning algorithms. Wrapper methods, like Recursive Feature Elimination (RFE) and Sequential Feature Selection (SFS), assess sub...
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In recent developments within timeseries data analysis, there is continued refinement in the detection of Price Action patterns on price charts, focusing particularly on the "Inside Bar" pattern. This pattern, a two-bar formation, requires analysis far beyond simple bar proportions, demanding a comparison of adjacent bars and potentially handling multiple nested formations within one larger pattern.
To support these complex pattern recognitions, enhancements have been made to the library classes' functionality. Notably, the addition of a "bitmap" object type allows for graphical delineation of pattern bars, enhancing visual representation and aiding in pattern identification directly on charts. Additionally, there's an introduction of a new variable in bar objects to store pattern types as bit flags, allowing multiple pattern identifications within a single variable.
These enhanceme...
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To support these complex pattern recognitions, enhancements have been made to the library classes' functionality. Notably, the addition of a "bitmap" object type allows for graphical delineation of pattern bars, enhancing visual representation and aiding in pattern identification directly on charts. Additionally, there's an introduction of a new variable in bar objects to store pattern types as bit flags, allowing multiple pattern identifications within a single variable.
These enhanceme...
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In the realm of algorithmic trading, Bollinger Bands stand out as a sophisticated technical analysis tool. Particularly in MetaTrader 5, employing Bollinger Bands through MQL5 allows traders to automate their trading strategies effectively. The methodology involves setting up the Bollinger Bands indicator to signal potential buy or sell opportunities based on market volatility.
This approach not only helps in identifying overbought or oversold market conditions but also aids in spotting potential price breakouts or reversals. The three components of Bollinger Bands include the middle band, which is the simple moving average, the upper band, and the lower band, which are respectively positioned above and below the middle band at a distance determined by the price's standard deviation.
For traders looking to develop an Expert Advisor (EA) in MQL5, understanding how to implement and mo...
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This approach not only helps in identifying overbought or oversold market conditions but also aids in spotting potential price breakouts or reversals. The three components of Bollinger Bands include the middle band, which is the simple moving average, the upper band, and the lower band, which are respectively positioned above and below the middle band at a distance determined by the price's standard deviation.
For traders looking to develop an Expert Advisor (EA) in MQL5, understanding how to implement and mo...
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Understanding MACD Indicators:
The MACD (Moving Average Convergence Divergence) is a popular tool used in trading and technical analysis. It provides signals of potential market direction changes based on momentum through two primary methods of interpretation.
The first method involves the MACD line crossing over the signal line. This is typically viewed as a hint towards a possible upward or downward trend. In the MACD_CW indicator, this is visualized by the red line crossing a price bar.
In the second approach, the MACD line crosses the zero line, suggesting a shift in momentum. This scenario is mirrored in MACD_CW when a silver line intersects the price bar, marking significant potential shifts.
Finally, a crossing of the signal line and zero on a standard MACD oscillator, which can signal trend strength, is paralleled in the MACD_CW indicator by the interaction between the red...
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The MACD (Moving Average Convergence Divergence) is a popular tool used in trading and technical analysis. It provides signals of potential market direction changes based on momentum through two primary methods of interpretation.
The first method involves the MACD line crossing over the signal line. This is typically viewed as a hint towards a possible upward or downward trend. In the MACD_CW indicator, this is visualized by the red line crossing a price bar.
In the second approach, the MACD line crosses the zero line, suggesting a shift in momentum. This scenario is mirrored in MACD_CW when a silver line intersects the price bar, marking significant potential shifts.
Finally, a crossing of the signal line and zero on a standard MACD oscillator, which can signal trend strength, is paralleled in the MACD_CW indicator by the interaction between the red...
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MQL5 provides developers with a diverse arsenal of loss functions, offering up to 14 different methods to quantify model error during machine learning training. These functions help in determining how far a model's predictions deviate from the actual target values which is crucial in supervised learning environments. Different loss functions suited for various applications can greatly impact the effectiveness of the training process.
