Objective:

The goal of this project is to build an Expert Advisor (EA) Robot that automates trading by using predictions generated from a machine learning (ML) model. The EA will operate in real-time, leveraging both historical and live data to make buy/sell decisions. Data can be obtained via two primary methods:

  1. API: A free version that provides limited data for a single currency pair, and a paid version offering access to multiple pairs.
  2. Trading Platforms: FX Pro or XM, which will serve as direct sources for real-time trading data.

Additionally, the EA Robot will use an MQL5 script that defines the trading strategy, enabling automated trading on any MetaTrader 5 (MT5) platform. The core focus is to train an ML model for predictive trading, integrate data streams, and implement a scalable MQL5 strategy for execution.

Key Steps:

1. Project Setup

  • API and Platform Access:
    • Obtain API credentials for both free and paid versions.
    • Set up FX Pro or XM trading platforms and retrieve login details.
    • Ensure compatibility with MetaTrader 5 (MT5) for seamless integration.
  • Test API Connection:
    • Test API endpoints to verify data retrieval for currency pairs. This ensures real-time data flow for both historical and live trading.

2. Data Retrieval and Preparation

  • API Data Integration:
    • Set up API connections to retrieve historical and live trading data.
    • The free version will provide access to data for a single currency pair (e.g., EURUSD), while the paid version allows for multiple pairs (e.g., GBPUSD, GOLD, USDCAD, etc.).
  • FX Pro / XM Data Handling:
    • Integrate FX Pro and XM platforms for data reading and trade execution.
    • Use Python and MT5 libraries to retrieve real-time data for selected currency pairs.
  • Historical Data Storage:
    • Collect historical data for model training, storing it in a structured format (e.g., CSV) for further processing.

3. Model Development and Training

  • Feature Engineering:
    • Compute technical indicators (e.g., RSI, MACD, EMA) for selected currency pairs using historical data.
    • Generate buy/sell signals for training, ensuring the data reflects actual market patterns.
  • Model Training:
    • Develop and train the model using historical data to predict buy/sell signals by choosing appropriate ML model like ( XGBoost, LSTM, or Reinforcement Learning)
    • Focus on ensuring that the model can generalize well to unseen data and respond effectively to market changes.
  • Model Evaluation:
    • Evaluate model performance based on metrics like accuracy, precision, recall, and profit optimization.
    • Adjust and fine-tune hyperparameters for improved predictive performance.

4. Backtesting and Performance Evaluation

  • Backtesting Framework Setup: 
  • Develop a system to test the model on historical data.
  • Performance Metrics Definition: 
  • Define relevant metrics (e.g., Sharpe ratio, drawdown).
  • Strategy Backtesting: 
  • Run the model through historical data to evaluate performance.
  • Results Analysis: 
  • Analyze backtesting results and identify areas for improvement.

5. Real-Time Data Integration

  • Real-Time Data Handling:
    • Set up continuous data feeds via APIs or FX Pro/XM platforms, fetching real-time trading data at regular intervals (e.g., 15 minutes).
    • Ensure that the data is cleaned, preprocessed, and normalized on the fly for live prediction purposes.
  • Technical Indicators Calculation:
    • Compute technical indicators on real-time data, ensuring the model uses up-to-date market conditions.
  • Prediction Script:
    • Develop scripts to apply the trained model to real-time data and generate buy/sell predictions.

6. MQL5 Script Development

  • Strategy Implementation:
    • Develop an MQL5 script that includes the trading strategy based on model predictions are creates an interface between the AI system and trading platform.
    • The script will incorporate logic for stop-loss, take-profit, and position sizing, ensuring that risk management is integrated.
  • Platform Compatibility:
    • Ensure the MQL5 script is compatible with any MT5 platform for easy deployment.
  • Execution of Trades:
    • Implement a system where the EA uses the strategy to execute trades automatically based on the model’s predictions.

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This approach ensures the development of a robust, data-driven EA Robot that integrates ML predictions with real-time trading platforms and APIs. It also guarantees flexibility for live trading on various currency pairs and adaptability to changing market conditions.

Contact Details

This solution was designed and developed by Blackcoffer Team
Here are my contact details:
Firm Name: Blackcoffer Pvt. Ltd.
Firm Website: www.blackcoffer.com
Firm Address: 4/2, E-Extension, Shaym Vihar Phase 1, New Delhi 110043
Email: ajay@blackcoffer.com
Skype: asbidyarthy
WhatsApp: +91 9717367468
Telegram: @asbidyarthy

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