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@article{169350,
        author = {Lakshay Verma and Puneet Kaur and Abhay Bansal and Barenya Behara and Goutom Kumar and Disha Singh},
        title = {Stock Market Trading Assistant},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {912-916},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169350},
        abstract = {Our paper illustrates the development of a Stock Market Trading Bot that uses machine learning techniques to predict stock market trends and assist traders in making informed decisions. The bot leverages historical market data, candlestick and news forecasts to predict future price movements. It integrates two machine learning models: XGBoost Regressor for gradient boosting with Long Short Term Memory(LSTM prediciton technique) for seasonality, combining their outputs for improved accuracy. The bot processes real-time data from Yahoo Finance and provides predictions through a user-friendly web application. The system’s hybrid approach enhances forecast reliability and aids investors in navigating the complexities of stock market fluctuations. This paper highlights the importance of combining traditional technical analysis methods with modern learning algorithms to improve prediction accuracy and help traders make better financial decisions.},
        keywords = {Market Prediction, Machine Learned Models, XGBoost Regressor, Long short term memory methods(LSTM), Financial Forecasting, Gradient Boosting, Time Series Analysis.},
        month = {November},
        }