Algorithmic Trading Bot Using Artificial Intelligence Supertrend Strategy

Bogdan-Petru Vrînceanu and Florentin Șerban
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Bogdan-Petru Vrînceanu: Bucharest Academy of Economic Studies, Romania
Florentin Șerban: Bucharest Academy of Economic Studies, Romania

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 1, 14-20

Abstract: This article presents a trading strategy that combines the Super Trend indicator with the K-Nearest Neighbors (KNN) algorithm, utilizing artificial intelligence (AI) to automate market decision-making and enhance trading accuracy. The strategy integrates the SuperTrend indicator, which dynamically tracks market volatility, with the KNN algorithm, allowing the system to classify market trends as bullish, bearish, or neutral based on historical data. This enables the strategy to make intelligent, data-driven decisions in real-time without human intervention. The AI-driven approach automates the entire trading process, from data analysis to trade execution, improving efficiency and removing emotional biases from trading decisions. The KNN algorithm plays a key role in this automation by analyzing past market conditions and identifying patterns that inform future price movements. This allows the system to adapt to changing market trends and react quickly to new data, ensuring timely and accurate decisions. The results of the strategy indicate strong performance, with a Net Profit of 959.38 USD and a Gross Profit of 3,005.71 USD, demonstrating the strategy's ability to generate consistent returns. The Profit Factor of 1.469 further highlights the system's ability to produce profits while managing risk effectively. Additionally, the Sharpe Ratio of 0.558 shows that the strategy provides positive risk-adjusted returns, making it a reliable tool for automated trading. In conclusion, this AI-powered Super Trend-KNN strategy showcases the potential of combining artificial intelligence with technical indicators for automated trading. By eliminating the need for manual intervention and leveraging AI to adapt to market conditions, the strategy provides an efficient and scalable solution for intelligent decision-making in trading.

Date: 2025
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