Explore an innovative approach to volatility prediction using MetaTrader 5 and Python integration for algorithmic trading. Discover a three-tier architecture where MetaTrader 5 efficiently streams market data, while Python employs libraries like Sklearn and XGBoost to identify volatility patterns, thanks to the property of stationarity. The Data Pipeline processes data for noise removal and metric calculation, ensuring optimized performance. The Analytics Core leverages machine learning for accurate volatility forecasts, outperforming complex models with simplicity. The Risk Advisor uses these forecasts for dynamic risk management adjustments. This system offers robust, adaptable trading strategy enhancements, bridging traditional indicators and modern analytics.

👉 Read | CodeBase | @mql5dev

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The FinMem framework, based on large language models, presents a novel approach to trading through its unique layered memory system. This system is divided into Working and Long-Term Memory components, managing short-term data and long-lasting information, respectively. The profiling module adjusts the agent's behavior based on user preferences and market conditions, while the decision-making module combines real-time data with stored memories to craft strategies that consider both short-term trends and long-term patterns. This is key to enhancing the accuracy and efficiency of investment decisions.

The FinMem framework's implementation in MQL5 introduces the CNeuronFinMem object, which reproduces the stratified data processing approach. The integrated algorithm manages data by processing an environment-state tensor and comparing it with a trading ...

👉 Read | AlgoBook | @mql5dev

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Part 25 of the MQL5 series introduces the development of an Expert Advisor (EA) to automate trading by identifying trend lines for signals. Unlike the previous focus on support and resistance zones, trend lines are used to assess market direction and breakout points.

The EA requires the user to manually draw two trend lines and name them appropriately. It can handle both breakout and reversal trades, setting stop-loss and take-profit levels based on the latest candle's data. By processing the last five candles, the EA detects if a price breaks or reverses from a trend line, ensuring relevant market moves. The use of trend line names and chart IDs enhances precise monitoring and engagement, avoiding unnecessary trades.

👉 Read | NeuroBook | @mql5dev

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Explore a revolutionary approach to cross-validation for time series in MetaTrader 5. Moving beyond classical methods, this article demonstrates how optimizing models with domain-specific validation techniques can effectively reduce overfitting. By training models on the most recent, relevant data rather than the full dataset, developers can enhance accuracy and reduce computational costs. Through empirical testing, it was found that using only 80% of the latest data yields the best results, challenging the traditional "more data is better" philosophy. This innovative strategy streamlines model training, ultimately benefiting developers with shorter development cycles and efficient resource utilization.

👉 Read | Signals | @mql5dev

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This article discusses the creation of an on-chart control panel for MetaTrader 5, targeting traders and developers interested in algorithmic trading. The focus is on building a user-friendly interface using MQL5 to automate lot size calculations and orders, enhancing speed and reliability. Readers learn how to assemble a static GUI layout, addressing key parameters such as order types, entry price, stop-loss, and risk per trade. The article details how to structure an Expert Advisor with essential functions like OnInit and OnTick. By following the guide, developers gain insights into crafting professional interfaces, improving both trading precision and interface usability.

👉 Read | Forum | @mql5dev

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In trading, coding is integral for developing systematic strategies, enhancing decision-making, and reducing emotional bias. The MetaTrader platform, widely used for trading, supports MetaQuotes Language (MQL) which facilitates the creation of algorithmic trading systems. MQL offers tools like Expert Advisors and custom indicators to automate trading based on predefined strategies.

Understanding the basics of MQL5 is pivotal. This includes variables, Boolean logic, loops, and conditional statements. These fundamentals help build logic into trading algorithms, paving the way for creating automated systems, such as a Simple Moving Average crossover strategy.

To practice, use a demo account to safely test and refine coding skills within MetaTrader's terminal without financial risk. Compiling and debugging are essential steps in the process, ensuring th...

👉 Read | Quotes | @mql5dev

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Explore the innovative world of algorithmic trading as we dissect the Markets Positioning Codex in MQL5, tailored for equity strategies with a long bias. This article delves into sophisticated signal patterns utilizing RSI and DeMarker oscillators, targeting Nvidia Corp. Our approach employs hidden divergences, slope breaks, and M/W formations to harness market trends and price confirmations, ensuring robust signal development. The meticulous design aims to outmaneuver MetaTrader 5's Strategy Tester intricacies, offering traders and developers insightful solutions for optimizing trades. Understand how discrete pattern combinations and adaptive testing regimes can significantly enhance trading algorithm efficacy and reliability.

👉 Read | Freelance | @mql5dev

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Analyzing and maintaining algorithmic trading strategies presents significant challenges for developers of all skill levels. Novices face difficulties calibrating moving average strategies, while experts continuously tweak complex models like deep neural networks. Both approaches have inherent limitations; machine learning models can be fragile, and human strategies often demand intensive manual configuration at the outset. Proposing an ensemble framework, this study combines supervised models with human intuition, leveraging shared technical indicators to enhance performance dynamically. This synergy optimizes system stability, minimizing the need for constant parameter adjustments and effectively transforming unprofitable elements into a cohesive, successful strategy.

👉 Read | AppStore | @mql5dev

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In Part 40, a Fibonacci Retracement trading system is developed in MQL5. This system calculates retracement levels using daily candle ranges or lookback arrays. It identifies bullish or bearish setups based on close versus open, triggering entries on price crossings of specified levels like 50% or 61.8%, with max trade limits. The implementation includes optional closures on new Fibonacci calculations, points-based trailing stops after a profit threshold, and SL/TP buffers as range percentages. The program is equipped with a visual representation, allowing for real-time monitoring. It combines flexibility and risk adjustment, suitable for traders looking to capitalize on pullback opportunities.

👉 Read | AppStore | @mql5dev

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Discover a cutting-edge approach to algorithmic trading with our comprehensive Delta-Gamma hedging framework on MetaTrader 5. By integrating robust Greek calculations directly into your Expert Advisor, you can automate the intricacies of Black-Scholes Greeks for dynamic portfolio management. Our system dynamically monitors delta exposure, leveraging high-frequency adjustments to maintain delta-neutral positioning while capitalizing on gamma-driven market shifts. Advanced risk management layers ensure precise execution, balancing between delta hedging and gamma scalping to capture short-term volatility. Experience enhanced trading precision and consistency with our automated solution, designed for traders focused on market-neutral strategies.

👉 Read | VPS | @mql5dev

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The ZigZag HH HL LH LL Pattern Label Indicator for MT4 presents an enhanced utility over the standard ZigZag tool by automatically detecting and labeling critical structural price points including Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), and Lower Lows (LL). This addition optimizes market structure analysis, pattern recognition such as Head & Shoulders and Quasimodo, and trend evaluation. The code comprises optimized functions compatible with MQL4, ensuring effective object management and timeframe tagging.

The indicator calculates the necessary ZigZag buffer for swings and line connections between points, essential for visualizing market trends. Its compatibility with dashboard scanners and multi-timeframe systems makes it an asset for manual traders, system architects, and EA developers seeking structured market insights. Practical a...

👉 Read | Forum | @mql5dev

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