A stock market, often known as an exchange for shares, is one of the best places for businesses to raise capital. Stock investing serves as an asset for the future financial advantages of an individual or other company. Because of the numerous variables that go into stock market forecasting, including politics, interest rates, economic growth, and a host of other elements, it can be very difficult to create an accurate prediction. The true prediction of the share provides a huge opportunity for profit and serves as an incentive for this field's research and analysis. There are two main approaches to stock market forecasting or analysis. The technical analysis comes second, followed by the fundamental analysis. The primary focus of fundamental analysis is on textual data, such as earnings reports and financial news. However, technical analysis uses historical data to forecast future prices (i.e. it focuses on the direction of prices). We suggest a straightforward deep learning (DL) architecture that makes use of Long-Short-Term Memory (LSTM), a sophisticated Recurrent Neural Network (RNN) component that has gained a lot of notoriety lately for its superior performance with time series data and resistance to the vanishing gradient problem.