A utility EA has been enhanced with features including a specified symbol and magic number. A simple panel using OBJ_LABEL graphical objects displays total and percentage profit on a trading account for designated periods: daily, weekly, and monthly. The daily origin date is calculated based on the current day, adjusting for weekends to ensure the correct settlement day is used, such as Friday if it's Saturday. This setup also reflects transaction amounts over the respective periods, ensuring precise tracking and evaluation of trading performance. The panel provides a straightforward yet comprehensive view of financial activity and assists in decision-making and strategy assessment.
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The article discusses advanced techniques for improving a MetaTrader 5-based trading system, focusing on integrating a feature to move orders directly on the chart, an essential addition for dynamic trading environments. The author highlights the challenges experienced when extending the system to support various markets like Forex and futures on the B3 exchange. The solution involved refining MQL5 code to adapt order calculations based on market specifics, minimizing errors related to volume and multiplier discrepancies. The new system architecture leverages MetaTrader 5’s capabilities to handle dynamic order management efficiently, aiding developers in creating versatile and robust algorithmic trading tools without significant performance trade-offs.
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The indicator "Fractals on OBV" leverages the On Balance Volume (OBV) indicator to identify potential market turning points. Developed at the request of the community, this tool implements fractal analysis on OBV data. Fractals, representing specific patterns in data series, highlight possible support and resistance levels in trading activity. By integrating fractals with the OBV indicator, the tool enhances technical analysis by providing additional insight into volume flow trends. This approach aims to assist in identifying reversals and optimizing trading strategies through detailed volume analysis. Analyze market dynamics with improved accuracy using fractal-based methodologies on OBV data.
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Explore how to enhance MetaTrader 5 WinForms objects with interactive and animated features to create a more dynamic trading interface. The latest update enables mouse interactivity, allowing GUI elements like buttons to respond visually to user actions. By tracking mouse events and states, elements can adjust their appearance based on user interaction—such as changing color on hover or press. This functionality adheres to typical Windows OS behavior, providing a seamless user experience. Developers are guided through adding new properties, managing event handling, and modifying graphical object behavior to support real-time user interaction, enabling more complex algorithmic trading solutions.
#MQL5 #MT5 #WinForms #UIProgramming

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Developers working with MetaTrader 5 often encounter issues with symbol name mismatches when configuring Expert Advisors (EAs). Users commonly overlook the necessity to adjust symbol names according to broker conventions, leading to EA malfunctions. A typical scenario is when an EA is set to "EURUSD," but the broker uses variations like "EURUSD.i" or "EURUSD.m," causing functionality failures.

To address this, a reference script uses the Levenshtein distance algorithm to match configured symbols with those available in the Market Watch window. This method aids in automatically adapting EAs to broker-specific naming, ensuring smoother operations without manual configurations.

The script serves multiple purposes: it validates configuration during development, assists in testing EA robustness, and supports educational endeavors in MQL5 by demonstrating arra...
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The article explores a sophisticated approach to volatility estimation using the GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, which is invaluable for financial data analysis. Unlike traditional methods that assume constant volatility, GARCH accounts for time-dependent volatility, providing a more realistic framework for risk assessment. It covers technical facets such as parameter optimization via the MinBLEIC algorithm in the ALGLIB library, and introduces an adaptive iGARCH indicator for real-time volatility forecasting. This model improves risk management by offering precise volatility predictions and adaptable modeling options, crucial for traders and developers in algorithmic trading.
#MQL5 #MT5 #Volatility #GARCH

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This Expert Advisor (EA) implements a strategy involving a sequence of positions aligned in a single direction, determined by the initial trade, either BUY or SELL. Subsequent entries are made at the most favorable prices, with an incremental volume increase following a Martingale-like approach. The series of positions, exemplified with SELL orders, continues until specific financial thresholds are met. Closure of the position series occurs when either a predefined maximum drawdown (in monetary terms) is achieved, indicating a loss, or when a target profit (also in monetary terms) is reached. This approach allows for potentially enhanced profit opportunities while managing risk through set financial parameters.
#MQL5 #MT5 #EA #Martingale

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Analyzing automated trading algorithms reveals key insights on adapting strategies for robust financial trading. Despite sophisticated backtesting tools available, such reports rarely predict how algorithms perform during unpredictable market shifts. The algorithm's historical success doesn't guarantee future performance due to the dynamic nature of markets.

