Discussing the article: "Data Science and ML (Part 43): Hidden Patterns Detection in Indicators Data Using Latent Gaussian Mixture Models (LGMM)"

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Check out the new article: Data Science and ML (Part 43): Hidden Patterns Detection in Indicators Data Using Latent Gaussian Mixture Models (LGMM).

Have you ever looked at the chart and felt that strange sensation… that there’s a pattern hidden just beneath the surface? A secret code that might reveal where prices are headed if only you could crack it? Meet LGMM, the Market’s Hidden Pattern Detector. A machine learning model that helps identify those hidden patterns in the market.

Almost all trading strategies available that we use as traders are based on some pattern identification and detection. We examine indicators for patterns and confirmations, and sometimes we even draw objects and lines, such as support and resistance lines, to identify the market's state.

While pattern detection is an easy task for us humans in financial markets, it is challenging to program and automate this process because of the nature of the markets (noisy and chaotic).

Some traders have adopted to the use of Artificial Intelligence (AI) and machine learning for this particular task using various computer vision-based techniques which process images data similar to what humans do, as we discussed in one of the previous articles.

In this article, we will discuss a probabilistic model named Latent Gaussian Mixture Model (LGMM), which is capable of detecting patterns. Given the indicators data, we will explore this model's effectiveness in detecting hidden patterns and making accurate predictions in the financial markets.

Author: Omega J Msigwa