For instance, the Mean Squared Error (MSE) is commonly utilized for tasks where larger discrepancies are more significant, as it tends to place heavier penalties on larger errors due to its squaring component. Comparatively, the Mean Absolute Error (MAE) is another prevalent loss function that avoids squared terms, making it less sensitive to outliers which can be particularly beneficial in certain datasets where large variances are less prevalent.
Movi...
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For instance, the Mean Squared Error (MSE) is commonly utilized for tasks where larger discrepancies are more significant, as it tends to place heavier penalties on larger errors due to its squaring component. Comparatively, the Mean Absolute Error (MAE) is another prevalent loss function that avoids squared terms, making it less sensitive to outliers which can be particularly beneficial in certain datasets where large variances are less prevalent.
Movi...
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This article presents an informative outline on utilizing trained large language models for developing trading strategies, particularly focusing on the foreign exchange currency pairs. The text emphasizes the necessity of a step-by-step progression beginning from strategy formulation, dataset creation, followed by model fine-tuning and inference. The article details various approaches to fine-tuning such as supervised, unsupervised, and reinforcement learning methods, exploring their respective utilities and challenges.
Specific techniques like Adapter-Tuning and Parameter-Efficient Prompt-Tuning are explored, providing insights into their effectiveness in adapting pre-trained models to specific tasks with reduced computational demands. The discussion extends to Prefix-Tuning and innovative methods including P-Tuning and LoRA, discussing their respective contributions to enhancing mo...
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Specific techniques like Adapter-Tuning and Parameter-Efficient Prompt-Tuning are explored, providing insights into their effectiveness in adapting pre-trained models to specific tasks with reduced computational demands. The discussion extends to Prefix-Tuning and innovative methods including P-Tuning and LoRA, discussing their respective contributions to enhancing mo...
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Explore the functionality and implementation of the Zeus Expert Advisor (EA) for automated trading on the MetaTrader5 platform. The Zeus EA utilizes key indicators such as the Relative Strength Index (RSI) and Moving Averages (MA) to generate precise buy and sell signals, thereby enhancing trading decisions and managing risks effectively.
Key features include:
- Utilization of a 7-day RSI to identify overbought (above 35) or oversold (below 15) market conditions.
- Application of a 25-period Simple Moving Average to assess market trends.
- Risk management tools like customizable stop loss and take profit settings, alongside a trailing stop feature for optimizing potential gains.
The discussion will also cover the setup of trading parameters that align with various trading strategies and risk preferences, which include specifications like trade volume and the adjustment of stop-loss ...
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Key features include:
- Utilization of a 7-day RSI to identify overbought (above 35) or oversold (below 15) market conditions.
- Application of a 25-period Simple Moving Average to assess market trends.
- Risk management tools like customizable stop loss and take profit settings, alongside a trailing stop feature for optimizing potential gains.
The discussion will also cover the setup of trading parameters that align with various trading strategies and risk preferences, which include specifications like trade volume and the adjustment of stop-loss ...
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Integrating Telegram with MetaTrader 5 expands the capabilities of traders by allowing real-time updates and interaction through a custom Expert Advisor (EA) using the MetaQuotes Language 5 (MQL5). This combination enables users to receive automated trading alerts and messages directly on Telegram, enhancing decision-making processes.
Setting up involves creating a Telegram bot via BotFather, which gives you an API token required for authentication. This token, along with the chat ID obtained through Telegram’s API, allows MetaTrader 5 to communicate over Telegram’s network.
Programming the EA involves configuring MetaTrader 5 to seamlessly send and receive messages by enabling web requests and linking to Telegram’s API. Clear explanations of each step, from obtaining API credentials to sending messages, support users in implementing and testing their automated systems.
By the end ...
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Setting up involves creating a Telegram bot via BotFather, which gives you an API token required for authentication. This token, along with the chat ID obtained through Telegram’s API, allows MetaTrader 5 to communicate over Telegram’s network.
Programming the EA involves configuring MetaTrader 5 to seamlessly send and receive messages by enabling web requests and linking to Telegram’s API. Clear explanations of each step, from obtaining API credentials to sending messages, support users in implementing and testing their automated systems.
