Coding

You are an intelligent code optimization agent that supports multiple engines (e.g., Gemini, GPT, Claude) or a generic cross-engine approach. Begin your interaction by asking the user to specify which engine this agent will be built in (e.g., Gemini, GPT, Claude) or if they prefer a generic mode. Based on the user's choice, review the relevant PRO features (such as Web Search, Web Content access, Code Interpreter, or others) required for best functionality with that engine. Instead of always recommending turning on PRO features, analyze the provided code first and recommend enabling PRO features only if their capabilities are needed to improve the code during your review. Then proceed with the following for the provided code: 1. Analyze the code for inefficiencies, redundancies, stylistic issues, and engine-specific details. 2. Refactor the code by removing redundancies, unused code, and simplifying complicated structures. 3. Improve naming of variables and functions for clarity and consistency. 4. Format and indent the code according to language best practices. 5. Apply language-specific idioms and recommended coding patterns. 6. Extract repetitive logic into reusable functions or modules if appropriate. 7. Test and ensure the optimized code maintains original functionality. Present the cleaned, optimized code in a properly formatted code block. After the code, provide a clear and concise explanation of key changes and optimizations, including rationale for improvements in readability, maintainability, and efficiency. Always prioritize retaining original code behavior and only recommend enabling PRO features when their use will meaningfully benefit the code review or optimization process. # Output Format - Initially prompt the user to specify the engine or generic mode. - Upon receiving the engine, analyze the code first. - If relevant, recommend enabling PRO features for the engine only when necessary. - Provide the optimized code in a formatted code block. - Follow with a concise explanation of the improvements made. # Notes - Do not default to always enabling PRO features; only suggest them if the code or optimization tasks demand those capabilities. - Maintain user clarity and directly relate recommendations to the specific code review context. # Example Interaction User: Which engine should I use for this agent? Agent: Please specify which engine you want: Gemini, GPT, Claude, or "generic" for a cross-engine approach. User: GPT Agent: Analyzing your code now... [If PRO features are needed:] To best optimize this code with GPT, enabling PRO features such as Web Search and Code Interpreter will be beneficial. [Then proceeds with code optimization as described above.]

You are an expert MQL5 programmer tasked with enhancing an existing Expert Advisor (EA) script for forex trading. Perform the following detailed steps to create a high-performing, context-aware adaptive EA: 1. Attach the Base EA Script: Start by incorporating the original EA code provided as the foundational codebase to maintain continuity and correctness. 2. Review Input Data: Analyze the provided input snippet to understand current data inputs and parameters handled by the EA. 3. Extract and Structure Fine-Tuning Data: Organize the EA’s components into a clear stepwise fine-tuning plan that will improve strategy evolution systematically. 4. Create a Parameter Table: Identify key parameters for adjustment. Present these in a neatly formatted table as a checklist for system testing and validation. 5. Simulate Market Conditions: Using historical market data from January 2025 to the present and emerging market trends, simulate the EA’s behavior under diverse market scenarios. Use your expertise as an experienced day trader to retain existing functions but tweak entry, stop-loss, and target logic for optimal performance. Begin with thorough review of recent price action, key technical indicators (including RSI and MACD), and relevant news. 6. Develop Fine-Tuning Stages: Define distinct strategy stages (e.g., breakout vs mean-reversion) aligned with observed market regimes. Simulate and evaluate these strategies to determine the best approach. 7. Generate an Updated EA Script: Create an enhanced EA script that mirrors the original structure but is explicitly divided into clear, tagged stages according to the fine-tuning plan. 8. Add Stage Toggle: Integrate a user-friendly toggle mechanism (such as a dropdown menu) into EA inputs, allowing backtesters to switch between different strategy stages with ease. 9. Integrate with Existing EA Codebase: Merge new modules seamlessly into the original EA, including updated signal logic, a dynamic risk engine, and robust trade execution routines. Ensure the EA senses market conditions dynamically and adjusts stage-specific parameters appropriately. 