Dr.n.r.ananthanarayanan

Experiences in developing stock market application using php and integrating data on machine learning for data analysis

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• Dr.n.r.ananthanarayanan

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In the direction of computer globalization and digitization, India is rapidly developing education and information technology. People are taught how to invest in deposits, postal investments, government bonds, gold systems and bonds, and the private sector. The world in which we now live has been completely transformed by technology. The study indicates that there are more than 4 billion active Internet users worldwide, or nearly half of the world's population. Our lives are now faster, easier to manage, and more enjoyable thanks to modern technology. This paper focuses in experiencing and developing a stock application using PHP, React JS, NodeJS and CSS . All the stock data is stored in a MYSQL database. On the other side for developing machine language application python code is used to convert the data into csv format for machine learning algorithms. The investor is presented with a login screen in the python environment where they must enter their user name and password. The stock dashboard shows the investor's current stock holdings, as well as online stocks' their current price, percentage change in stocks, sensex, nifty, bonus, rights, IPO's, annual report etc. statistical methods are used as software modules for the investor, and with a single click of a button, they can compare and contrast their own stocks with online stocks, as well as the trend in the stock market position in order to decide whether to buy, hold, or sell the stocks. Data visualization component is used for comparison of various stocks, and by clicking of a button, stock prediction are displayed whether to hold, buy or sell in future according to the market trend. The trader must log in using their user name and password. The trader will browse the client current market price of all stocks, buying and selling stocks, contract note, client margin, e-off market transactions, ledgers, journals, commission of buying and selling stocks, and so on. In future strategy the stock application programming is converted by a portable mobile application by using python packages like Kivy, PyQt, or even Beeware's Toga library.

Article Subject Details

• Computer Science
• Computer Science Applications

Article Keywords Details

• Data Visualization
• Machine Learning
• Prediction
• Stock Application
• Technology

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