Machine Learning for Predicting the Stock Price Direction with Trading Indicators

Authors

  • Md. Siam Ansary Ahsanullah University of Science and Technology, BANGLADESH

DOI:

https://doi.org/10.53907/enpesj.v3i2.178

Keywords:

stock , dhaka stock exchange, yahoo finance api, machine learning, classification

Abstract

There are a number of possible advantages in utilizing trading indicators and machine learning to predict the direction of stock prices.  It is crucial to remember that stock price prediction is difficult by nature and that there is no way to ensure accuracy. The financial markets are extremely information-rich, dynamic, and complex. Large datasets may be processed and analysed by machine learning algorithms far more quickly than by people, which makes it possible to spot patterns or trends that might not be immediately obvious. By using historical price and volume data, the algorithms can be trained to identify patterns that could predict future moves. Because they offer insights into possible market moves, predictive models can help with risk management. Because ML models are always learning from fresh data, they can adjust to changing market conditions. This flexibility is essential in markets      where a variety of factors impact the market. Several machine learning models have been used in this experimental effort to monitor the direction of stock prices, and the outcomes are extremely encouraging.

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Published

2023-12-31

Issue

Vol. 3 No. 2 (2023): ENP Engineering Science Journal

Section

Article

License

Copyright (c) 2023 ENP Engineering Science Journal

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