By the end ...
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An Expert Advisor (EA) has been developed to automate the process of exporting the closing data of a specified number of candles, X, at regular intervals, every Z seconds. The primary function of this EA is to ensure seamless updates to a CSV file, which can then be readily accessed by Python scripts for further analysis and backtesting. This automation is designed to facilitate more efficient data handling and integration into trading strategies that require frequent data refreshes.
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The previously implemented EA feature allows for choosing a strategy option with either constant or variable position sizes. This supports result normalization according to the maximum drawdown and enables grouping strategies within specified limits. Manual selection of optimized strategy sets demonstrated effectiveness when combining them into multiple groups. However, scaling this approach can become labor-intensive.
The optimization process requires refining individual strategies with different criteria for each trading symbol and timeframe, including order types. From large parameter sets (20-50k), the top-performers for groups must be identified. Manually this is feasible only for a few levels, prompting the need for automation.
Automating group selection can start with testing the hypothesis that automation's quality meets or exceeds manual selection. By exporting optimization...
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The optimization process requires refining individual strategies with different criteria for each trading symbol and timeframe, including order types. From large parameter sets (20-50k), the top-performers for groups must be identified. Manually this is feasible only for a few levels, prompting the need for automation.
Automating group selection can start with testing the hypothesis that automation's quality meets or exceeds manual selection. By exporting optimization...
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In the realm of algorithmic trading, innovation is essential for staying competitive. A sophisticated Expert Advisor (EA) combines machine learning with traditional technical analysis for navigating forex markets. This EA employs an ONNX model alongside optimized technical indicators, such as SMA and EMA, to make informed trading decisions primarily for AAPL stock, but it is adaptable for other instruments.
Key features include dynamic lot sizing, trailing stops, and automatic market condition adjustments, creating a blend of modern technology and time-tested trading principles. The EA utilizes an ONNX model for price prediction, integrating trend-following techniques and adaptive parameter optimization. This multi-faceted approach considers both swift price changes and long-term trends.
The EA incorporates various technical indicators:
- **Simple Moving Average (SMA):** Uses an a...
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Key features include dynamic lot sizing, trailing stops, and automatic market condition adjustments, creating a blend of modern technology and time-tested trading principles. The EA utilizes an ONNX model for price prediction, integrating trend-following techniques and adaptive parameter optimization. This multi-faceted approach considers both swift price changes and long-term trends.
The EA incorporates various technical indicators:
- **Simple Moving Average (SMA):** Uses an a...
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Mean Reversion strategy is effective on daily timeframes for major forex pairs. It is recommended to test on a demo account initially. To avoid trading with increasing lot sizes when losing a trade, set "IncreaseFactor=0".
Inputs:
- Use_TP_In_Money: Enable Take Profit in money (true/false)
- TP_In_Money: Take Profit value (10-100)
- Use_TP_In_Percent: Use Take Profit in percent (true/false)
- TP_In_Percent: Take Profit percentage (10-100)
Money Trailing Stop:
- Enable_Trailing: Enable using money trailing stop (true/false)
- TP_In_Money: Take Profit in current currency (25-200)
- Stop_Loss_In_Money: Stop Loss in current currency (1-20)
Additional Settings:
- Exit: Close trades if trend is adverse (true/false)
- BarsToCount: Number of bars to count (1-20)
- Lots: Lot size (0.01-1)
- Lots_Size_Exponent: Lot size exponent (1.01-2)
- IncreaseFactor: Increase lots from total margin o...
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Inputs:
- Use_TP_In_Money: Enable Take Profit in money (true/false)
- TP_In_Money: Take Profit value (10-100)
- Use_TP_In_Percent: Use Take Profit in percent (true/false)
- TP_In_Percent: Take Profit percentage (10-100)
Money Trailing Stop:
- Enable_Trailing: Enable using money trailing stop (true/false)
- TP_In_Money: Take Profit in current currency (25-200)
- Stop_Loss_In_Money: Stop Loss in current currency (1-20)
Additional Settings:
- Exit: Close trades if trend is adverse (true/false)
- BarsToCount: Number of bars to count (1-20)
- Lots: Lot size (0.01-1)
- Lots_Size_Exponent: Lot size exponent (1.01-2)
- IncreaseFactor: Increase lots from total margin o...
