✨ What you'll learn in this masterclass
Build and deploy institutional-grade algorithmic trading models in MetaTrader 5
Train Random Forest and Machine Learning classifiers on financial tick & OHLC data
Completely eliminate lookahead bias and prevent overfitting with walk-forward matrices
Export trained models directly into native MQL5 Expert Advisors without external latency
Automate dynamic stop-loss, volatility-adjusted lot sizing, and multi-currency execution
Download and customize full source code templates (.MQ5, .EX5, Python scripts)
Course content
3 sections • 9 lectures • 3h 22m total length
Lesson 1 - Decision Trees
30:00
Lesson 2 - Random Forests
32:00
Lesson 3 - Visualizing a Random Forest in Excel
15:00
Lesson 4: Coding a Data Collection Script
20:00
Lesson 5 - Collecting Market Data
15:00
Lesson 6: Coding the Random Forest Model
25:00
Lesson 7 - Building a Random Forest Model
18:00
Lesson 8 - Coding the Random Forest EA
25:00
Lesson 9 - Testing the Random Forest EA
22:00
Requirements
- Basic understanding of financial markets, Forex/Crypto charts, and candlestick terminology.
- A PC or Virtual Private Server (VPS) running Windows 10/11 with MetaTrader 5 installed.
- No advanced machine learning background required — all model pipelines, parameters, and algorithms are taught from the ground up with copy-paste code.
Description
Every single day, financial markets generate millions of data points. For the average retail trader, this data is overwhelming, leading them to rely on simple, lagging indicators that fail to capture the true complexity of price action.
But in the world of quantitative finance, data is not overwhelming—it is the foundational building block for predictive modelling.
Hello everyone, in this course, I am going to teach you how to transition from traditional rule-based trading to dynamic, data-driven algorithmic execution using Machine Learning natively in MQL5.
Specifically, we are going to dive deep into one of the most robust and versatile machine learning algorithms available: The Random Forest.
This course is highly informative and strictly project-based. I have structured the curriculum to give you a deep and practical understanding of how machine learning models actually process market data. Here is exactly what you will learn how to build:
We will cover Historical Data Mining & Preprocessing were we will write MQL5 scripts to extract years of intraday market data, clean it, and structure it for machine learning analysis.
We shall put emphasis on exhaustive Feature Engineering, where you will learn how to mathematically transform standard price data into complex, predictive features that highlight volatility shifts, momentum exhaustion, and institutional liquidity.
You will learn how to build Decision Trees & use them for Ensemble Learning by breaking down the core mechanics of how decision trees split data, and how combining hundreds of these trees into a "Random Forest" prevents overfitting and creates a highly stable consensus model.
Finally, we will integrate our Random Forest into a fully automated MQL5 Expert Advisor. You will learn how to program dynamic position sizing, adjusting your capital exposure based on the underlying states of the markets.
Whether you are an experienced algorithmic trader looking to add machine learning to your toolkit, or an MQL5 developer ready to move beyond basic indicators, this course is packed with a lot of practical value. I will walk you through the logic, the mathematics, and every single line of code.
Expand your quantitative skillset and learn how to engineer intelligent trading systems. So click that Enroll now, and join me into the random forest.
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