Machine Learning-Based Automated Trading Strategies for the Indian Stock Market

Abstract

The Indian stock market is highly volatile and influenced by various macroeconomic factors, making it challenging for traders to consistently achieve profitable trades using traditional methods. Machine learning (ML)-based automated trading systems offer an intelligent, data-driven approach to analyzing historical market trends, detecting patterns, and executing trades with minimal human intervention. This study explores the application of ML techniques in developing automated trading strategies tailored for the Indian stock market. The research focuses on supervised learning methods, such as Random Forest and Long Short-Term Memory (LSTM) networks, for stock price prediction and trend classification. Additionally, reinforcement learning models, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), are employed to develop adaptive trading strategies that maximize returns while minimizing risks. Historical stock price data from the National Stock Exchange (NSE), combined with technical indicators and sentiment analysis from financial news, are used for model training and evaluation. Back testing results on select Indian stocks (e.g., Reliance, TCS, HDFC Bank) reveal that ML-based trading strategies significantly outperform conventional technical analysis methods. LSTM-based models achieved 15% higher returns compared to traditional moving average crossovers, while DQN-based reinforcement learning strategies demonstrated superior risk management capabilities. This study highlights the potential of ML-driven automated trading systems in improving profitability, decision-making, and risk management in the Indian stock market. However, challenges such as data quality, computational complexity, and regulatory constraints remain key areas for further exploration. Future research will focus on integrating real-time sentiment analysis and optimizing high-frequency trading models for enhanced market performance.