Agricultural Futures Trading Decision Using AI Agent With Multiscale Candlestick Analysis

The high volatility, seasonality and complex trading environment of agricultural futures markets present significant challenges to the dynamic adaptability of algorithmic trading systems. Traditional methods rely on fixed-scale image analysis and lack adaptability, making them unsuitable for varying volatility conditions. Therefore, this study proposes an AI agent for agricultural futures trading decisions. First, an agricultural futures adaptive volatility rate serves as a computational tool to dynamically identify high-volatility regions and generate finer-scale candlestick charts. Second, the agent uses Vision-Language Large Model with robust image comprehension to analyze the multiscale candlestick charts and extract key features such as trend direction and technical patterns. Subsequently, a Large Language Model with advanced natural language understanding and logical reasoning serves as the “decision-making brain”, evaluating market trends and making buy/sell decisions. Finally, the agent refines its decision logic through a multimodal feedback mechanism that combines numerical and textual information from ongoing interactions with the environment, thereby enhancing system adaptability and robustness. Experimental results indicate that this framework significantly improves the accuracy, stability, and risk control of trading strategies, offering valuable insights for human decision-making. In addition, it demonstrates potential applicability in other financial markets such as stock indices, energy, and major commodities, providing an innovative solution for intelligent trading in complex market conditions.