MQL5 Algo Trading
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Multivariate time series forecasting is a machine learning task focusing on predicting future trends from historical data. This task is difficult due to feature correlations and temporal dependencies and finds real-world applications in sectors like healthcare and finance. Transformer-based architectures, although impactful in NLP and computer vision, face challenges in time series forecasting due to training instability, especially with smaller datasets. The "SAMformer" framework addresses this by using simplified architecture, incorporating Sharpness-Aware Minimization and channel-wise attention to improve training stability and generalization. SAMformer optimizes Transformers to perform competitively by tackling entropy and loss sharpness issues, introducing novel strategies to enhance model efficiency and reliability.

πŸ‘‰ Read | Signals | @mql5dev

#MQL5 #MT5 #TimeSeries
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The 2 Moving Averages with Bollinger Bands indicator for MetaTrader 5 offers a comprehensive approach to technical analysis by merging two well-established tools. This combination facilitates traders' ability to pinpoint trend shifts and potential entry points. It features customizable fast and slow moving averages, allowing users to select period, calculation method, and price application. The indicator generates Buy and Sell signals based on moving average crossovers. Integrated Bollinger Bands provide clear volatility and price channel visualization, adjustable in multiple parameters. Additionally, the tool supports various alert systems, including chart alerts and notifications through email.

This indicator is adaptable to any symbol and timeframe, holding value in scalping, swing trading, or trend-following methodologies. Users can enhance setups...

πŸ‘‰ Read | Calendar | @mql5dev

#MQL4 #MT4 #Indicator
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The "2 Moving Averages with Bollinger Bands" is a custom indicator for MetaTrader 5 designed for technical analysis in trading. It combines configurable fast and slow moving averages with optional Bollinger Bands to generate real-time Buy and Sell signals. These signals are triggered by crossover events, with additional options for alerts through charts, sound, and email notifications. This indicator is versatile across all symbols and timeframes and is built on a non-repainting logic.

Key features include customizable periods and methods for the moving averages, such as SMA, EMA, SMMA, and LWMA, and the inclusion of upper, middle, and lower Bollinger Bands. Suitable for scalping, swing, or trend-following strategies, it aids in identifying trend reversals and entry points. Though it's a powerful tool, it's essential to pair it with sound risk mana...

πŸ‘‰ Read | NeuroBook | @mql5dev

#MQL5 #MT5 #Indicator
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In Part 5 of our MQL5 series, we focus on creating a rolling ticker tape for the Multi-Timeframe Scanner Dashboard. This feature allows for real-time monitoring of multiple symbols, displaying bid prices, spreads, and daily percentage changes dynamically, thus providing essential information swiftly without overwhelming charts.

The architecture involves structured scrolling lines for symbols, prices, and spreads, utilizing arrays for data and timers for smooth performance. Customization options include speed, coloring for price movements, and layout adjustments. We implement this in MQL5 by defining input parameters, creating text labels with specific fonts and sizes, and using bitmap images for symbols.

The update mechanism relies on timers instead of tick-based updates, ensuring consistent refresh rates. Functions manage background adaptation to charts' r...

πŸ‘‰ Read | CodeBase | @mql5dev

#MQL5 #MT5 #Ticker
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Explore the power of algorithmic trading with innovative techniques for combining strategies in MetaTrader 5. Harness the potential of a genetic optimizer to refine trading strategies collectively, boosting profitability through collaborative decision-making. However, this approach revealed an unexpected outcome: strategies with high correlation might inadvertently be selected, highlighting the need for revising selection methods. Delve into Python for data analysis using libraries like Pandas and scikit-learn, where statistical models can enhance strategy validation. Furthermore, leverage ONNX for neural network deployment, ensuring model independence and easy integration. Although initial results were suboptimal, careful revision promises enhanced trading performance through uncorrelated strategy analysis.

πŸ‘‰ Read | Forum | @mql5dev

#MQL5 #MT5 #Strategy
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An Expert Advisor showcases functionality for order placement followed by modifications to set Stop Loss and Take Profit values. It features a mechanism where any open order is closed precisely 30 seconds after execution. This behavior suits strategies that rely on predefined trade lifespan, using exact timing for entries and exits. The application of fixed-duration trades helps in managing exposure with time-sensitive precision. Automating such processes can aid in backtesting strategies reliant on short-term price movements, enhancing decision-making and operational efficiency.

πŸ‘‰ Read | VPS | @mql5dev

#MQL4 #MT4 #EA
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In the latest article, the development of a London Session Breakout System in MetaTrader 5 (MQL5) takes center stage. This system is designed to harness the volatility of the London market open by pinpointing pre-session price ranges and executing pending orders. It integrates advanced risk management tools such as risk-to-reward ratios, drawdown limits, and a real-time monitoring control panel. Detailed implementation steps are provided, including range calculation, order placement, and creation of a dynamic user interface for traders. The article culminates in a robust algorithm ready for backtesting and further refinement, offering significant practical benefits for traders and developers alike.

