Algorithmic Trading Using Machine Learning

The Algorithmic Trading using Machine Learning is a financial trading system that integrates technical analysis indicators and future predictions with news sentiment analysis through reinforcement learning algorithms. The system trains autonomous trading agents to make effective buy, sell, or hold decisions in complex market environments by combining historical stock data, technical indicators, and sentiment analysis from financial news. It implements and compares three state-of-the-art reinforcement learning algorithms: Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and Advantage Actor-Critic (A2C). The algorithm was tested on diverse market conditions exhibiting remarkable adaptability, including bull, bear, and sideways markets. This research contributes to the field of algorithmic trading by offering a multi-modal approach that considers both market data and news sentiment for more informed trading decisions, paving the way for more sophisticated and adaptable automated trading systems.