Discussing the article: "MetaTrader 5 Machine Learning Blueprint (Part 2): Labeling Financial Data for Machine Learning"
Something I don't get:
If you train models with not the raw tick data but built bars(time, tick etc), do you have to build bars during live trading?
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Check out the new article: MetaTrader 5 Machine Learning Blueprint (Part 2): Labeling Financial Data for Machine Learning.
In this second installment of the MetaTrader 5 Machine Learning Blueprint series, you’ll discover why simple labels can lead your models astray—and how to apply advanced techniques like the Triple-Barrier and Trend-Scanning methods to define robust, risk-aware targets. Packed with practical Python examples that optimize these computationally intensive techniques, this hands-on guide shows you how to transform noisy market data into reliable labels that mirror real-world trading conditions.
Picture this: You're training to become an elite sniper. Would you rather practice shooting at perfect circles on a paper target, or train with human-silhouette targets that mimic real combat scenarios? The answer is obvious—you need targets that reflect the reality you'll face.
The same principle applies to machine learning in finance. Most academic research uses what's called "fixed-time horizon labeling", the equivalent of shooting at those perfect circles. This approach asks a simple question: "Will the price be higher or lower in exactly X days?" But here's the problem: real traders don't just care about where the price ends up. They care about the journey, that being when their stop-loss gets hit, when they should take profits, and how the price moves along the way.
Think of it this way: if you're building a model to predict whether someone will have a heart attack, you wouldn't just look at whether they're alive or dead in exactly 365 days. You'd want to know about warning signs, early interventions, and the sequence of events that are relevant for medical decisions. Financial markets work the same way.
This article assumes you already know your way around Python and have a basic grasp of machine learning concepts. We'll be diving deep into practical code and real-world applications that you can implement immediately.
Author: Patrick Murimi Njoroge