πŸ‘‰ Read | Forum | @mql5dev

#MQL5 #MT5 #TradeSystem
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Explore the intersection of algorithmic trading and machine learning with a focus on enhancing the Awesome Oscillator and Envelope Channels using advanced CNN techniques. This approach leverages a dynamic CNN architecture guided by dot product cross-time attention, optimizing kernel and channel sizes. The method captures dependencies across time, crucial for non-stationary financial data. While offering significant adaptability, it incurs computational cost and the risk of overfitting. Alternatives like dilated or adaptive convolutions are also considered for efficiency. Detailed Python implementations illustrate encapsulating Awesome Oscillator and Envelopes Channels, showcasing practical applications for traders and developers.

πŸ‘‰ Read | Forum | @mql5dev

#MQL5 #MT5 #Indicator
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Machine learning continues to evolve rapidly, propelled by data growth and complex algorithms. Optimization remains crucial, with the ADAM algorithm emerging as a particularly effective method since its introduction in 2014. It was developed by D. P. Kingma and J. Ba for optimizing neural network weights, owing to its adaptive moment estimates.

ADAM's design offers several advantages: simplicity, computational efficiency, low memory requirements, and independence from gradient rescaling. However, its reliance on analytical gradients limits its application scope. A new proposal suggests a population-based adaptation, extending its use beyond neural networks to broader optimization challenges.

This approach involves transitioning ADAM into a stochastic, population-based algorithm. The C_AO_ADAM class implements a version that integrates randomness and hybrid...

πŸ‘‰ Read | Calendar | @mql5dev

#MQL5 #MT5 #Algorithm
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An Expert Advisor evaluates the closing of the last five candles to identify trends and initiate a buy trade based on the analysis. Users can define stop-loss and take-profit levels in the input parameters to manage risk and potential profit. This setup provides traders with a structured approach to capture market movements by analyzing recent price action, leveraging programmable functions for systematic trading. It's a strategy that encapsulates decision-making in the forex market, emphasizing adaptability and user control over key trading metrics. The simplicity of input adjustments for SL and TP ensures customization for various trading environments and risk management preferences.

πŸ‘‰ Read | Calendar | @mql5dev

#MQL4 #MT4 #EA
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Dive into the world of MQL5 with Part 19 of the series, focusing on implementing the Wolfe Wave pattern in algorithmic trading. This segment transitions from theory to execution, teaching how to programmatically identify and trade the Wolfe Wave, a critical price reversal structure. Learn to detect key swing points, apply Fibonacci expansions, and draw crucial trend lines for pattern confirmation. Understand symmetry checks to ensure pattern integrity, while using MQL5 to label and visualize waves directly on the chart. Enhance your EA by implementing logic to confirm trades and manage risks effectively, ensuring reliable patterns and clear insights for traders and developers.

πŸ‘‰ Read | Calendar | @mql5dev

#MQL5 #MT5 #MQL5
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In Part 6, a Dynamic Holographic Dashboard is developed in MetaQuotes Language 5 (MQL5). It displays multi-symbol and timeframe indicators such as the Relative Strength Index (RSI) and volatility based on the Average True Range (ATR). Enhanced with pulse animations, sorting, and interactive controls, the tool optimizes traders’ analysis processes.

The dashboard architecture integrates real-time data with sorting, alerts, and visual effects, using arrays for data management and indicator functions. A centralized update loop ensures the UI remains responsive.

Implementation begins with defining metadata and inputs, incorporating arrays for symbol lists and error logging. Global variables manage data and indicators, employing classes for efficient object handling. Initialization and update functions maintain real-time data flow for strategic trading.

πŸ‘‰ Read | AlgoBook | @mql5dev

#MQL5 #MT5 #Dashboard
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The "Eliot Waves" EA targets trendlines via Zigzag for trading when an Elliot retracement occurs. It's equipped with Trailing Stop Loss, Take Profit, and is versatile across time frames, applicable to major forex pairs and NASDAQ stocks. Designed for execution at open candle prices, its parameter "IncreaseFactor" controls lot size increases after losses.

The EA integrates moving averages and trendlines, although only moving averages work in test mode. In live trading, they function concurrently, with the trendline acting as a filter. For accurate tests, employ the "open prices only" setting. This setup aims to enhance trade success probability.

Key inputs involve take profit settings in money/percent, trailing stop features, and drawdown control. Regular optimization every few months is advised, considering use as hedging grid EA or solo trade EA.

