Abstract: Algorithmic trading has revolutionized the financial markets by leveraging computational power and complex mathematical models to execute trades at high speeds and frequencies. This paper proposes a novel algorithmic trading model that optimizes trade execution using machine learning techniques. We employ a combination of supervised learning for predictive modeling and reinforcement learning for decision-making processes. The results demonstrate significant improvements in trading accuracy and profitability, outperforming traditional heuristic-based trading systems. Our findings suggest that the integration of advanced machine learning methodologies can enhance trading strategies and contribute to more efficient market operations.

Keywords: Algorithmic Trading, Machine Learning, Supervised Learning, Reinforcement Learning, Predictive Modeling, Financial Markets, Trading Strategies, High-Frequency Trading

| DOI: 10.17148/IARJSET.2024.11579