Discussing the article: "MQL5 Wizard Techniques you should know (Part 98): Using an Unscented Kalman Filter and a Capsule Network in a Custom Signal Class"
Keep dip and silent
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Check out the new article: MQL5 Wizard Techniques you should know (Part 98): Using an Unscented Kalman Filter and a Capsule Network in a Custom Signal Class.
This article presents 'CSignalUKFCapsNet', as a custom class coded in MQL5. This class is meant to be used with the MQL5 Wizard when assembling an Expert Advisor and when selected in the Wizard it defines the Expert Advisor's entry signals. In building this custom class, we brought together the algorithm Unscented Kalman Filter and the Capsule Neural Network. Our algorithm is showcased with four operation modes, and the coding of this as a custom class for the MQL5 Wizard, allows testing with various Trailing Stop methods and Money Management systems.
In this ongoing series where we dive into advanced custom classes for the MQL5 Wizard, we are progressively constructing a toolkit that can be put to work in various market environments and situations. We build different class types that cater for: money management, trailing stops, and entry signals. In this series we have just covered the former two which means we now rotate back to signals where we look at a new implementation.
Within the articles where we handle custom signals, we have so far focused on: raw high-speed execution, where we paired bitwise vectorization with perceptron classifiers for markets that were driven by clear binary threshold states. We then covered the B-Tree indexing algorithm that we paired with Bayesian networks, and in this pairing, our aim was to be less deterministic in navigating less certain environments. Recently we explored the Disjoint Set Union algorithm that we paired with deep belief networks which was set up for traders with a discrete-view of market regimes looking to spot changes in volatility.
In principle we keep pivoting between deterministic models and those that depend on probability or that try to make sense of markets in more uncertain times, and in each approach we provide a new algorithm-network pairing.
For this article, we rotate from regime classification (determinism) and return to real-time state estimation where we are introducing a custom signal class that is run by the algorithm Unscented Kalman Filter (UKF) and is paired with a Capsule Neural Network. This expands the toolkit library of what is on offer in prototyping and developing trading systems within MetaTrader and as always, is not intended as a silver bullet. This model is meant to bring to the fold trade set ups that work by stripping away market-noise without adding the lagging problems of many indicators such as the moving average.
Author: Stephen Njuki