Discussing the article: "MQL5 Wizard Techniques you should know (Part 19): Bayesian Inference"

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Check out the new article: MQL5 Wizard Techniques you should know (Part 19): Bayesian Inference.

Bayesian inference is the adoption of Bayes Theorem to update probability hypothesis as new information is made available. This intuitively leans to adaptation in time series analysis, and so we have a look at how we could use this in building custom classes not just for the signal but also money-management and trailing-stops.

We continue our exploit of MQL5 wizard by reviewing Bayesian inference, a method in statistics that processes and updates probabilities with each new information feed. It clearly has a broad spectrum of possible applications, however for our purpose as traders, we zero in on its role in forecasting time series. The time series open to traders for analysis are primarily prices of the traded securities, but as we’ll see in this article, these series could be ‘expanded’ to also consider alternatives like security trade history.

In theory, Bayesian Inference should enhance market-adaptability of any trade system, since the re-assessment of any hypothesis in inherent. This should lead to less curve fitting when tested on historical data and subsequently given forward walks or live account drills. But that is the theory and in practice implementation can wreck a sound idea, which is why we’ll try to consider more than one possible implementation of Bayesian Inference for this article.

Our article, thus, is structured in a simple format that covers the definition of Bayesian inference, application examples that cover illustrations in a custom signal class, money-management class, and trailing stop-class; strategy testing reports, and finally a conclusion.

Author: Stephen Njuki