Discussing the article: "A Grid Expert Advisor Based on a Cellular Automaton with Online Learning in MQL5 (Part III): A Living Feature Graph"

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Check out the new article: A Grid Expert Advisor Based on a Cellular Automaton with Online Learning in MQL5 (Part III): A Living Feature Graph.

The third article in this series introduces a trainable feature graph in the Cellular10K architecture: the weights of the feature → feature connections are strengthened online after correct predictions and weakened after errors. The article covers soft initialization, the message-passing step, a local Hebbian learning rule, weight clipping, normalization, and decay. The article demonstrates integration with a cellular automaton and a binary predictor, along with diagnostics metrics and practical activation thresholds for controlling overfitting.

In the previous part of this series, we converted the Cellular10K architecture from a conventional grid to a graph structure. The features are no longer just an array, feat[50], and the cells of the cellular automaton now have not only geometric neighborhoods but also logical connections based on features, strategies, and long-range small-world chords. This was an important step: the model began to think not in terms of a flat grid, but in terms of a network of interacting agents.

However, a fixed graph has a fundamental limitation. If we specify in advance that momentum is connected to trend strength, RSI to Bollinger Bands, and volatility to ATR, we are once again introducing a rigid assumption into the system. Yes, it is smarter than a regular grid, but it is still static. The market, however, is constantly changing: today, volume confirms a breakout; tomorrow, that same volume turns out to be a trap; today, the RSI acts as a reversal indicator; tomorrow, it simply shows the strength of the trend.

For this reason, the third step in developing the architecture is to make the graph trainable rather than fixed. Now, the connections between features are strengthened if, taken together, they lead to a profitable prediction, and weakened if their combined effect leads to an error. As a result, the system gains not only 10,000 adaptive cells but also a dynamic feature-space structure.

Author: Yevgeniy Koshtenko