Stock Market Prediction is beneficial to investors. It provides shareholders with useful assistance in making suitable decisions about whether to purchase or sell shares. Accurate stock price prediction is extremely challenging because of multiple factors. Stock prices are influenced by a variety of factors, but the price at any given time is determined by supply and demand in the market. Due to the growing volume of data, it is now impractical, if not impossible for humans to manually analyze data for certain tasks like predicting stock market movements, necessitating automation. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature. Thus, this also means that there is a lot of data to find patterns in. This gives rise to the concept of algorithmic trading, which uses automated, pre-programmed trading strategies to execute orders. Machine learning algorithms explore large amounts of data and search for a model that will achieve the programmer’s goal. Our project’s goal is to use machine learning to implement a momentum strategy. We attempted to predict trading signals using machine learning techniques based on a set of technical indicators and rules.
In a nutshell, algorithmic trading gives investors the ability to make more trades in a shorter amount of time without being affected by human emotions or trading mistakes. The following is an illustration of algorithmic trading instructions:
*Purchase 100 shares of the XYZ Company if the price rises to Rs. 450 before 2:00 PM. Now, the algorithmic trading order will automatically place an order for 100 shares of the XYZ company if the share price rises above Rs 450. The order will only be carried out by the algorithmic trading program, though, if the target price is reached before 2:00 PM. The directives are null and void after 2:00 PM.
*If company QPR's 20-day moving average dips below the 200-day moving average prior to market close, sell 100 shares of the company. In this scenario, if the 20-day moving average of the QPR company dips below the 200-day moving average before the market closes, the algorithmic trading software will sell 100 shares of QPR. Otherwise, the order is not carried out