Algorithmic Trading Simulation of Tata Consultancy Services (TCS) Using Moving Average Strategies: Evidence From 2020 to 2025

Abstract

The rapid advancement of algorithmic trading has significantly transformed modern financial markets by enabling systematic, rule-based decision-making with minimal human intervention. This study examines the effectiveness of a simple moving-average crossover-based algorithmic trading strategy applied to Tata Consultancy Services, one of India’s leading large-cap stocks. The trading system generates buying and selling recommendations through its 20-day short-term simple moving average and 50-day long-term simple moving average system, which operates on daily adjusted closing-price data from the years 2020 to 2025. The model operates in the R programming environment, which undergoes back testing through financial analysis packages that test its performance while safeguarding against look-ahead bias. The performance assessment of the proposed strategy uses key financial metrics, which include cumulative returns and annualised returns together with volatility and maximum drawdown. The empirical results show that the moving-average crossover strategy successfully detects medium- to long-term price movements while it protects investors better from losses than traditional buy-and-hold methods. The trading strategy shows decreased market downturns and smaller price movements, but it lacks capacity to produce profits during market stability because it keeps switching between buying and selling signals. The research demonstrates that basic technical trading rules still function effectively in Indian emerging markets when investors assess market performance over long durations. The results support existing literature that emphasises the role of trend-following strategies in enhancing risk-adjusted performance rather than consistently maximising absolute returns.