πŸ‘‰ Read | CodeBase | @mql5dev

#MQL4 #MT4 #EA
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A new indicator update enables easy tracking of when a price reaches a specified time across any candle or timeframe. Ideal for those monitoring market sessions or specific time zones, this tool aids in executing trades at precise times, e.g., 9.00 am. Version 1.1 introduces the day of the week feature, aiding in recalling the trading day. Customization options include line width, color, and position of the time text relative to the current candle. This update offers enhanced flexibility and convenience. Ensure it aligns with your trading strategies for optimal results.

πŸ‘‰ Read | Calendar | @mql5dev

#MQL5 #MT5 #Indicator
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The SuperTrend Indicator provides a robust tool for accurately detecting market trends, integrating seamlessly within price charts. Designed for traders across various styles, including scalping and swing trading, it adjusts for volatility using the Average True Range (ATR) and adapts swiftly to reversals.

Highlighted features include real-time trend updates, specific buy/sell signals with price line interaction, and built-in noise reduction. It functions across all timeframes and instruments such as forex, stocks, and cryptocurrencies, making it versatile for traders of all experience levels. The indicator can be paired with strategies like EMA or support/resistance approaches.

Key settings include the ATR period, multiplier, price type, line width, and customizable trend colors. For application, a closing price above the SuperTrend line implies a bul...

πŸ‘‰ Read | VPS | @mql5dev

#MQL5 #MT5 #Indicator
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Explore the evolution of trading analytics through Candle-Range Theory (CRT) in MetaTrader 5, a groundbreaking framework that defines market movements with precision. By categorizing each completed candle into Large-Range, Small-Range, Inside Bar, or Outside Bar based on ATR, CRT offers an innovative toolset. The CRangePattern class, CRT Indicator, and CRT Expert Advisor collectively enable advanced, non-repainting trading signals, with minimal CPU usage and full customizability. Suitable for diverse instruments, CRT’s modular architecture enhances strategy portability, empowering traders and developers to seamlessly integrate actionable insights into their trading systems while ensuring robust compatibility with future trading environments.

πŸ‘‰ Read | Docs | @mql5dev

#MQL5 #MT5 #Algorithm
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The exploration into enhancing a News Headline EA has revealed promising strategies for trading around high-impact news like Non-Farm Payroll releases. By developing a MetaTrader 5 Expert Advisor (EA), the goal was to automate post-news trading by observing historical chart data to understand market behaviors following such events. The EA utilizes algorithmic precision to highlight NFP release times, allowing traders to study and identify patterns in price movements. By leveraging custom logic for NFP event identification and dynamic back-testing, developers can refine entry setups that target sustained post-event momentum, enhancing strategy efficacy and precision for algorithmic trading solutions.

πŸ‘‰ Read | Quotes | @mql5dev

#MQL5 #MT5 #EA
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Python and MQL5 provide alternatives for task scheduling beyond repetitive tasks. In MQL5, OnTimer provides basic scheduling for functions, a method with limited flexibility for tasks requiring different time intervals. For algorithmic trading, Python's schedule module offers a human-friendly approach to scheduling tasks with more precision based on day or week timings. This module's advantage lies in its ability to handle various scheduling needs without sacrificing code simplicity.

MQL5 can adopt a similar structure, creating a system where schedules are managed with a class, allowing task execution based on user-defined intervals. This can be beneficial for trading activities that require precise timing for actions like opening trades or sending trading reports.

πŸ‘‰ Read | VPS | @mql5dev

#MQL5 #MT5 #Algorithm
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Traders who favor minimalistic charts can benefit from this lot size calculation script for Forex and CFD instruments. Choose between RiskByPercent for percentage-based risk or RiskByAmount for a fixed value. Enter your stop loss in pips suitable for 4-digit brokers; the script adapts for 5-digit brokers. If the calculated lot size falls below or above broker limits, the script adjusts to the nearest allowable value. Alerts provide details on the calculated lot size, risk amount, converted stop loss, and tick value. This tool aids in precise trade entry and can be customized or integrated with existing Expert Advisors for enhanced functionality.

πŸ‘‰ Read | NeuroBook | @mql5dev

#MQL4 #MT4 #Forex
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Efficient trade management is crucial for optimizing returns and minimizing risk. This script is designed for traders who prefer uncluttered charts, enabling precise lot size calculation for Forex or CFD trading. Choose between two options: RiskByPercent for managing risk by a percentage of equity, or RiskByAmount for a fixed monetary risk. Input your stop loss in pips, especially relevant for 4-digit brokers. The script intelligently adapts for 5-digit brokers and respects broker-imposed lot size restrictions. Upon calculation, it provides an alert detailing the lot size, risk amount, adjusted stop loss, and instrument tick value. This versatile script can be customized or incorporated into an existing Expert Advisor to align with specific trading strategies.

πŸ‘‰ Read | NeuroBook | @mql5dev

#MQL5 #MT5 #script
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