A Comparison Between Human Trading and Algorithmic Trading

This study presents a comparative analysis of human trading and algorithmic trading, focusing on the methodology employed and the resulting performance metrics. Methodologically, an algorithmic trading bot was developed using Pine Script 5, leveraging the Learning Vector Quantization (LVQ) algorithm model. The bot operated autonomously, executing predefined trading strategies without human intervention. In contrast, human trading involved manual decision-making within defined working hours. Key performance indicators such as net profit, total closed trades, win rate, and profit factor were analysed to assess the efficacy of each approach. The results revealed that while algorithmic trading offered benefits in terms of automation and efficiency, human trading demonstrated instances of superior profitability and adaptability, particularly during working hours. This study underscores the importance of integrating the strengths of both approaches to develop robust trading strategies that are adaptive and effective in navigating dynamic market conditions.