Safe Trade – A Stock Recommender using Machine Learning Algorithms

The business and financial sector currently controls the majority of the global economy, and stock market trading is a key activity there. The project uses Google’s Tensorflow’s Keras API to create LSTM, training the algorithm on historical data to forecast stock prices. We focused on forecasting the trend in a stock’s value over the subsequent 20 days after the prediction day. It is a method where one attempts to foresee the future worth of the company’s present stocks to prevent loss or maybe make a profit. With the use of several quantities that will be discussed later in this report, this study will show how machine learning can be used to anticipate the same. To create somewhat accurate forecasts, we suggest a machine learning algorithm that will be taught using various datasets of certain firms that were available in the past. Shares market forecasting is a method for estimating the future value of a company’s stock or another financial asset traded on an exchange. The only goal of stock market forecasting is a measurable substantial gain, along with, of course, avoiding large losses. Given that the efficient market hypothesis states that these forecasts cannot be produced with the data that is now available, some people may question the veracity of the findings of these predictions and conclude that they are fundamentally unexpected. However, a variety of instruments and technology make it possible to learn about future trends, which generates real income. The suggested approach detects frames with 85.5% accuracy.