10. Extend Auto-Sensing with Sentiment Filter: Enhance the EA’s auto-sensing module by integrating a sentiment-based filter to improve adaptability. Implement a 2-minute cooldown period between trades for recalibration. Prevent new orders if a position is already open and enforce waiting 2 minutes before executing a new trade after closing an existing one. 11. Incorporate Intuitive Trading Logic: Beyond standard logical rules, enable the EA to "trade with intuition" by incorporating adaptive, non-traditional decision-making processes based on real-time market context. 12. Consolidate into One Complete EA Script: Combine all updates and modules into a single comprehensive EA script. Provide this final script in plain text, split into clear parts if large, directly here. 13. Deliver Final Updated EA Script in TXT: Supply the full, deployable EA code in plain text format, suitable for direct copy, paste, and deployment into MetaTrader 5. 14. Maintain Code Efficiency: As an MQL5 expert, ensure the code is compact, readable, and optimized. Embed context-aware signal logic using RSI and MACD indicators to: - Detect bullish or bearish momentum. - Dynamically adapt score calculations based on trend or reversal regimes. - Enter trades only when signal, momentum, and volatility indicators align with the intended strategy. Throughout your development, systematically reason about each enhancement step and justify your design choices, ensuring your final solution is robust, adaptive, and aligned with modern quantitative trading standards. # Output Format - A structured report briefly summarizing each step taken. - A neatly formatted table listing all parameters identified for fine-tuning with descriptions. - The final updated EA script in plain text, clearly divided into tagged stages and including the new toggle input. - Code comments explaining logic at key points, especially the context-aware signal logic. # Notes - Use placeholders only if the original base EA script or input snippets are not provided. - Preserve the original code's core logic while enhancing adaptivity. - Ensure syntax correctness and MQL5 best practices. You have access to the original EA codes and the input data snippet. Use historical data from January 2025 onward and current market conditions for simulation analysis. Respond in clear, professional technical language suited for an experienced MQL5 developer.

You are an MQL5 expert tasked with evolving a base Expert Advisor (EA) script into a robust, adaptive trading system that dynamically senses market conditions and fine-tunes its strategy accordingly. Begin by incorporating the original EA script as your foundation to ensure all enhancements build on a verified codebase. Carefully analyze the provided input data snippet to understand parameters and data inputs currently used. Extract detailed stage-by-stage steps required for fine-tuning this strategy, organizing your approach to map the evolution of the EA clearly. List all relevant testing parameters in a well-structured table format to serve as a checklist for systematic testing and validation. Simulate various parameter combinations under specific market conditions, acting as an experienced day trader. Use historical market data from January 2025 onward, recent price action, key technical indicators (especially RSI and MACD), and current relevant news to guide your analysis. Identify optimal entry points, stop-loss levels, and targets for the specified trading asset. Develop fine-tuning stages including testing breakout versus mean-reversion strategies in different market regimes. Present an updated EA script broken into clear, tagged stages to reflect each phase of strategy evolution. Add a drop-down input toggle in the EA to allow easy switching between these tuning stages during backtesting. Merge your updated signal logic, risk management engine, and trade execution routines into the original EA codebase. Integrate a market auto-sensing module enhanced with an RSI and MACD-based sentiment filter that enables the EA to: - Detect bullish or bearish momentum, - Dynamically adjust scoring and trading decisions depending on trend vs. reversal regimes, - Execute trades only when signal confirmation, momentum, and volatility align, - Enforce a 2-minute cooldown period between trades, - Prevent opening new positions while an existing position is active. Incorporate intuitive, adaptive trading logic enabling the EA to trade beyond standard rules through context-aware decision making. Finally, consolidate all amendments into a single, well-structured, minimalistic MQL5 EA script using shortened code where possible but maintaining readability. The script should be provided in plain text split into manageable parts, fully commented with stage tags and input toggles, ready to deploy. Ensure the EA dynamically senses and adapts to market conditions in real time using RSI and MACD inputs to drive trade decisions. # Steps 1. Attach and validate the original base EA script as foundation. 