Discussing the article: "Self Optimizing Expert Advisors in MQL5 (Part 16): Supervised Linear System Identification"

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Linear system identifcation may be coupled to learn to correct the error in a supervised learning algorithm. This allows us to build applications that depend on statistical modelling techniques without necessarily inheriting the fragility of the model's restrictive assumptions. Classical supervised learning algorithms have many needs that may be supplemented by pairing these models with a feedback controller that can correct the model to keep up with current market conditions.

In our previous discussion on feedback controllers, we learned that these systems can stabilize the performance of trading strategies by first observing their behavior in action. We have provided a quick link to the previous discussion, here. This application design, allowed us to capture the dominant correlational structures that persisted across both winning and losing trades. In essence, feedback controllers helped our trading application learn how to behave optimally under current market conditions—much like human traders, who focus less on predicting the future and more on responding intelligently to the present.

The reader should note that, up to this point, our focus has been on feedback controllers that correct simple, rule-based strategies. This simple approach allowed the reader to immediately observe the impact the feedback controller made, even if it may have been the reader's first encouter with the subject matter. In Figure 1 below, we have built a schematic diagram of the application configuration to help the reader visualize the changes we are making today. 

Author: Gamuchirai Zororo Ndawana