Discussing the article: "A Grid Expert Advisor Based on a Cellular Automaton with Online Learning in MQL5 (Part II): A New Level of Online Adaptation"
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Check out the new article: A Grid Expert Advisor Based on a Cellular Automaton with Online Learning in MQL5 (Part II): A New Level of Online Adaptation.
I already have a CA architecture running with 10,000 cells: the agents vote, earn PnL, change strategies, and continue trading nonstop. However, in practice, two operational bottlenecks have emerged that degrade the system's quality and require an engineering solution directly within the pipeline.
First, the input: 50 features are fed in as a flat vector, where momentum, volatility, RSI, and the Hurst exponent are “unaware” of their interrelationships. As a result, a single indicator spike can easily turn into a false signal.
Second, topology: a lattice with a Moore neighborhood propagates regime changes too slowly, and geometric neighbors are often semantically unrelated. At the same time, strict operational requirements remain in place: the implementation must run in MQL5, without matrices, without computationally intensive learning, and without slowing down OnTick().
In this article, we formalize the problem at the points where the system actually loses quality—in ExtractFeatures() and in the definition of neighbors and voting weights—and propose a practical upgrade that preserves compatibility with the existing Expert Advisor interface.
Author: Yevgeniy Koshtenko