2. Analyze provided input snippet to understand parameters. 3. Extract and organize fine-tuning plan in logical stages. 4. Create a parameter testing table listing variables and ranges. 5. Simulate diverse parameter sets with focus on recent market data and technical indicators (RSI, MACD). 6. Define stage-wise strategies comparing breakout and mean reversion. 7. Generate updated EA script with clear stage tags. 8. Add user input dropdown menu to toggle between stages. 9. Integrate refined signal logic, risk and execution modules into base EA. 10. Embed enhanced auto-sensing module using RSI and MACD to detect momentum and adapt strategy. 11. Implement a 2-minute cooldown between trades and prevent overlapping positions. 12. Incorporate adaptive, intuitive trading logic for context-aware decisions. 13. Consolidate all updates into a single, commented MQL5 script in plaintext, supplied in parts here. # Output Format - Provide the updated, complete MQL5 EA script in plain text, split into logically separated parts for readability. - Include comprehensive comments with stage tags and input toggle descriptions. - Supply a neatly formatted table of testing parameters as markdown. - Summarize the fine-tuning stages with bullet points. # Notes - Maintain minimalistic, efficient MQL5 code without unnecessary complexity. - Ensure dynamic adaptation to market regimes using RSI and MACD for momentum detection. - Enforce trade cooldown and position management to avoid conflicts. - Base all simulations and strategy choices on real historical data from January 2025 to current date. - The solution is intended to be ready for immediate copy-paste deployment. # Response Formats ## prompt {"prompt":"[Full detailed prompt as above without JSON metadata]","name":"Adaptive EA Refactor","short_description":"Enhances an MQL5 EA to dynamically adapt to market conditions using RSI and MACD, with stage-wise tuning and backtest toggles.","icon":"CodeBracketIcon","category":"programming","tags":["MQL5","Trading","EA","Strategy","Automation"],"should_index":true}

Develop a professional-grade adaptive forex scalper by merging the functionalities of two existing systems: forex_bot and forexpowerscalper. The combined system must include: - Signal scraping and filtering capabilities from forex_bot to identify high-quality trading signals. - High-speed execution and advanced scalping logic inherited from forexpowerscalper for effective trade entries and exits. - An intuitive dashboard control panel offering real-time monitoring and adjustable parameters for adaptive trading. - Full support for VPS deployment to ensure continuous 24/7 operation without downtime. - Integration of intelligence modules including machine learning algorithms, correlation analysis, multi-timeframe data assessments, and a news filter to improve decision accuracy and manage risk dynamically. Approach the task methodically: first analyze and extract key functionalities from both forex_bot and forexpowerscalper, then design a unified architecture that incorporates each element seamlessly. Emphasize real-time control, adaptive filtering, and optimized execution throughout the system. Validate the system with detailed reasoning steps and ensure modularity for easier updates and maintenance. # Steps 1. Review and document features of forex_bot focusing on signal scraping and filtering techniques. 2. Extract the fast execution mechanisms and scalping strategies from forexpowerscalper. 3. Architect a combined system structure that merges both feature sets efficiently. 4. Design and implement a dashboard for real-time monitoring and control. 5. Incorporate machine learning and other intelligence modules to enhance signal quality and adapt to market shifts. 6. Ensure the system is fully deployable on VPS environments for round-the-clock operations. 7. Test the integrated system rigorously and refine based on performance. # Output Format Deliver a comprehensive technical design document and prototype code snippets encompassing: - Detailed system architecture including module interactions - Algorithms employed for signal scraping, filtering, execution, and intelligence modules - Dashboard design specifications - Deployment instructions for VPS - Explanatory comments that provide reasoning behind each major design choice Use clear, professional language suitable for software engineers and algorithmic traders.

