Discussing the article: "Low-Frequency Quantitative Strategies in MetaTrader 5 (Part 5): Pre-Backtest Evaluation of Machine-Learning-Generated Signals Through Formulaic Alphas"
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Check out the new article: Low-Frequency Quantitative Strategies in MetaTrader 5 (Part 5): Pre-Backtest Evaluation of Machine-Learning-Generated Signals Through Formulaic Alphas.
The article shows how to evaluate machine-learning alphas before a full backtest by expressing them as formulaic alphas. We compute Information Coefficient (IC), Rank IC, Information Ratio (ICIR), and t-statistics to quantify forecasting strength and stability. A MetaTrader 5 backtest illustrates differences versus execution-dependent tests, and a Python parser facilitates reproducible calculations and bulk screening.
Machine learning became part of retail traders' toolkits. However, after several years of widespread automated signal mining, a cost became evident in the early 2010s: techniques that once produced real edges began generating an increasing number of low-value signals. With many machines working 24/7 on the same data universe, the number of signals surged; most of them were weak or short-lived.
In 2016, in an effort to address these problems, Dr. Zura Kakushadze proposed a method to formalize predictive signals using an algebraic expression. He called them formulaic alphas. These algebraic expressions should facilitate combining dozens or hundreds of weak signals into a single “mega-signal” and make these machine-generated signals explainable and auditable.
This article explains how formulaic alphas are built and how they can be used to evaluate machine-generated trading signals without embedding them in a full trading strategy or running backtests.
Author: Jocimar Lopes