Introduction

If you’re looking to level up your automated trading game, this comprehensive MQL5 course is the perfect place to start. Over eight weeks, we’ll walk you through everything from the basics of MetaTrader 5 and coding fundamentals to advanced topics like developing neural networks and integrating machine learning. Whether you’re a complete beginner to trading automation or an experienced developer wanting to diversify your skill set, you’ll gain practical, hands-on experience with real coding exercises and structured, step-by-step guidance.

By the end of this course, you’ll be comfortable designing and deploying fully functional Expert Advisors (EAs), optimizing trading strategies, and harnessing the power of data-driven market analysis. Get ready to transform your approach to forex and CFD trading; and discover just how powerful MQL5 can be in creating robust, profitable algorithms.

Week 1: Introduction to MQL5 and MetaTrader 5 Basics

Objectives

Gain familiarity with MetaTrader 5, the MQL5 programming environment, and foundational coding skills.

Topics

  • MetaTrader 5 platform overview: interface, tools, and key elements.
  • MQL5 programming basics: syntax, data types, variables, and operations.
  • Working in MetaEditor: compiling, debugging, and code management.

Exercises

Setup Exercise: Install MetaTrader 5 and MetaEditor.

Testing Criteria: Successfully launch both applications and ensure access to the terminal’s main features.

Basic Script Exercise: Write scripts to manipulate variables (e.g., performing simple calculations).

Testing Criteria: Verify the script runs without errors and produces the correct outputs.

Week 2: Core Programming Fundamentals in MQL5

Objectives

Develop foundational programming skills essential for trading logic.

Topics

  • Control structures: conditionals (if, switch), loops (for, while), and basic functions.
  • Arrays, enumerations, and effective data handling.
  • Preprocessor directives and modular code using #include files.
  • Version Control and Modular Code Organization: Introduction to Git version control and best practices for structuring code into modules and reusable functions.

Exercises

Control Structures Exercise: Implement conditional checks and loops in scripts.

Testing Criteria: Validate correct branching and loop executions.

Trading Logic Script: Write a program to simulate basic trading logic with conditional checks.

Testing Criteria: Confirm accurate output based on trade conditions (e.g., “buy” or “sell” signals).

Version Control Setup: Organise the project with Git and modularize code.

Testing Criteria: Verify the EA functions correctly after modular restructuring, with a clear version history accessible.

Week 3: Advanced Data Types and Object-Oriented Programming (OOP)

Objectives

Master OOP concepts and use advanced data types to create flexible and reusable code.

Topics

  • OOP fundamentals: classes, inheritance, polymorphism, encapsulation.
  • Advanced data types: structures, pointers, and references.
  • Modular programming with custom class hierarchies and function overloading.

Exercises

Class Creation Exercise: Develop a class to manage order data, including attributes for entry price, stop loss, and take profit.

Testing Criteria: Instantiate the class and test all properties and methods for accuracy.

OOP-based EA: Implement a simple EA using custom classes for trade management.

Testing Criteria: Ensure the EA places orders correctly, with structured data flow and encapsulated trade logic.

Week 4: Building a Basic Expert Advisor (EA) and Event-Driven Trading

Objectives

Develop a fully functional EA with basic trading strategies and event-driven logic.

Topics

  • EA structure and event handling: OnInit, OnDeinit, and OnTick functions.
  • Order placement functions, trade execution, and event-driven programming.
  • Event-driven trading with OnTimer() for scheduling specific actions.
  • Building simple trading strategies like moving average crossovers.

Exercises

Moving Average EA: Create an EA for a moving average crossover strategy.

Testing Criteria: Confirm the EA executes buy/sell orders correctly upon crossover events in backtesting.

Timed Actions EA: Add functions to pause trading around specified times (e.g., low liquidity periods).

Testing Criteria: Verify that the EA suspends trading during specified times and resumes afterward.

Week 5: Intermediate EA Programming, Order Management, and Risk Management

Objectives

Enhance EA functionality with advanced order management, error handling, and risk management techniques.

Topics

  • Advanced order management: handling market and pending orders, including partial closes and scaling.
  • Stop loss, take profit, and dynamic trailing stops.
  • Error handling and optimization for various market conditions.
  • Risk and money management: position sizing, risk-per-trade calculations, and maximum loss limits.

Exercises

Enhanced EA Features: Modify the existing EA to include trailing stops, error handling, and position sizing.

Testing Criteria: Backtest to confirm trailing stops and error handling work as intended and prevent over-leveraging.

Scaling Strategy: Implement scaling in/out based on market conditions.

Testing Criteria: Confirm the EA scales trades according to predefined conditions.

Week 6: Technical Indicators, Multi-Timeframe Analysis, and Multi-Asset Trading

Objectives

Integrate and manage technical indicators, multi-timeframe data, and multiple assets in EAs.

Topics

  • Using built-in indicators (RSI, MACD) and applying them to trading strategies.
  • Developing custom indicators and using iCustom for indicator integration.
  • Multi-timeframe and multi-symbol trading for diversified strategies.
  • Correlation analysis between assets for better portfolio risk management.

Exercises

Multi-Indicator EA: Enhance the EA to use multiple indicators (e.g., RSI and MACD) for trade signals.

Testing Criteria: Test and validate indicator-based signals and trading accuracy across timeframes.

Custom Indicator Development: Create and backtest a custom indicator strategy.

Testing Criteria: Confirm the accuracy of custom indicator signals across different symbols and timeframes.

Week 7: Optimization, Strategy Testing, Logging, and Reporting

Objectives

Refine EAs for peak performance, reliability, and robustness through optimization, data logging, and statistical reporting.

Topics

  • Strategy testing and optimization in MetaTrader 5’s Strategy Tester.
  • Debugging techniques and log management for troubleshooting.
  • Logging trade data, generating reports, and calculating performance metrics (e.g., drawdown, Sharpe ratio).
  • Portfolio backtesting across multiple assets to evaluate risk and performance.

Exercises

Optimization Exercise: Run optimizations on the EA with different parameter ranges.

Testing Criteria: Document outcomes by evaluating results to identify the most profitable and stable parameters.

Logging and Reporting: Implement logging of key metrics after each trade and session, generating performance reports.

Testing Criteria: Verify data accuracy in reports and insights on EA behavior and effectiveness.

Portfolio Backtesting: Test the EA across multiple assets to assess diversification and cumulative portfolio performance.

Testing Criteria: Ensure portfolio backtesting is reliable and provides meaningful performance insights.

Week 8: Advanced Topics – Neural Networks, Machine Learning, and Data Preparation

Objectives

Introduce neural networks and machine learning applications in MQL5, integrating Python for advanced data processing and trading algorithm enhancements.

Topics

  • Introduction to neural networks in MQL5: building basic architectures and training models.
  • Integrating Python with MQL5 for machine learning enhancements and complex model training.
  • Data preparation and feature engineering: extracting and processing price data for ML models.
  • Creating and testing a simple neural network-based trading model in MQL5.

Exercises

Data Preprocessing and Feature Engineering: Implement scripts for scaling, normalizing, and generating meaningful features from price data.

Testing Criteria: Ensure data is correctly structured and ready for model training, with feature integrity validated.

Neural Network Model: Develop a basic neural network in MQL5 using predefined classes, focusing on market pattern recognition.

Testing Criteria: Backtest model performance on identifying patterns, with improvements noted in predictive accuracy.

Machine Learning Integration: Use Python to train a model on historical data and apply it in MQL5.

Testing Criteria: Assess trade accuracy and risk-adjusted performance improvements over traditional methods.