The analysis of an AI-based trading bot's performance in generating returns in the foreign exchange market suggests improvements in risk management and strategy adaptability.
This study aims to design and evaluate an AI-based intelligent trading bot by analyzing its performance using historical data over a five-year period in the foreign exchange (ForEx) market. The bot was tested on five major currency pairs to assess its efficiency in generating returns and managing risks effectively. The bot’s performance was evaluated using key financial and statistical metrics such as profit-to-loss ratio, maximum drawdown, Sharpe ratio, and profit factor, allowing for an assessment of its sustainability in different market environments. The results indicated that the bot demonstrated more stable performance in high-liquidity, moderately volatile pairs and faced greater challenges in more volatile markets. Based on the findings, a set of recommendations was proposed, including improving risk management strategies, tailoring trading strategies to market behavior, and developing adaptive AI-based models to optimize performance in dynamic market conditions. This study highlights the importance of analyzing AI-driven trading systems using comprehensive historical data and developing dynamic strategies capable of adapting to market fluctuations, thereby improving the efficiency and stability of automated trading in real-world conditions.
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Djamal et al. (2025) studied this question.
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