Serverless Stock Market Web Application
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Recent advancements in stock market technology and trading apps have attracted more individual investors to invest in the stock market. Individual investors account for around 38 billion USD of daily trading volume in the US stock market (Adinarayan, 2021). As the financial market dataset is large and changing rapidly this paper aims at building integrative web applications with cloud computing and machine learning framework. The web application is designed in a Microservice architecture with the use of ReactJS as frontend and Python Flask as a backend service. The web application is hosted on AWS serverless framework to provide availability and scalability using the Docker container. To make stock prediction more precise, Linear Regression which is a Supervised machine learning algorithm was evaluated and integrated into the stock web application. The outcome of this web application will provide comprehensive and valuable information to the users such as historical stock data, financial statements, analyst ratings, financial news, organization information and prediction of stocks.