Coding

You are an experienced AI developer engineer assigned to design, review, and implement a comprehensive, production-level, multimodule Agentic Agent tool based on the conversation and code provided. Your task is to thoroughly analyze all given dialogue and associated code, then produce a fully detailed, production-quality implementation. If the resulting code or explanation is too extensive to provide at once, divide your response into clearly marked sections (e.g., "Section 1: [Module Name]"). Present one section at a time and wait for explicit confirmation before proceeding to the next section. Ensure each part includes detailed explanations, coding best practices, and necessary comments to facilitate understanding and maintenance. # Steps - Review the entire conversation and codebase provided. - Identify all modules and functional components required. - Design a modular, scalable architecture suitable for production environments. - Implement each module with clean, well-documented code. - Include error handling, logging, and configuration options as appropriate. - Summarize each section before delivery. - Await confirmation after each section before continuing. # Output Format - Provide the code in well-formatted, syntax-highlighted blocks with detailed comments. - Include an overview or explanation before each code segment. - Clearly label each section (e.g., "Section 1: Initialization Module"). - Await and respect user confirmation before proceeding to the next section. # Notes - Maintain clarity and completeness without overwhelming the user. - Emphasize production readiness including maintainability, performance, and security considerations. - If necessary, include instructions for deployment or integration. Begin by confirming your understanding and requesting the initial focus area or any additional context needed to start.

Create a detailed XML configuration file for a highly profitable and aggressive risk management bot designed for derivative trading. The bot should implement strategies that maximize returns while managing risk with advanced parameters suitable for aggressive trading styles. Consider including adjustable parameters such as trade size, leverage, stop loss thresholds, take profit levels, and risk-to-reward ratios, with emphasis on maximizing profits while controlling drawdown and exposure. Ensure that the XML structure is well-formed, logically organized, and can be easily modified or extended. Include comments within the XML to explain each parameter's purpose and typical value ranges to facilitate user adaptation. # Steps 1. Define the root element representing the bot configuration. 2. Specify general settings such as bot name, version, and trading instrument. 3. Detail risk management settings with aggressive yet controlled parameters. 4. Include trade execution parameters like entry/exit rules, leverage usage, and position sizing. 5. Add safety features to prevent catastrophic losses, such as maximum drawdown limits and emergency stop conditions. 6. Comment each key parameter for clarity. # Output Format Provide a single, complete XML file content as plain text, properly indented and commented, ready to be used in an XML-compatible bot platform for derivative trading. # Notes - Prioritize aggressive profit strategies balanced with robust risk controls. - Ensure compatibility with common derivative trading bot platforms. - Avoid overly conservative parameters that would limit profitability.

Create a detailed agentic AI prompt to develop a social media mobile application for iOS using React Native, targeting music and film media professionals. The app should facilitate connections, portfolio sharing, rental and listing of spaces or studios for music and film locations, and include a marketplace for equipment buy/sell/rent. Include the following detailed components: 1. User Profiles: Allow media professionals to create and showcase portfolios with multimedia content specifically for music and film industries. 2. Networking Features: Enable users to connect, follow, and message each other. 3. Space/Studio Listings: Functionality to list, browse, book, and manage rental of music and film production locations with detailed descriptions, availability, and booking features. 4. Equipment Marketplace: Allow users to list equipment for sale, rent, or purchase with secure transaction and messaging capabilities. 5. User Authentication and Security: Implement secure sign-up, login, and privacy controls. 6. Responsive and Intuitive UI: Design a user-friendly interface optimized for iOS devices. 7. Backend Integration: Include guidance for necessary backend services for data storage, image/media handling, and real-time updates. Ensure the prompt instructs the AI to prioritize modular, maintainable, and scalable React Native code implementation, including clear instructions for state management, navigation, and API integration. # Steps - Define high-level app architecture and core features. - Outline component hierarchy and screen designs. - Specify data models for users, listings, portfolios, and marketplace items. - Describe user flows for typical use cases: profile creation, listing spaces, renting equipment, and social interactions. - Emphasize secure authentication and data protection. - Request code generation for critical screens and components with React Native best practices. # Output Format Provide the full agent prompt in Markdown format (.md) suitable for feeding directly to an AI coding assistant. Structure the prompt logically with headings, bullet points, and explicit coding instructions. # Example ```md # Build iOS Social Media App for Media Professionals Design a React Native app targeting music and film industry professionals to connect and manage their portfolios, book studios, and trade equipment... [Full detailed prompt with architecture, features, and coding directives] ``` # Notes - Focus on iOS compatibility and React Native ecosystem. - Make sure the agent understands the domain-specific needs of media professionals. - Include requests for code comments and testing recommendations.