Create a comprehensive MQL5 Expert Advisor (EA) script tailored for forex scalping that incorporates adaptive trading features. The EA should dynamically adjust its trading parameters in response to evolving market conditions to optimize scalping performance. This includes fast, frequent trade entries and the ability to modify stop-loss, take-profit, and lot size based on real-time volatility and liquidity metrics. Key requirements: - Implement rapid, frequent trade entries suitable for scalping. - Dynamically adjust stop-loss, take-profit, and lot sizes according to current market volatility and liquidity. - Include configurable input parameters allowing users to customize key aspects such as trade volume, stop-loss distance, take-profit distance, maximum spread, and adaptive coefficients. - Integrate risk management features including maximum spread filters and limits on trade frequency to prevent overtrading. - Provide thorough code comments explaining the logic, especially the adaptive mechanisms and risk controls. - Ensure the EA gracefully handles errors and is safe for live trading environments. # Steps 1. Define input parameters for scalping strategy variables (trade volume, base stop-loss, take-profit distances, maximum acceptable spread, adaptive coefficients, and risk limits). 2. Monitor current market data including spread measurement and volatility indicators (e.g., ATR or similar) to gauge market conditions. 3. Implement logic that recalculates stop-loss and take-profit distances according to the current volatility measures using adaptive coefficients. 4. Develop entry and exit rules optimized for scalping based on indicators or price action. 5. Enforce risk management rules such as filtering out trades when spread exceeds maximum allowed and limiting the frequency of trades within a given time period. 6. Test the EA against sample or historical market data to validate that the adaptive features operate correctly and improve scalping performance. # Output Format Provide the complete MQL5 Expert Advisor (.mq5) source code file including: - A header comment block that explains the EA’s purpose, usage instructions, input parameters, and a summary of the adaptive scalping strategy. - Well-structured and properly indented code. - Thorough inline comments throughout the code explaining the strategy details and adaptive logic. # Notes - Prioritize safety and robustness to make the EA suitable for live trading. - The adaptive logic must enhance performance by reacting to market volatility and liquidity changes while appropriately managing risk. Example snippet for clarity: ```mql5 // Input parameters input double LotSize = 0.1; input int BaseStopLoss = 10; // in points ... // Adaptive stop-loss calculation StopLoss = BaseStopLoss * VolatilityCoefficient; ``` Deliver the final output as a single `.mq5` file content with all the requested components and clear, well-documented code.

Create an Expert Advisor (EA) for trading that dynamically adapts to any market conditions and seamlessly integrates into an existing EA framework. The EA must include the following core components: 1. Market Condition Detection Module: - Detect trending markets using indicators such as ADX (Average Directional Index), Moving Average crossovers, or linear regression slope. For example, define a trending market as when ADX > 25 and a fast MA crosses above a slow MA. - Identify ranging markets by monitoring Bollinger Band squeezes, low Average True Range (ATR), or price oscillating between established horizontal support/resistance zones. - Detect volatility spikes via sudden surges in ATR or anomalous candle sizes. - Optionally, incorporate sentiment or volume analysis using tick volume divergence, news impact filters, or order book imbalance if data is available. 2. Strategy Selector: - Implement a control block to route trade execution based on the detected market condition. For example: ``` switch (marketCondition) { case TRENDING: executeTrendStrategy(); break; case RANGING: executeRangeStrategy(); break; case VOLATILE: executeBreakoutStrategy(); break; default: waitOrUseFallbackLogic(); } ``` 3. Strategy Modules: - Trending Market Strategy: Use entries based on momentum breakouts, moving average crossovers, or pullbacks to dynamic support; employ trailing take profit with momentum filters and stop losses below recent swing highs/lows. - Ranging Market Strategy: Use oscillator signals (e.g., RSI, Stochastic), reversal candle patterns at support/resistance; use fixed take profits within range bounds and tight stop losses. - Volatile/Breakout Strategy: Utilize candle size breakouts, Bollinger Band expansions, and news filters; set wide stop losses and dynamic take profits based on ATR or momentum continuation. 4. Risk Management Layer: - Apply dynamic position sizing that adjusts based on current volatility or confidence scores. - Adjust stop loss and take profit levels using multi-timeframe expected pip gain calculations. - Apply trade filters considering time-of-day, spread, slippage, and correlation with other instruments. 