Machine Learning-Driven Trading Strategies: An Empirical Analysis of BSE Blue-Chip Stocks Using Advanced Algorithmic Approaches

This study has comprehensively investigated the empirical nature of the machine-learning- based trading strategies to be applied to blue-chip stocks of the Bombay Stock Exchange. Focused on how well modern algorithmic methodologies could improve the trading performance of the highly volatile Indian capital market setting, the study takes three major BSE securities-Reliance Industries, Tata Consultancy Services, and Infosys-into historical data for the entire period of 2024 trading and implements machine learning models: Random Forest, Support Vector Machines, and Deep Q-Networks-for automated trading strategy generation. The use of deep technical indicators in the methodology, including Bollinger bands, moving averages, and Z-scores, sets it apart while using the reinforcement learning framework to maximize risk-adjusted returns. Our hybrid modeling approach combines a supervised learning procedure, unsupervised learning, and rigorous back testing to validate performance properly. Backing this empiricism is the result that the machine-learning-based strategies in trading perform significantly ahead of traditional buy-and-hold strategies with higher Sharpe ratios and controlled drawdown levels. Key findings reveal that reinforcement learning algorithms like Proximal Policy Optimization are capable of assimilating market changes to variations and make profitable trades under different market conditions. This machine-learning-based framework provides the solution to several major challenges including the minimization of overfitting, optimization of data quality, and considerations for regulatory compliance during its actual deployment. Risk management schemes embedded inside the algorithmic frame- work act to shield the portfolio from volatility while maximizing return potential. The results obtained from this study add value in investing and provide the framework for institutional and retail investors interested in using cutting-edge computational techniques to achieve superior portfolio performance in the Indian stock market ecosystem.