Create an extremely aggressive scalper trading Expert Advisor (EA) exclusively in MQL5 for the MetaTrader 5 platform, designed to trade only the XAUUSD symbol on the M1 (1-minute) timeframe. This EA should execute very fast and frequent trades to capture small price movements quickly and efficiently. The EA must meet the following requirements: - Use only MQL5 language and standard libraries. - Operate strictly on the XAUUSD symbol at the M1 timeframe; verify both on initialization and disable trading with user notification if the conditions are not met. - Employ highly aggressive scalping strategies focusing on rapid entries and exits that react swiftly to minor price movements. - Incorporate configurable risk management controls including stop loss, take profit, trailing stop, and lot size optimized for scalping. - Optimize for low latency and minimal drawdown, including safeguards to prevent overtrading and control risk exposure. - Structure the code modularly with well-defined functions and provide comprehensive inline comments throughout explaining variables, logic, methods, and risk controls for easy future adaptation. # Steps 1. Define and initialize input parameters: lot size, stop loss in points, take profit in points, trailing stop in points, and any scalping-specific settings. 2. Implement fast and responsive signal generation logic using appropriate MQL5 indicators or price action techniques tailored for aggressive scalping of XAUUSD on M1. 3. Develop robust order management functions to open and close positions immediately when signals trigger; handle order execution errors gracefully. 4. Introduce safety mechanisms such as limiting maximum open trades, restricting trading to certain times if needed, and enforcing a maximum daily loss. 5. Perform verification checks on EA start to confirm the symbol is XAUUSD and timeframe is M1; if not, disable all trading and notify the user. 6. Add detailed comments explaining each section of the code: variable purpose, trading logic, risk management, and safeguards. # Output Format Provide the complete MQL5 source code for the Expert Advisor (*.mq5 file) that compiles without errors and is ready to deploy immediately on MetaTrader 5. The code must include all setup, indicator calculations or price action logic, trade entry and exit handling, risk management, order execution and validation checks. Ensure all parts are thoroughly commented inline to facilitate straightforward understanding and modification in the future.

You are to create instructions for an Agentic AI agent that enables it to execute a specific Python script when it detects the keyword 'create dashboard'. The AI agent must strictly use only the data provided—no additional information, fabrication, or hallucination is allowed. The instructions should clearly define the keyword trigger, specify the exact conditions under which the script should be executed, and ensure that the agent understands it must not infer or add any information beyond the provided data. # Steps 1. Listen for the keyword 'create dashboard'. 2. Upon detecting this keyword, execute the given Python script exactly as provided. 3. Avoid any interpretation, modification, or fabrication—use only the data given without adding any new information. # Output Format Provide the complete, detailed instructions for the Agentic AI agent as plain text, formatted clearly so that it can be directly implemented to fulfill the task without ambiguity. # Notes - Emphasize strict adherence to the given data. - Do not include any extra steps unrelated to the execution of the script upon the trigger. - The instructions should be precise, actionable, and unambiguous.

Create a detailed design for an aggressive scalping Expert Advisor (EA) intended for trading on the 1-minute (M1) timeframe that integrates multiple advanced trading techniques: price action patterns, VWAP (Volume Weighted Average Price), order flow analysis, and Smart Money Concepts (SMC). The EA must meet these requirements: - Entry Conditions: Combine signals from price action, VWAP levels, order flow indicators, and SMC to identify high-probability trade entries. - False Signal Avoidance: Implement robust filters to minimize trades taken during false signals or market noise. - Initial Capital: Assume the starting capital is $20. - Risk Management: Employ precise money management techniques to protect the small capital, controlling risk per trade and overall exposure. - Trailing Stop: Use a dynamic trailing stop mechanism to lock in profits and reduce losses. # Steps 1. Detail how to analyze and integrate signals from price action, VWAP, order flow, and SMC collectively to generate entry signals. 2. Describe or pseudo-code the filters used to detect and avoid false or unreliable signals. 3. Explain the trailing stop logic suitable for a scalping EA on M1 timeframe, able to adjust as price moves favorably. 4. Define money management rules and risk parameters tailored to $20 initial capital, including position sizing and max allowed risk per trade. # Output Format Provide a comprehensive and clear description or algorithm outline that covers: - Entry logic and signal integration. - False signal filtering techniques. - Risk management and money management details. - Trailing stop implementation. Optionally include relevant pseudo-code or MQL4/MQL5 code snippets illustrating core parts of the EA logic. Ensure explanations clarify how the different methodologies (price action, VWAP, order flow, SMC) synergize in the EA logic. Focus on clarity, depth, and actionable details to enable precise EA development based on your design.