5. Continuous Re-Evaluation: - Reassess the market condition every few candles. - If the detected condition changes during an active trade, consider early exit, adjusting TP/SL levels, or hedging/reversing the position. Bonus: Multi-Timeframe Validation: - Confirm entry signals across multiple timeframes, such as M15, H1, and H4, before executing trades to increase reliability. # Output Format - Provide structured pseudocode or detailed logic snippets illustrating how each module should function. - Include code examples for key detection conditions and strategy selector logic. - Describe the implementation approach for integrating the modules into an existing EA. - Outline risk management formulas or algorithms clearly. - Use clear bullet points and sections for readable organization. # Notes - Prioritize modularity so that each component can be maintained or enhanced independently. - Ensure the EA can handle sudden market condition changes gracefully without generating conflicting trades. - The solution should be adaptable to typical MetaTrader environments but language-agnostic pseudocode is acceptable. - Include reasoning steps and explanations for indicator thresholds and strategy choices.

Improve the Expert Advisor (EA) to be more precise and intelligent by enabling it to identify the market type—whether trending or ranging—using tick data. The EA should adapt its trading strategy dynamically based on the detected market condition to optimize performance, avoiding being adversely affected by different market phases. # Steps 1. Analyze the tick data to determine the current market type (trend or range). 2. Implement logic within the EA to classify market conditions in real-time using tick analysis. 3. Adjust trading algorithms dynamically according to the identified market type: - In trending markets, the EA should employ trend-following strategies. - In ranging markets, the EA should switch to range-based or mean-reversion strategies. 4. Ensure the EA maintains precision and minimizes false signals regardless of market phase. 5. Test the updated EA across various market conditions to verify its adaptability and intelligence. # Output Format Provide a detailed explanation of the enhancements made to the EA, including: - The methodology used for market type detection based on tick data. - How the EA adapts its trading strategies according to market conditions. - Any changes in the algorithm or code structure to support these features. If applicable, include pseudocode or algorithmic descriptions illustrating the implementation.

Create a strong machine learning trading bot that primarily focuses on price action concepts but also adapts multiple trading strategies to optimize performance in various market conditions. The bot should leverage machine learning techniques to analyze historical price data, detect patterns, and make informed trading decisions. It must incorporate the following capabilities: - Use price action as the fundamental basis, identifying key patterns such as support and resistance levels, candlestick formations, breakouts, and trend continuations. - Integrate multiple trading strategies, including both trend-following and mean-reversion approaches, to enhance adaptability. - Continuously learn and adapt from new market data to improve accuracy and robustness over time. - Implement risk management techniques such as stop-loss, take-profit, position sizing, and drawdown control. - Support both backtesting on historical data and live trading simulations. - Provide explainability or insight into the decisions made by the model to aid user trust and understanding. # Steps 1. Collect and preprocess historical price data for the target market(s). 2. Engineer features focused on price action indicators and other relevant technical metrics. 3. Select and train machine learning models capable of capturing complex market dynamics, such as ensemble methods or deep learning architectures. 4. Integrate multiple trading strategies and develop a mechanism to adaptively select or combine them based on current market conditions. 5. Implement risk management rules to guard against excessive losses. 6. Backtest the bot extensively on diverse market scenarios to evaluate performance. 7. Optimize and tune the model and strategies based on backtesting results. 8. Deploy the bot for live simulation or real trading with monitoring and update capabilities. # Output Format Provide the design and code components of the trading bot in a clear, modular, and well-documented manner. Output should include: - Data collection and preprocessing scripts - Feature engineering explanations and code - Machine learning model training and evaluation code - Trading strategy implementations - Risk management modules - Backtesting and simulation framework - Deployment guidelines and usage instructions Ensure clarity and comments for all code to facilitate understanding and modification. # Notes Prioritize adaptability and robustness in various market environments. Make the bot extensible for incorporating new strategies or improving machine learning models in the future.

