Latent Gaussian Mixture Model (LGMM) offers a structured approach to uncover hidden patterns in financial data through a probabilistic generative model. By clustering data based on latent variables, LGMM enables traders to identify underlying trends and integrate these features into machine learning models. A key highlight of LGMM is its ability to handle data generated from multiple Gaussian distributions while revealing insights that improve model accuracy.

In a financial context, LGMM can be applied to the indicators data, assisting in the discovery of market patterns not visible at first glance. Using the Expectation-Maximization algorithm, LGMM estimates latent variables to optimize clustering. When combined with a classifier model, such as Random Forest, LGMM provides a robust foundation for developing predictive models and trading robots.

However, LGMM's...

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