There are two commonly held beliefs when it comes to picking stocks. Fundamental analysis is the initial step in making an investment decision, while technical analysis is the process used in making that decision. Investing involves using two separate analytical tools: fundamental analysis and technical analysis. Fundamental analysis and technical analysis can both tell whether an investment in a company is appealing or unattractive, and then go on to speculate on what the future trends of stocks will be. The combination of fundamental and technical research may provide a complete trading strategy. Artificial recurrent neural network (RNN) architecture, long short-term memory (LSTM) networks. The large-sequence-processing capabilities of LSTMs apply to many data sets. The vast quantity of data that is produced every day in the stock market is ideal for use in artificial intelligence applications. We want to use LSTM to build a financial market forecasting network that uses Technical and Fundamental analysis of businesses to predict the stock prices the next day. To do both kinds of analysis, the input data is pre-processed to contain necessary variables and then trained on LSTM& GRU. In this work we proposed Clustered Gradient Descent Adam optimizer usually perform better than models with Adam optimizer. The GRU model beats the LSTM model when it comes to overall accuracy.