Stock Trading Strategy Using Rl Algorithm

This research focuses on applying a reinforcement learning (RL) model to enhance stock trading strategies by incorporating market sentiment analysis, historical price data, and market trends. Key steps involve preprocessing, feature engineering, and assessing performance using metrics such as returns, Sharpe ratio, and maximum drawdown. The RL-based approach adapts to changing market conditions to optimize Return of Investment by making informed buy and sell decisions. The project showcases RL's ability to improve algorithmic trading through adaptive decision-making.