OPTIMIZING STOCK TRADING STRATEGY WITH REINFORCEMENT LEARNING

We propose to determine a single exchange-traded fund (SPY) investing strategy That will maximize our total wealth. We compare our findings to the tried-and-true “buy- and-hold” and Moving average convergence divergence (MACD) strategies. Here, we Intend to base an evaluation on every es- sential standard that is considered when establishing the pricing. As mentioned, we intend to forecast future price Fluctuations for a specific stock. Prices from previous days, as well as financial Media stories connected to the firm of interest, are used to create these forecasts. Reinforcement learning is all about taking the right steps to maximize your reward In a given situation. It is used by a variety of software and computers to determine The best feasible action or path in a given situation. The Reinforcement Machine Learning model is employed in this work to forecast the closure price using past Data. We develop a predictor for multiple firms using these trained models that Forecasts the every day close stock prices. By providing inputs such as open, high, And low prices, stock volume, and the latest events about each firm, this predictor May be used to determine the price at which the stock value will close for a certain Day. The goal of this project is to learn and get hands-on experience in Data Analytics and Machine Learning.