A detecção de anomalias é um processo que identifica desvios da norma em conjuntos de dados. No trading de IA, isso envolve analisar grandes volumes de dados de mercado para encontrar padrões incomuns que podem indicar anomalias no mercado. Algoritmos de IA, especialmente aqueles baseados em machine learning, são treinados com dados históricos para reconhecer o que é um comportamento ‘normal’. Quando dados em tempo real são alimentados nesses sistemas, os algoritmos rapidamente marcam qualquer coisa que fuja dos padrões aprendidos. Essa capacidade é essencial no trading cripto, onde comportamentos de mercado voláteis e imprevisíveis são comuns. Ao detectar anomalias, os traders podem reagir rapidamente, potencialmente aproveitando movimentos repentinos do mercado ou evitando perdas, especialmente durante flutuações significativas.
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