Enhancing Trading Strategies: A Multi-indicator Analysis for Profitable Algorithmic Trading

Abstract

Algorithmic trading has become increasingly prevalent in financial markets, and traders and investors seeking to leverage computational techniques and data analysis to gain a competitive edge. This paper presents a comprehensive analysis of algorithmic trading strategies, focusing on the efficacy of technical indicators in predicting market trends and generating profitable trading signals. The research framework outlines a systematic process for investigating and evaluating stock market investment strategies, beginning with a clear research objective and a comprehensive review of the literature. Data collected from various stock exchanges, including the S&P 500, undergo rigorous preprocessing, cleaning, and transformation. The subsequent stages involve generating investment signals, calculating relevant indicators such as RSI, EMAs, and MACD, and conducting backtesting to compare the strategy's historical performance to benchmarks. The key findings reveal notable returns generated by the indicators analyzed, though falling short of benchmark performance, highlighting the need for further refinement. The study underscores the importance of a multi-indicator approach in enhancing the interpretability and predictive accuracy of algorithmic trading models. This research contributes to understanding of algorithmic trading strategies and provides valuable information for traders and investors looking to optimize their investment decisions in financial markets.