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Predicting Stock Price in Python Using TensorFlow and Keras

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  • Orlunwo Placida Orochi

    (Computer Science Department, Ignatius Ajuru University of Education)

  • Ledesi Kabari

    (Computer Science Department, Ignatius Ajuru University of Education)

Abstract

One of the most important practices in the financial world is stock trading. The act of attempting to forecast the future value of a stock or other financial instrument listed on a stock exchange is known as stock market prediction. This paper discusses how Machine Learning can be used to predict a stock’s price. When it comes to stock forecasts, most stockbrokers use technical and fundamental analysis, as well as time series analysis. Python is the programming language used to forecast the stock market. In this paper, we propose a Machine Learning (ML) method that will be trained using publicly accessible stock data to obtain intelligence, and then use that intelligence to make an accurate prediction. In this context, this research builds a neural network in TensorFlow and Keras that predicts stock market, which is basically a Python scraper that extracts finance data from the Yahoo Finance platform; more precisely, a Recurrent Neural Network with LSTM cells was constructed, which is the current state-of-the-art in time series forecasting.

Suggested Citation

  • Orlunwo Placida Orochi & Ledesi Kabari, 2021. "Predicting Stock Price in Python Using TensorFlow and Keras," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 8(6), pages 107-111, June.
    • Handle: RePEc:bjc:journl:v:8:y:2022:i:6:p:107-111
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    References listed on IDEAS

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    1. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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