## 1 Introduction  
The practice of trying to determine the future value of a business stock or other financial instrument that is traded on an exchange is known as a stock market prediction. A stock trader who can accurately forecast the price of the stock in the future may realize big profits. According to the efficient-market theory, stock prices should reflect all of the information that is currently accessible, and any price changes that are not based on newly revealed information are therefore intrinsically unpredictable. The efficacy of prior prediction algorithms is limited when they just use traditional stock data since the stock market is extremely sensitive to the information that is provided from outside sources. The proliferation of the Internet has led to the development of new types of collective intelligence, such as Google Trends and Wikipedia. The alterations that are being made to these platforms will have a big impact on the stock market. It is considered that the stock price is affected not only by the feeling of financial news but also by the amount of trading activity. Predicting the stock market has received a lot of interest from both academics and businesspeople. The question still stands: How well can the past price history of common stock be used to make predictions about the stock's future price? The efficient market hypothesis (EMH) and the random walk theory were used in the early research on stock market predictions. These early models said that you cannot predict stock prices because they are based on new information (news) and not on current or past prices. So, stock prices will move in a random way and their accuracy cannot be more than 50%. There are more and more studies that show that the EMH and random walk hypotheses are wrong. These studies show that the stock market can be predicted to some extent, which calls into question the EMH's basic ideas. Many people in the business world also see [PERSON]'s ability to consistently beat the S&P index (as a practical sign that the market can be predicted. It is difficult to build an appropriate model since the price might vary depending on several different variables including news, data from social media platforms, fundamentals, output of the firm, historical data, and government bonds price in relation to the economy of the nation. A prediction model that takes into account just one element may not be accurate. As a result, taking into account several elements, such as news, data from social media, and historical pricing, raises the level of precision achieved by the model. Figure 1 describes various applications of AI in Stock trading. Quantitative trading uses computer algorithms and programs based on basic or complicated mathematical models to discover and profit from trading opportunities. Algorithmic trading executes orders based on time, price, and volume using pre-programmed trading instructions. High-frequency trading, which is commonly abbreviated as HFT, is a type of trading that makes use of highly advanced computer algorithms to execute a huge number of orders in a very short amount of time (fractions of a second). An automated trading system (ATS), a form of algorithmic trading, employs computer software to issue buy and sell orders and automatically send them to a market center or exchange (Figure 1). Figure 2 shows different algorithmic trading strategies. Trend following involves buying an item when its price trend rises and selling when it falls, anticipating price fluctuations to continue. In mean reversal asset prices and historical returns gradually revert to their long-term mean or average, according to mean reversion. Momentum trading involves purchasing and selling assets based on current price patterns. Statistical arbitrage uses big, varied portfolios to trade short-term. The moving average crossover approach identifies possible locations of support or resistance in the market.