Distinguish between lazy, persistent, and unstable algorithms. Lazy algorithms may extend trade durations post-market shift, eroding profits. Persistent algorithms switch from profitability to losses without apparent notice. Unstable algorithms can incur significant losses with slight parameter changes, emphasizing rigorous testing to identify vulnerabilities.

Risk management is crucial. Always invest within your risk tolerance, diversify exposure, and maintain readiness for market unpredictability. Au...
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The BollingerBandsEA utilizes the Bollinger Bands indicator for trading decisions. It enters a buy order when the lower Bollinger Band is breached downwards and a sell order otherwise. Key integrated settings include: Magic number, Fixed volume, Percentage volume, Volume type, Risk for Position, Lots, Stoploss (in points), Trading duration after session start, and Trading end time before session closure. Additional features are available such as trailing stop, breakeven allowance, and specific profit factor criteria after stop adjustments. A condition exists for closing positions in a loss after a set duration. Usage is advised strictly on backtest or demo accounts to evaluate performance. Check for updates or revised versions before deployment.
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Pair trading offers a market-neutral strategy by taking advantage of correlations between trading symbols. The method relies on simultaneous buying and selling, enabling profit regardless of market direction.

Instruments with strong correlations are key, as demonstrated by pair symbols like EURUSD and GBPUSD, which exhibit predictable dependencies. Techniques such as spread calculation and symbol spread indicators enhance decision-making capabilities.

Seasonal analysis further refines spread trading by identifying repeating movement patterns over time. This element, though beneficial, requires careful monitoring due to potential disruptions from unforeseen events.

Success in pair trading is contingent on the meticulous selection of correlating instruments, balancing potential profit with minimal risk.
#MQL5 #MT5 #PairTrading #StatArb

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A series of indicators, "Color N Bars," assesses trends over a specified number of bars using the 'MA: trend N Bars' method. This tool visualizes trends by applying distinct colors for easier analysis. One variation, "MA On Stochastic Color N Bars," integrates moving averages with stochastic oscillators, enhancing trend identification and providing a clear representation of market direction. These indicators aid in assessing momentum and price movements, facilitating informed decision-making in technical analysis. The color-coded approach enables traders to quickly understand the trend dynamics, promoting efficiency in trading strategies and analysis.
#MQL5 #MT5 #Indicator #Trend

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The article explores the implementation of a refined risk manager class for MetaTrader 5, focusing on adaptive position sizing and profit-target features. It details enhancements to a risk management system, allowing smoother adjustments in position sizes when loss thresholds are met, and introduces parameters to halt trading upon reaching a set profit target—a feature beneficial for prop trading. A key innovation is using virtual positions to optimize profit calculations and manage drawdowns. The approach helps developers ensure strategies are robust under varying market conditions, enhancing both risk management and trading efficiency in algorithmic trading systems. Practical applications include improving strategy resilience and optimizing gains.
#MQL5 #MT5 #RiskManagement #TradingEA

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The following provides a straightforward MQL5 code example for an indicator designed to visualize spread[] data values, represented in points. These values are captured by the OnCalculate event handler. The spread data are consistent with those obtained through the CopyRates function when gathering MqlRates data. This example serves as a starting point for developers looking to monitor spread fluctuations accurately. The indicator can be customized further to suit various analytical requirements, allowing traders and developers to integrate spread analysis seamlessly into their trading strategies.
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Enhancements to the MQL5 Economic Calendar streamline news event tracking, improving user experience for traders and developers. New database views display past and upcoming events, while expanded expert input menus refine news filter options and stop order methods. These updates leverage previous code to boost strategy tester performance and manage trade slippage and stop orders. Key features include customizable news profiles, structured calendar components, and precise SQL handling for event data retrieval. These improvements offer practical benefits, allowing developers to create more responsive and effective trading strategies by efficiently managing economic news impacts.
#MQL5 #MT5 #News #AlgoTrading