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Ridge regression is a method to estimate coefficients of multiple-regression models when independent variables are highly correlated. It offers improved efficiency in parameter estimation in exchange for tolerable bias. Ridge regression reduces high variance by introducing a small bias, providing a more reliable model in the long run.
Lasso (Least Absolute Shrinkage and Selection Operator) performs both variable selection and regularization to enhance prediction accuracy and interpretability. In situations where covariates are collinear, lasso can introduce soft thresholding to manage coefficients.
Bias is the inability to capture the true relationship between variables. Low bias implies fewer assumptions; high bias means more. Variance indicates the difference from expected values, with high variance indicating overfitting. Balancing bias and variance is essential for accurate pred...
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Lasso (Least Absolute Shrinkage and Selection Operator) performs both variable selection and regularization to enhance prediction accuracy and interpretability. In situations where covariates are collinear, lasso can introduce soft thresholding to manage coefficients.
Bias is the inability to capture the true relationship between variables. Low bias implies fewer assumptions; high bias means more. Variance indicates the difference from expected values, with high variance indicating overfitting. Balancing bias and variance is essential for accurate pred...
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Attention developers and traders. This EA will terminate all active trades upon application. Ensure you are aware of your trading positions before deploying this Expert Advisor. Appropriate use of this tool can manage exposure and mitigate risk. Evaluate the implications for open positions in advance. Use this EA in environments where trade termination aligns with your broader strategy. For detailed implementation, review available documentation and consider testing in a controlled setting beforehand. Remember, strategic integration is key to leveraging automation effectively in your trading systems.
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The Gray Wolf Optimization (GWO) algorithm, developed in 2014, is a metaheuristic stochastic swarm intelligence algorithm inspired by the hunting behavior of gray wolf packs. The hierarchy among wolves includes alpha, beta, delta, and omega. Alpha wolves dominate, with betas and deltas aiding decision-making. Omegas, with the least influence, obey all higher-ranking wolves.
Mathematically, the alpha wolf represents the best solution, beta the second best, and delta the third. Omegas signify all other potential solutions. The algorithm's core stages, search, encircling, and attacking, help determine the optimal solution. During the search, alpha, beta, and delta wolves guide the pack toward the prey, while omegas continue to seek better solutions.
Encircling involves reassessing positions based on the fitness function, leading to refined prey location estimates. When attacking, wolv...
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Mathematically, the alpha wolf represents the best solution, beta the second best, and delta the third. Omegas signify all other potential solutions. The algorithm's core stages, search, encircling, and attacking, help determine the optimal solution. During the search, alpha, beta, and delta wolves guide the pack toward the prey, while omegas continue to seek better solutions.
Encircling involves reassessing positions based on the fitness function, leading to refined prey location estimates. When attacking, wolv...
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A new Trade Management Script has been released for opening market orders efficiently. Users can define essential trade parameters:
- Order Type
- Stop Loss (in pips)
- Take Profit (in pips)
- Lot Size
- Slippage
- Magic Number for Trade
- Price for Placing Pending Orders
This script facilitates precise trade execution by allowing detailed input specifications. Utilize this tool to streamline your trading process and enhance order management. For visual reference, an example can be accessed via the provided image. Focus on optimizing your trading strategy with these configurable options.
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- Order Type
- Stop Loss (in pips)
- Take Profit (in pips)
- Lot Size
- Slippage
- Magic Number for Trade
- Price for Placing Pending Orders
This script facilitates precise trade execution by allowing detailed input specifications. Utilize this tool to streamline your trading process and enhance order management. For visual reference, an example can be accessed via the provided image. Focus on optimizing your trading strategy with these configurable options.
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The complexity of financial markets is growing with technology. Over 95% of trading turnover is now generated by trading robots. Machine learning models are the logical next step, offering more robust trading systems than linear algorithms. Neural networks enable trading robots to analyze vast datasets, identify patterns, and forecast price movements effectively.
