Discussing the article: "MQL5 Wizard Techniques you should know (Part 81): Using Patterns of Ichimoku and the ADX-Wilder with Beta VAE Inference Learning"
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Check out the new article: MQL5 Wizard Techniques you should know (Part 81): Using Patterns of Ichimoku and the ADX-Wilder with Beta VAE Inference Learning.
This piece follows up ‘Part-80’, where we examined the pairing of Ichimoku and the ADX under a Reinforcement Learning framework. We now shift focus to Inference Learning. Ichimoku and ADX are complimentary as already covered, however we are going to revisit the conclusions of the last article related to pipeline use. For our inference learning, we are using the Beta algorithm of a Variational Auto Encoder. We also stick with the implementation of a custom signal class designed for integration with the MQL5 Wizard.
Almost every trader has come to accept that markets move in cycles of optimism and pessimism, and yet we have very few out-of-the-box tools that capture these cycles with the consistency required for profitable trading. Recently, global markets have leaned toward bearish sentiment, with sudden selloffs and shallow rebounds becoming more frequent. In such a setting, mechanical strategies based on lagging indicator rules are bound to produce false signals, as volatility shakes out trades that would otherwise have followed through in calmer conditions.
The lack of out-of-the-box solutions points to the need to customize. This is where the IDE backed trading platforms stands out. Not only do they provide institutional-quality execution and charting, but they can come with system assembly wizards. These wizards, as in the case of MetaTrader, amount to a framework that allows traders to assemble an Expert Advisor (EA) quickly, even if they do not code complex trading logic from scratch. The Wizard’s real strength lies in its ability to integrate custom signal classes — which means that traders can inject advanced machine learning techniques directly into their automated strategies.
Among the available modern machine learning methods, Variational Autoencoders (VAEs) have gained attention for their ability to compress noisy, high-dimensional data into structured latent representations. Unlike a simple autoencoder, a β-VAE introduces a controlled penalty that encourages its hidden layer to capture disentangled, meaningful features rather than memorizing raw inputs. In financial trading, this translates to extracting the essence of patterns from streams of technical indicators, and less susceptibility to noise.
Author: Stephen Njuki