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Como Encomendar um Robô de Negociação em MQL5 e MQL4
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30 - 3000 USD
50 - 100 USD
30+ USD
Termos de Referência
hi i am looking for a guy to impliment the following and give me a good ea,
---
### **1. Core Trading Logic**
- **Moving Average Crossover**:
- Uses a fast MA (e.g., 5-period) and a slow MA (e.g., 20-period) to identify trends.
- Buy signal: Fast MA crosses above Slow MA.
- Sell signal: Fast MA crosses below Slow MA.
- **Relative Strength Index (RSI)**:
- Identifies overbought and oversold conditions.
- Buy signal: RSI below the oversold level (e.g., 30).
- Sell signal: RSI above the overbought level (e.g., 70).
- **Average True Range (ATR)**:
- Used for dynamic stop loss and take profit levels.
- Adjusts risk based on market volatility.
---
### **2. Risk Management**
- **Dynamic Position Sizing**:
- Calculates lot size based on a percentage of account balance and ATR.
- Ensures consistent risk per trade.
- **Stop Loss and Take Profit**:
- Stop loss and take profit levels are based on ATR to adapt to market volatility.
- **Maximum Spread Control**:
- Avoids trading when the spread exceeds a specified threshold.
- **Slippage Control**:
- Limits the maximum allowable slippage for trade execution.
---
### **3. Advanced Features**
- **Multi-Timeframe Analysis**:
- Confirms trends using a higher timeframe (e.g., H1) moving average.
- Reduces false signals by aligning with the broader trend.
- **Trailing Stop**:
- Locks in profits as the trade moves in your favor.
- Adjusts the stop loss dynamically based on the specified distance.
- **News Filter**:
- Avoids trading during high-impact news events.
- Can be implemented using an external API (e.g., Forex Factory calendar).
---
### **4. Machine Learning Integration (Optional)**
- **ML Model for Trade Signals**:
- Uses a trained machine learning model (e.g., Random Forest) to predict trade directions.
- Features include MA, RSI, ATR, and other technical indicators.
- **Socket Communication**:
- Communicates with a Python server to fetch ML predictions in real-time.
---
### **5. Error Handling and Robustness**
- **Indicator Initialization Checks**:
- Ensures all indicators are properly initialized before trading.
- **Order Execution Checks**:
- Validates trade execution and handles errors gracefully.
- **Empty Control Statements**:
- Ensures all control statements (e.g., `if`, `for`) have valid code blocks.
---
### **6. Optimization and Customization**
- **Input Parameters**:
- Allows users to customize key parameters (e.g., risk percentage, indicator periods, stop loss distance).
- **Backtesting and Optimization**:
- Compatible with MetaTrader 5's strategy tester for backtesting and parameter optimization.
---
### **7. Code Quality and Structure**
- **Modular Design**:
- Separates core logic into functions (e.g., `CalculateLotSize`, `OpenOrder`, `TrailingStop`).
- Improves readability and maintainability.
- **Comments and Documentation**:
- Includes detailed comments to explain each component of the code.
- **Error Logging**:
- Prints error messages to the terminal for debugging.
---
### **8. Compatibility**
- **MetaTrader 5**:
- Fully compatible with the MetaTrader 5 platform.
- Uses MQL5's built-in functions and libraries (e.g., `Trade.mqh`, `Indicator.mqh`).
- **Python Integration**:
- Can be extended to integrate with Python for advanced features like machine learning.
---
### **9. Performance Considerations**
- **Efficient Indicator Calculations**:
- Uses `iATR`, `iMA`, and `iRSI` for efficient indicator calculations.
- **Low Latency**:
- Designed for scalping with minimal latency in trade execution.
---
### **10. Future Enhancements**
- **News Filter Implementation**:
- Integrate an external API to fetch high-impact news events.
- **Machine Learning Enhancements**:
- Train more advanced models (e.g., LSTM, XGBoost) for better predictions.
- **Multi-Currency Support**:
- Extend the EA to trade multiple currency pairs simultaneously.
- **Advanced Risk Management**:
- Add features like daily loss limits and maximum drawdown control.
---
### **Summary**
This EA is built with a focus on **scalping strategies**, **risk management**, and **adaptability**. It combines traditional technical indicators with optional machine learning integration for enhanced performance. The modular design and detailed documentation make it easy to customize and extend for specific trading needs.
Let me know if you need further clarification or additional features!
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Informações sobre o projeto
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