Serviço Freelance MQL5: Zaniva Trade

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Strategy Overview

The trading robot will implement an AI-powered trend-following strategy that confirms trades on the 4-hour time frame and executes entries on the 15-minute time frame.

Key Components

1. Trend Confirmation (4-hour time frame):

- Use a machine learning algorithm to analyze market trends and confirm trade directions.

- Integrate with technical indicators (e.g., MA, RSI, Bollinger Bands) for trend validation.

2. Entry Signals (15-minute time frame):

- Use a combination of technical indicators and AI-driven analysis to generate entry signals.

- Enter long positions when the AI model predicts an upward trend.

- Enter short positions when the AI model predicts a downward trend.

3. Stop Loss (SL) and Take Profit (TP) based on ETR (Expected Trading Range):

- Calculate the ETR based on historical price movements and AI-driven analysis.

- Set SL and TP levels according to the ETR.

4. Grid Strategy:

- Implement a dynamic grid system that adjusts to market conditions.

- Use AI to optimize grid size, spacing, and order placement.

5. Martingale Strategy:

- Implement a dynamic martingale system that adjusts position sizes based on AI-driven risk assessment.

- Use AI to optimize martingale multiplier and risk management.

AI-Powered Dashboard

1. Design Inspiration: Reference the Forex Gold Investor EA dashboard design.

2. Features:

- Real-time market analysis and trend predictions.

- Trade signal generation and execution.

- Dynamic grid and martingale system management.

- Risk management and position sizing.

- Performance metrics and analytics.

Technical Requirements

1. MQL5 programming: Develop the trading robot using MQL5.

2. Machine Learning Integration: Integrate a machine learning library (e.g., TensorFlow, PyTorch) or use MQL5's built-in AI capabilities.

3. Dashboard Design: Create a user-friendly and interactive dashboard with real-time data visualization.

Deliverables

1. MQL5 code: Provide the complete MQL5 code for the trading robot.

2. AI model: Deliver the trained AI model and any necessary libraries or frameworks.

3. Dashboard: Provide a fully functional dashboard with real-time market analysis and trading capabilities.

4. Documentation: Document the strategy's logic, parameters, and risk management features.

Development Considerations

1. Back testing: Perform thorough back testing to ensure the strategy's effectiveness.

2. Risk Management: Implement robust risk management features to protect against market volatility.

3. Scalability: Ensure the dashboard and trading robot can handle high volumes of data and trades

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