Why we use Machine Learning/AI in our portfolio

For each asset and time frame considered for investment, our algorithms calculate the probability of making profits.

In order to determine their potential profitability, historical patterns, and trends are analyzed using machine learning. This is achieved through supervised learning. The model is trained on various indicators based on expert knowledge, enabling it to develop a robust, real-time model.

It’s worth noting the difference between human decisions and advanced computational power: while an individual might analyze 3-4 indicators simultaneously, the computational system can process all the indicators chosen for inclusion. A holistic view of price indicators allows us to make more measured decisions.

Lastly, models are designed by machine learning experts who use a multimodal regression to identify similarities in the current timeframe. This means we use multiple data sources to verify that the data we are using is accurate and a true representation of what is happening in the market.

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