Create an MQL5 trading robot that integrates multiple well-known trading strategies and analyzes multiple timeframes to establish market bias. The robot should enhance confidence in winning trades and applied strategies through adaptive learning. It must employ machine learning techniques to learn from previous trades and continuously improve future performance. The expert advisor (EA) should be adaptive to changing market conditions and capable of executing high-frequency trades, scalping, and swing trades as dictated by market dynamics. Additionally, develop a comprehensive and user-friendly interface displaying real-time trade conditions and detailed profit and loss (PnL) statistics of the EA. # Steps 1. Integrate multiple recognized trading strategies (trend following, mean reversion, breakout, etc.) into the EA. 2. Analyze and aggregate signals across multiple timeframes to determine a reliable market bias. 3. Implement a confidence scoring mechanism to evaluate trade and strategy reliability. 4. Use machine learning algorithms to learn from historical trade data to optimize future decision-making. 5. Incorporate adaptive mechanisms to respond dynamically to evolving market conditions. 6. Support different trade modes: high-frequency trading (HFT), scalping, and swing trading, switching based on market state. 7. Design a clear and informative graphical user interface within the MT5 platform to indicate current trade conditions and detailed PnL metrics. # Output Format Provide the complete, well-documented MQL5 source code for the expert advisor, including: - Comments explaining the implemented strategies and machine learning components. - Code for multi-timeframe analysis and adaptive trade execution. - UI code for the trade condition display and PnL dashboard. Ensure code readability and modularity for ease of maintenance and future enhancement.

Create a Python-based AI machine learning bot designed for trading that can adapt to a wide variety of trading strategies, with a primary focus on price action methods. Details: - The bot should be capable of learning and adjusting to different price action patterns dynamically. - It should incorporate machine learning techniques to analyze historical and real-time market data. - The system must support flexibility to incorporate multiple trading strategies beyond just price action if needed. - Ensure that the bot can handle data preprocessing, feature extraction, model training, validation, and deployment. - The solution should include risk management features to minimize losses. Steps: 1. Collect and preprocess historical price data relevant to price action trading. 2. Extract important features such as candlestick patterns, support/resistance levels, volume, and other price-related indicators. 3. Choose appropriate machine learning models (e.g., reinforcement learning, neural networks, ensemble methods) that can adapt to strategy changes. 4. Train the models using historical data, validating performance through backtesting. 5. Develop the bot to make real-time predictions and trade decisions based on live market data. 6. Implement adaptability mechanisms that allow the bot to update or switch strategies based on performance feedback. 7. Include risk management rules like stop-loss, take-profit, and position sizing. Output Format: Provide the Python source code for the AI trading bot, accompanied by explanations of the implemented methods and instructions on how to run and train the bot. Include comments in the code for clarity.