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In 2019, the Artificial Electric Field Algorithm (AEFA) was introduced as an optimization algorithm inspired by natural phenomena. Originating from Coulomb's law, AEFA models agents as charged particles that exert electrostatic forces on one another. This innovative approach aims to address complex optimization challenges by leveraging the principles of attraction and repulsion analogous to electrical charges. Detailed equations have been formulated within this framework, incorporating elements such as the Coulomb constant, particle charges, force calculations, and motion updates, all essential for algorithmic implementation and effectiveness. AEFA's methodologies facilitate nuanced search space exploration, balancing global search and local optimization.
#MQL5 #MT5 #algorithm #AI

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A moving average indicator simplifies the analysis by calculating based on the currently viewed timeframe. It relies on the timeframe window and the defined moving average period. For instance, a chart on a 15-minute timeframe displays an Exponential Moving Average (EMA) on the close price with a period of 16. Conversely, a 5-minute timeframe chart may utilize an EMA period of 48 on the same price type. Despite differing timeframes, both charts maintain a consistent moving average relative to the underlying market structure. This showcases the adaptability of moving averages in providing insight across various timeframes.
#MQL4 #MT4 #Indicator #Trading

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Explore the use of Autoencoders in algorithmic trading. Delve into their architecture, comprising encoder and decoder blocks, which compress and reconstruct data efficiently. Learn how Autoencoders tackle tasks like data compression, pre-processing, and noise removal, often differing from PCA in their approach by using complex, non-linear transforms. Discover potential trading applications such as data pre-processing or market trend evaluation, leveraging Transfer Learning to extend model utility. Engage in practical experimentation with a simple autoencoder, normalizing data inputs for effective model training, optimizing market insights for traders and developers.
#MQL5 #MT5 #AITrading #Algorithm

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The following provides a straightforward MQL5 code example for an indicator designed to visualize spread[] data values, represented in points. These values are captured by the OnCalculate event handler. The spread data are consistent with those obtained through the CopyRates function when gathering MqlRates data. This example serves as a starting point for developers looking to monitor spread fluctuations accurately. The indicator can be customized further to suit various analytical requirements, allowing traders and developers to integrate spread analysis seamlessly into their trading strategies.
#MQL5 #MT5 #MQL #Indicator

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Enhancements to the MQL5 Economic Calendar streamline news event tracking, improving user experience for traders and developers. New database views display past and upcoming events, while expanded expert input menus refine news filter options and stop order methods. These updates leverage previous code to boost strategy tester performance and manage trade slippage and stop orders. Key features include customizable news profiles, structured calendar components, and precise SQL handling for event data retrieval. These improvements offer practical benefits, allowing developers to create more responsive and effective trading strategies by efficiently managing economic news impacts.
#MQL5 #MT5 #News #AlgoTrading

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In 2019, the Artificial Electric Field Algorithm (AEFA) was introduced as an optimization algorithm inspired by natural phenomena. Originating from Coulomb's law, AEFA models agents as charged particles that exert electrostatic forces on one another. This innovative approach aims to address complex optimization challenges by leveraging the principles of attraction and repulsion analogous to electrical charges. Detailed equations have been formulated within this framework, incorporating elements such as the Coulomb constant, particle charges, force calculations, and motion updates, all essential for algorithmic implementation and effectiveness. AEFA's methodologies facilitate nuanced search space exploration, balancing global search and local optimization.
#MQL5 #MT5 #algorithm #AI

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