MQL5 MACHINE LEARNING: Code EAs with Hidden Markov Models

Name: MQL5 MACHINE LEARNING: Code EAs with Hidden Markov Models
Rating: 5.0 (2 reviews)

Master Algorithmic Trading: Build a Probabilistic Classifier using MQL5, Base-Encoding, and Hidden Markov Models

Created byLatvian Trading Solutions, Joy D Moyo

Last updated 3/2026

English

English [Auto],

What you'll learn

Master the mathematics of Hidden Markov Models and Log-Likelihoods to eliminate emotional trading.
Code a massive 10-year historical data-mining engine in MQL5 to extract institutional footprints.
Mathematically compress raw price action into 36 Elite Market States using Base-Encoding.
Build an automated Ensemble Expert Advisor that uses multi-timeframe voting to predict daily regimes.
Implement dynamic quantitative risk management that scales position size based on statistical confidence.
Replace traditional lagging indicators with predictive machine learning algorithms.

Course content

4 sections • 9 lectures • 1h 11m total length

Introduction6:03
Explore Markov chains and hidden Markov models to model market states, transition matrices, observations, and the two-layer architecture of hidden states and emissions.

Demonstrating HMM using a spreadsheet8:02
demonstrates hidden markov models in excel to visualize calculations and infer market regimes from hourly and 15-minute observations, with observed states and emissions for bullish, bearish, and inconclusive hidden states.
Analysing the script for price data collection8:54
Export mql5 data for excel, using rsi 14 and ema 50, label moves as bullish, bearish or inconclusive based on 20% range, and save hourly and 15-minute data to csv.
Building a hidden Markov model on a spreadsheet16:29
Build a hidden Markov model in a spreadsheet by loading data, analyzing it in Excel, and deriving state IDs from RSI divergence and EMA signals to drive a SPDT advisor.

Analysing the script that gets the hidden Markov Model trained10:43
Develop algorithmic trading system with a hidden Markov model to predict closing prices. Collect 2010–2020 data, validate 2020–2025, and build a probability matrix with Laplace smoothing using RSI and EMA.
Training the Hidden Markov Model with 10 year historical data5:53
Load the hidden Markov model data collector onto the chart and configure dates, RSI, AMA, and the inconclusive threshold; extract eight high-probability states to train the expert advisor.
Analysing the Expert Advisor code8:04
Analyze an expert advisor that demonstrates a hidden Markov model by using preloaded state probabilities from an Excel matrix to decide buy or sell trades within a daily window.
Testing the Expert Advisor6:10
Test and evaluate a hidden Markov model powered expert advisor across six years of data, examining trades, profits, and predictive edge.

Requirements

A basic understanding of trading strategy development

Description

Stop trading the shadows, and start trading the machine.

In the world of retail trading, most participants rely on lagging indicators and emotional guesswork. They react to the chaotic, random walk of price action, fighting a losing battle against institutional algorithms. But what if you could strip away the noise and mathematically identify the hidden forces driving the market?

In this unique, project-based course, you will learn how to build an elite, predictive algorithmic trading system from scratch using MQL5. We will move beyond traditional technical analysis and step into the world of quantitative finance. You will discover how to use Hidden Markov Models (HMM) and statistical frequency to calculate genuine, objective probabilities of future price action.

This course is designed for traders and developers who want to stop guessing and start operating with a mathematically proven edge. Together, we will build a complete quantitative discovery engine.

Throughout this course, you will learn how to:

Mine Historical Data: Build a massive 10-year data-mining engine in MQL5 to audit the markets and extract institutional footprints.
Master State Discretization: Mathematically compress raw price action into 36 discrete "Elite Market States" using Base-Encoding combinatorics.
Calculate Log-Likelihoods: Construct an Emission Probability Matrix to determine the exact statistical probability of future daily closes.
Build an Ensemble Expert Advisor: Code a multi-timeframe "Committee" that forces Micro, Intraday, and Macro trends to mathematically "vote" on trade execution, eliminating false signals.

Whether you are a seasoned algorithmic trader looking to integrate predictive machine learning, or an MQL5 developer ready to build institutional-grade systems, this course will provide you with a universal framework for market discovery.

Join me, and transform the way you view the financial markets.

Who this course is for:

Anyone willing to learn how to use Hidden Markov Models in developing trading algorithms

MQL5 MACHINE LEARNING: Code EAs with Hidden Markov Models

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 6min

Demonstrating Hidden Markov Chains on a spread sheet3 lectures • 33min

Building a Hidden Markov chain expert advisor4 lectures • 31min

Conclusion1 lecture • 2min

Requirements

Description

Who this course is for: