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Artificial intelligence (AI) is used in stock market trading to analyse large amounts of data and make decisions. This can increase the efficiency and accuracy of trading decisions and help traders react better to market changes.
One of the most common applications of AI in stock trading is the use of machine learning (ML), a subset of AI. ML algorithms can be trained to recognize patterns in data and make predictions and decisions based on this. An example of this would be an algorithm that analyses share prices and makes buy or sell decisions based on the recognized pattern.
Another example is the use of natural language processing (NLP) to analyze large volumes of news and social media to capture market sentiment and company news. This can help traders better predict the impact of news events on the market.
Another example is the use of neural networks (NN), which are a special type of ML algorithm inspired by the human nervous system. These algorithms can also be trained to recognize patterns in data and make forecasts and decisions.
AI systems in stock trading need to be able to process large amounts of data in real time and make decisions quickly. This requires powerful hardware such as GPUs (graphics processing units) and HPC (high-performance computing) as well as advanced algorithms and software.
It is important to note that AI systems in stock market trading are not perfect and can make mistakes. It is important to always compare the results of AI systems with other data sources and analyses to ensure that the decisions are correct.
Overall, the use of AI in stock market trading has the potential to increase the efficiency and accuracy of trading decisions.
