Discussing the article: "MQL5 Wizard Techniques you should know (Part 95): Using Disjoint Set Union and Deep Belief Network in a Custom Signal Class"

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Check out the new article: MQL5 Wizard Techniques you should know (Part 95): Using Disjoint Set Union and Deep Belief Network in a Custom Signal Class.

For this article we switch to a custom MQL5 Wizard class that examines entry Signals. Our custom class is ‘CSignalDSUDBN’ this time around, and is coded by combining the Disjoint Set Union algorithm with a Deep Belief network. As has been the case throughout these series, our model is testable with MQL5 Wizard-Assembled Expert Advisors that can be tuned with different trailing stops and money management classes.

In the last MQL5 Wizard article that showcased Custom Signals, we used the pairing of a B-Tree Index algorithm and a Bayesian Network. The system we had then was meant for arbitrage by maneuvering inefficiencies over co-dependent assets. It showed some potential in classifying, multi-symbol market states. That model, though, could be unsuitable for single-asset traders that track momentum in volatile environments. Those who trade the news. A multi-asset index is not helpful in trading breakouts. For that, you could use a time-based noise filter. 

In realizing this, therefore, this article pivots to the Disjoint Set Union (DSU) algorithm that we pair with a Deep Belief Network (DBN). While the last custom signal article had the Bayesian model mapping static relationships, this approach uses generative DBN filters for noise. We are not in any way touting a universal solution; we are proposing an alternative approach to a specific issue some breakout traders face. How to group high-volatility bars into a cluster via a DSU and then identify if the cluster stands for a regime shift or a transient trap.

Author: Stephen Njuki