Stock price forecasting is done by studying past prices, news, social media updates, and global market trends. Company announcements, leadership transitions, and international events that have the potential to affect stock prices are also considered by experts. Leaving out any one of these factors can lead to forecasting errors. We have developed a system that interprets real-time data, presents informative charts, and predicts stock trends using technical indicators and machine learning. It is developed using Python's finance libraries and Streamlit for ease of use, helping traders make intelligent investment decisions.
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Akshat et al. (2025) studied this question.
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