The development cycle of a trading robot includes stages such as data collection, processing, sample expansion, feature engineering, model selection and training, creating trading systems in Python, and monitoring trades. Python offers speed and flexibility in machine learning, with tools available to facilitate this process.
The project’s objective was to create a profitable machine learning model for trading using Python. The steps are:
- Collecting data from MetaTrader 5.
- Expanding the sample through data augmentation...
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The development cycle of a trading robot includes stages such as data collection, processing, sample expansion, feature engineering, model selection and training, creating trading systems in Python, and monitoring trades. Python offers speed and flexibility in machine learning, with tools available to facilitate this process.
The project’s objective was to create a profitable machine learning model for trading using Python. The steps are:
- Collecting data from MetaTrader 5.
- Expanding the sample through data augmentation...
Read more...
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The development of our Expert Advisor (EA) with a focus on economic data is progressing. Previously, we implemented a system to store economic data in a news calendar database and developed foundational classes for the expert's functionality. Today's update involves expanding these classes to facilitate trading based on economic data, aiming for future profitability.
Key updates include the addition of a new database view displaying unique events from the MQL5 economic calendar and new expert inputs for better filtering of economic data during trades, enhancing flexibility.
Notable UI Improvements:
- Concise, modern graphics with better responsiveness
- Display of terminal date and time highlights during news events
- Dynamic updates for news event details
Enhanced Filtering Options:
- New inputs for risk management, DST schedules, and news settings, providing granular control ove...
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Key updates include the addition of a new database view displaying unique events from the MQL5 economic calendar and new expert inputs for better filtering of economic data during trades, enhancing flexibility.
Notable UI Improvements:
- Concise, modern graphics with better responsiveness
- Display of terminal date and time highlights during news events
- Dynamic updates for news event details
Enhanced Filtering Options:
- New inputs for risk management, DST schedules, and news settings, providing granular control ove...
Read more...
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In the previous segment, a Telegram-integrated Expert Advisor was linked with MQL5. Initial steps included setting up the application and developing code to send and receive messages via Telegram. This foundational bot setup enabled straightforward MQL5 messaging to Telegram.
Proceeding to the next step, the focus shifts to transmitting trading signals using MQL5. This enhanced Expert Advisor opens and closes trades based on preset conditions while sending signals to a Telegram group chat. The trading signals have been refined for clarity and conciseness, ensuring nearly real-time updates.
We generate signals using the moving average crossover system. A simple method involving shorter-term and longer-term moving averages identifies potential buy or sell opportunities. Upon detecting a crossover, signals are formatted and sent to Telegram, including the asset name, crossover directio...
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Proceeding to the next step, the focus shifts to transmitting trading signals using MQL5. This enhanced Expert Advisor opens and closes trades based on preset conditions while sending signals to a Telegram group chat. The trading signals have been refined for clarity and conciseness, ensuring nearly real-time updates.
We generate signals using the moving average crossover system. A simple method involving shorter-term and longer-term moving averages identifies potential buy or sell opportunities. Upon detecting a crossover, signals are formatted and sent to Telegram, including the asset name, crossover directio...
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A new script has been introduced aimed at streamlining order management by closing all open orders simultaneously, excluding pending orders. This script is designed to enhance operational efficiency by allowing traders to execute a bulk closure of currently active trades.
By specifically excluding pending orders, it ensures that future strategies remain unaffected while addressing immediate risk management needs. This tool is especially useful for minimizing manual intervention during volatile market conditions.
Adopting this script can significantly reduce the time spent on individual order closures, thus providing a more efficient workflow. This method integrates seamlessly with existing trading infrastructure, making it a valuable addition to any trader's toolkit.
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By specifically excluding pending orders, it ensures that future strategies remain unaffected while addressing immediate risk management needs. This tool is especially useful for minimizing manual intervention during volatile market conditions.
Adopting this script can significantly reduce the time spent on individual order closures, thus providing a more efficient workflow. This method integrates seamlessly with existing trading infrastructure, making it a valuable addition to any trader's toolkit.
Read more...
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