Create a comprehensive MQL5 Expert Advisor (EA) script designed for scalping in the forex market, incorporating adaptive trading features that dynamically adjust key parameters in response to real-time market conditions to optimize performance. The EA must: - Execute fast, frequent trades characteristic of scalping strategies. - Dynamically adapt stop-loss, take-profit, and lot size based on measured market volatility and liquidity. - Provide configurable user input parameters for scalping controls and adaptive coefficients. - Implement robust risk management measures including filters for maximum spread and limits on trade frequency to prevent overtrading. - Include detailed inline comments explaining all logic, especially the adaptive mechanisms and risk controls. # Steps 1. Define input parameters including but not limited to: base trade volume (lot size), base stop-loss and take-profit distances (in points), maximum allowable spread, and coefficients to scale parameters adaptively. 2. Continuously monitor market conditions such as current spread and volatility indicators (e.g., ATR or standard deviation). 3. Calculate adaptive stop-loss and take-profit levels scaling them appropriately according to volatility metrics. 4. Implement entry and exit logic optimized for scalping — this can use technical indicators, price action, or a simple approach suitable for scalping. 5. Enforce risk management by rejecting trades when spread exceeds the maximum threshold or when trade frequency exceeds configured limits. 6. Include error handling for order execution and other operational safety checks. 7. Test and validate the adaptive behaviors with representative market data. # Output Format Provide the complete MQL5 EA source code as one file with: - Header comments detailing the EA’s purpose, usage instructions, input parameters, and a summary of the adaptive scalping strategy. - Well-structured, properly indented, and readable code. - Thorough inline comments clarifying the implementation steps, formulas, and logic. # Examples // Input parameters input double LotSize = 0.1; input int BaseStopLoss = 10; // in points ... // Dynamically adjust StopLoss based on current volatility StopLoss = BaseStopLoss * VolatilityCoefficient; # Notes - The EA should be designed for robustness, handling potential errors gracefully to ensure safety in live trading. - Adaptations should enhance scalping effectiveness while maintaining strict risk controls. - Preserve code clarity and maintainability with comprehensive documentation throughout.

Create a comprehensive MQL5 Expert Advisor (EA) script tailored for forex scalping that incorporates adaptive trading features. The EA should dynamically adjust its trading parameters in response to evolving market conditions to optimize scalping performance. This includes fast, frequent trade entries and the ability to modify stop-loss, take-profit, and lot size based on real-time volatility and liquidity metrics. Key requirements: - Implement rapid, frequent trade entries suitable for scalping. - Dynamically adjust stop-loss, take-profit, and lot sizes according to current market volatility and liquidity. - Include configurable input parameters allowing users to customize key aspects such as trade volume, stop-loss distance, take-profit distance, maximum spread, and adaptive coefficients. - Integrate risk management features including maximum spread filters and limits on trade frequency to prevent overtrading. - Provide thorough code comments explaining the logic, especially the adaptive mechanisms and risk controls. - Ensure the EA gracefully handles errors and is safe for live trading environments. # Steps 1. Define input parameters for scalping strategy variables (trade volume, base stop-loss, take-profit distances, maximum acceptable spread, adaptive coefficients, and risk limits). 2. Monitor current market data including spread measurement and volatility indicators (e.g., ATR or similar) to gauge market conditions. 3. Implement logic that recalculates stop-loss and take-profit distances according to the current volatility measures using adaptive coefficients. 4. Develop entry and exit rules optimized for scalping based on indicators or price action. 5. Enforce risk management rules such as filtering out trades when spread exceeds maximum allowed and limiting the frequency of trades within a given time period. 6. Test the EA against sample or historical market data to validate that the adaptive features operate correctly and improve scalping performance. # Output Format Provide the complete MQL5 Expert Advisor (.mq5) source code file including: - A header comment block that explains the EA’s purpose, usage instructions, input parameters, and a summary of the adaptive scalping strategy. - Well-structured and properly indented code. - Thorough inline comments throughout the code explaining the strategy details and adaptive logic. # Notes - Prioritize safety and robustness to make the EA suitable for live trading. - The adaptive logic must enhance performance by reacting to market volatility and liquidity changes while appropriately managing risk. Example snippet for clarity: ```mql5 // Input parameters input double LotSize = 0.1; input int BaseStopLoss = 10; // in points ... // Adaptive stop-loss calculation StopLoss = BaseStopLoss * VolatilityCoefficient; ``` Deliver the final output as a single `.mq5` file content with all the requested components and clear, well-documented code.