You are to create an AI agentic coder capable of building a complete website from scratch using Python. This AI coder must integrate multiple providers and models seamlessly. Additionally, design a comprehensive workflow divided into phases: planning, research, execution, testing (via command line), and automatic bug/error fixing. Incorporate and implement tools and pluggable components that enhance the AI agentic coder’s functionality and adaptability. # Steps 1. **Phase Planning:** Develop a detailed plan outlining the website’s features, architecture, and technology stack. 2. **Research:** Gather necessary information on multi-provider and multi-model integration techniques and choose appropriate libraries/frameworks. 3. **Execute:** Code the website with Python, ensuring integration with the chosen providers and models. 4. **Test Running:** Enable testing through command line interface, allowing for monitoring and validation of the website functionalities. 5. **Auto Fix Bugs/Errors:** Implement mechanisms for detecting, diagnosing, and automatically fixing bugs or errors found during testing. 6. **Tools & Pluggable Components:** Integrate relevant tools and design the coder to support pluggable modules to extend or customize functionality. # Output Format Provide a detailed plan and implementation strategy of the AI agentic coder, including: - Architecture design - Phase-wise workflow description - Example code snippets demonstrating multi-provider/model integration - Commands for testing in cmd and methods for auto bug fixing - Description or list of tools and pluggable components integrated Present the output in clear sections labeled accordingly for easy understanding and implementation.

Create an Expert Adviser (EA) in MQL5 that implements an aggressive scalping strategy based on the following detailed logic: - For a **buy** setup: - Detect when the current market price breaks above the high of the previous bullish candle. - Upon this breakout, place a **sell stop order** one tick below the breakout price. - When this sell stop order is triggered (activated), initiate a trailing stop that trails the price by one tick into profit. - The position must be closed immediately if the market moves two ticks against the trade (i.e., moves two ticks below the trailing stop for a buy setup). - The trailing stop should continue to follow the price until it is hit. - Once the trailing stop is hit and the market price moves above the original buy entry level, place a new sell stop order again one tick below the price, continuing the cycle. - For a **sell** setup: - Detect when the current market price breaks below the low of the previous bearish candle. - Upon this breakout, place a **buy stop order** one tick above the breakout price. - When this buy stop order is activated, initiate a trailing stop that trails the price by one tick into profit. - Close the trade if the market moves two ticks against the position. - The trailing stop should continue trailing until hit. - After the trailing stop is hit and the market price goes below the original sell entry, place a new buy stop order to repeat the cycle. Additional Requirements: - The EA must handle order placement, activation, trailing stop management, and closing of trades accurately in real-time. - Ensure the EA manages multiple trade cycles in a continuous loop according to the logic defined. - Implement appropriate safety checks to avoid code errors or unexpected behavior. - Optimize for aggressive scalping performance on relevant timeframes. # Steps 1. Identify the previous candle and check its bullish or bearish status. 2. Monitor for breaks above (for bullish) or below (for bearish) the previous candle's high/low. 3. Place the corresponding pending order (sell stop or buy stop) with one tick offset. 4. Upon order activation, monitor price movements to trail the stop by one tick. 5. Close the position if the price moves two ticks against the entry. 6. After closing, watch for the market to move beyond the original entry level and place the next pending order accordingly. 7. Repeat the cycle continuously. # Output Format Provide a complete, well-documented MQL5 Expert Adviser source code (.mq5) implementing the above scalping strategy, including comments explaining each critical section and logic. The code should be syntactically correct, ready for compilation and deployment on MetaTrader 5. Ensure all parameters, such as tick size or symbol properties, are dynamically obtained from the current chart context. # Notes - Use the market information functions to dynamically determine tick value and size. - Carefully handle order ticket management and check for order execution and modification results. - The EA must correctly handle stop order placement, trailing stop updates, and order close conditions within the OnTick() function or suitable event handlers. - Avoid hardcoding values; rely on dynamic symbol properties where applicable. - The trailing stop should move only one tick in profit increments, not more. # Examples No code snippets are provided here; the full working implementation is expected in the final output.

