Maximizing Portfolio Returns in Stock Market Using Deep Reinforcement Techniques

Stock markets have become attractive investments due to the potential for high returns. However, investing in the stock market also comes with inherent risks, and making informed decisions is essential to minimize losses. Accurately predicting stock prices is key to reducing risk and maximizing returns. While there are various investment opportunities in the stock market, ranging from listed stocks to derivatives, predicting the most likely direction of stock prices can be challenging. In this study, we aim to design a predictive machine learning model using deep reinforcement learning, a technique that leverages reward functions to optimize future rewards. This approach differs significantly from classical machine learning and regression algorithms. It offers several advantages, including the ability to evaluate potential trades and select those that are most likely to provide optimal returns. By using deep reinforcement learning, historical data can be better analyzed to predict future stock prices. This technique helps us identify potential trading strategies by leveraging reward functions to accurately predict which trades will most likely provide the best returns. The model will be evaluated by comparing the performance of three agents using the Sharpe Ratio, a mathematical evaluation of returns that considers factors such as expected and risk-free returns. By analyzing the performance of different agents, the optimal trading strategy can be identified to provide more accurate predictions and better results for investors.