trabajo MQL5: EA With deep Martingale Machinae Learning IA, time specific trading Work on H4

Trabajo finalizado

Plazo de ejecución 1 día

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Tarea técnica

Deep Martingale Machinae Learning IA (Intelligent Algorithm): The forex robot should be able to utilize machine learning algorithms for market analysis and price trend prediction. The system should intelligently manage risk while applying the Martingale strategy.
Time Specific Trading on H4: The robot should only execute trades during specific timeframes, such as on the H4 (4-hour) chart. It will focus on analyzing trends and trading signals within this timeframe to optimize trading performance.
Dynamic Risk Management: The system should be capable of adjusting risk levels over time and according to market conditions. When employing Martingale, the robot needs to automatically adjust investment capital and trade sizes to avoid undesirable risks.
Backtesting and Optimization: The system needs to support historical backtesting using past market data to evaluate the strategy's performance. Additionally, the robot should facilitate optimization by allowing parameter adjustments to find the best settings for specific market conditions.
Real-time Market Analysis: The robot should be able to monitor the market in real-time and respond promptly to new trading signals. It should utilize real-time data to make decisions based on constantly changing market conditions.
User-Friendly Interface: The robot should have a user-friendly interface that enables users to configure parameters and manage trading activities easily.
Risk Warning and Disclaimer: A risk warning and disclaimer should be provided alongside the robot to ensure users understand the risks of forex trading and the use of the robot.
Continuous Support and Updates: Ensuring ongoing support and updates are provided to ensure the robot remains effective in the face of changing market environments and user requirements.

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Información sobre el proyecto

Presupuesto

50+ USD

Para el ejecutor

45 USD

Plazo límite de ejecución

de 3 a 5 día(s)

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