Create a detailed system prompt to guide the development of a very aggressive scalping Expert Advisor (EA) for trading, which includes a sharp trailing stop mechanism. This EA should focus on frequent, quick trades aiming to capitalize on small price movements with minimal exposure. The prompt should instruct the language model to: - Understand the key features of aggressive scalping strategies. - Incorporate a trailing stop that quickly adjusts to lock in profits while minimizing losses. - Consider typical parameters like entry criteria, stop loss placement, take profit levels, risk management, and trade frequency. - Suggest or include example pseudocode or algorithmic steps to illustrate the EA's logic. # Steps 1. Define aggressive scalping: frequent trades, small profit targets, short holding periods. 2. Explain the importance of a sharp trailing stop and how it protects profits. 3. Detail conditions for trade entry and exit. 4. Describe risk management rules. 5. Provide example pseudocode or algorithmic flow for the EA. # Output Format Provide the response as a detailed, structured plan or design document suitable for guiding the implementation of the aggressive scalping EA with sharp trailing stop. Use clear sections and bullet points, and include code snippets or pseudocode where relevant. # Notes Avoid overly general descriptions; focus on actionable and technical details relevant to creating an aggressive scalping EA with trailing stop functionality.

Create an aggressive sniper Expert Advisor (EA) for forex trading that executes trades with proper risk management. The EA should allow customization of lot size and the maximum number of concurrent trades. Each trade executed by the EA must display the label 'FXNeuron' visibly on the chart. Ensure that the EA incorporates sound risk management principles suitable for aggressive trading styles and that users can specify both the lot size per trade and how many trades can be opened simultaneously. # Steps 1. Develop trade entry logic focused on aggressive sniper trading strategies. 2. Implement risk management to control exposure according to input parameters. 3. Add customizable inputs for lot size and maximum number of trades. 4. Ensure that each trade placed has the label 'FXNeuron' clearly shown on the chart. # Output Format Provide a detailed explanation or code snippet of the EA logic, including risk management rules, lot size configuration, number of trades limit, and labeling mechanism. The explanation should be clear and suitable for implementation in an MQL4 or MQL5 environment.

Create an aggressive trading bot for MetaTrader 5 (MT5) that focuses on high-frequency trade execution and risk management strategies to maximize short-term profits. The bot should use technical indicators and real-time market data to identify entry and exit points quickly and should include features like dynamic stop losses, take profit levels, and position sizing to handle market volatility effectively. Key requirements: - Utilize MT5's MQL5 language and platform tools. - Implement high-frequency trading strategies based on technical analysis indicators (e.g., RSI, MACD, moving averages). - Incorporate dynamic risk management techniques such as trailing stops, stop losses, and adjustable take profit targets. - Enable customizable parameters for aggressiveness, risk tolerance, trade size, and indicator thresholds. - Ensure the bot can monitor and adjust positions automatically in response to market changes. # Steps 1. Define trading strategy based on chosen technical indicators suitable for aggressive trading. 2. Code the bot in MQL5, implementing technical indicator calculations and trade logic. 3. Integrate risk management rules including stop loss, take profit, and position sizing. 4. Test the bot in MT5 strategy tester with various market conditions. 5. Optimize parameters to balance aggressiveness and drawdown. # Output Format Provide the complete MQL5 code for the trading bot with detailed comments explaining the logic and use of technical indicators. Also include a summary explaining the strategy and instructions for parameter adjustments. # Notes Ensure the bot follows MT5 platform rules and includes safeguards to prevent excessive losses. Emphasize fast execution and responsiveness to market data while managing risks appropriate for aggressive trading.

Create a detailed specification and implementation plan for an aggressive trading bot designed for the XAUUSD (Gold vs. US Dollar) pair on the MetaTrader 5 (MT5) platform. The bot should focus on high-frequency or high-risk trading strategies aiming for rapid profits but also include risk management features to mitigate large losses. Key Requirements: - Strategy Description: Clearly explain the trading strategies used, such as scalping, momentum, breakout, or trend-following, and how aggressiveness is incorporated. - Technical Indicators: Include specific indicators or signals (e.g., RSI, MACD, moving averages) used to trigger trades. - Trade Execution: Describe entry and exit criteria, including stop loss, take profit, and possible trailing stops. - Risk Management: Incorporate money management rules, position sizing, and maximum drawdown limits. - Performance Metrics: Suggest metrics for evaluating bot performance like win rate, profit factor, maximum drawdown, etc. - MT5 Implementation: Provide guidance on how to develop and deploy the bot within MT5, including coding language (MQL5) considerations. Steps: 1. Define the trading strategy and aggressiveness level. 2. Choose and justify technical indicators. 3. Establish trade entry/exit rules and risk management parameters. 4. Outline backtesting methodology to validate the strategy. 5. Describe step-by-step implementation approach for MQL5 on MT5. Output Format: Present the response as a comprehensive, structured document including these sections: Introduction, Trading Strategy, Indicators Employed, Entry and Exit Rules, Risk Management, Backtesting Strategy, MT5 Implementation Plan, and Conclusion. Use clear, specific language suitable for a professional trading developer audience. Include examples or pseudocode snippets where helpful to illustrate concepts.