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.
#MQL5 #MT5 #Bollinger #EA

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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.
#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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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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Highlighting high-volume bars can be an effective way to analyze market momentum and trend confirmation in trading. The approach involves coloring bars when their volume surpasses a standard deviation or a specified multiple of it. This technique leverages volume as a critical metric for confirming the break of significant price levels.

Incorporating volume analysis into your trading strategy can provide additional context to price movements and help in identifying potential strength or weakness in a market move. By quantifying volume deviations, traders can gain insights into market dynamics that may not be apparent through price analysis alone.

This method can assist in distinguishing between legitimate breakouts and false signals, providing a more comprehensive picture of market activity. It's a reliable tool to consider for technical analysis.
#MQL4 #MT4 #Algorithm #AI

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Dive into the intricate details of the Accelerator Oscillator (AC), a dynamic tool in algorithmic trading. Learn its calculation, interpretation, and application in strategic trading systems. Understand how to harness the AC for insightful momentum analysis, leveraging MetaTrader 5's MQL5 language. Explore three distinct strategies that utilize the AC indicator: Zero Crossover, AC Strength, and AC & MA. Each offers unique perspectives on market movements, facilitating the automatic generation of buying or selling signals. Ideal for traders and developers aiming to enhance their algorithmic trading capabilities, this resource offers a foundational approach to crafting robust trading systems.
#MQL5 #MT5 #Trading #Indicator

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An indicator has been developed to display the names of the days of the week directly on financial charts using text labels. Users can customize the settings via input parameters. This feature can enhance chart readability and provide temporal context. By aligning analysis with specific weekdays, traders and developers can streamline their review process. The integration of these labels into the chart interface can support more precise timing in strategy testing and historical data analysis. Such a tool can be beneficial for those needing to correlate trading patterns or events with day-specific data within technical analysis frameworks.
#MQL4 #MT4 #Indicator #Chart

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A new script enables users to display Strategy Tester reports directly on a chart. Here is the process: place the `report_into_chart.mql5` file in the `\MQL5\Scripts` directory. Compile the file to generate `report_into_chart.ex5`. Extract the `StrategyTester.zip` archive and move the `StrategyTester.html` into the `\MQL5\Files` directory. Refresh the Metatrader 5 platform. Execute the `report_into_chart` script within Metatrader. The deals will be visualized on the chart with blue arrows indicating buy positions and red arrows for sell positions. Users may substitute the `StrategyTester.html` with an alternative *.html report from the Strategy Tester at their discretion. This technique provides a visual representation of strategy evaluation data within Metatrader.
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Developers and traders exploring algorithmic trading can benefit from a unique take on the traditional moving average crossover strategy. By implementing equal-period moving averages—one applied to opening prices and the other to closing prices—this approach addresses the inherent lag in conventional methods. This setup is applied to the highly active EURUSD pair, offering insights for improving trading strategies. The proposed system leverages careful integration with MetaTrader 5's event-driven architecture, utilizing expert advisors and dynamic stop-loss adjustments through ATR. The result is a more responsive strategy with improved profitability over a volatile four-year period, showcasing the potential for versatile application across different indicators.
#MQL5 #MT5 #Strategy #AlgoTrading

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The article highlights advanced ensemble techniques for classification tasks. The emphasis is on combining models to enhance classification accuracy, with a focus on ordinal class rank outputs. These techniques are necessary due to the prediction instability in numeric-based classifiers. A key assumption is that component models are trained on datasets with exclusive and exhaustive class targets, allowing for either categorical class outputs or numeric scores.

Ensemble methods such as majority rule, Borda count, and model averaging are examined. The majority rule method focuses on the class receiving most votes. The Borda count captures full prediction spectrum by scoring classes based on their relative rankings. Averaging component outputs utilizies numeric values for enhanced ensemble performance, though it requires careful consideration of the outputs’ co...
#MQL5 #MT5 #Ensemble #ML

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