A multimodal foundation agent for financial trading : Tool-augmented, diversified, and generalist

• Main Authors: ZHANG, Wentao, ZHAO, Lingxuan, XIA, Haochong, SUN, Shuo, SUN, Jiaze, QIN, Molei, LI, Xinyi, ZHAO, Yuqing, ZHAO, Yilei, CAI, Xinyu, ZHENG, Longtao, Xinrun WANG, AN, Bo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Large Language Models; Quantitative trading; Financial AI agents; Data mining; Machine learning; Electronic commerce; Artificial Intelligence and Robotics; Management Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9830; https://ink.library.smu.edu.sg/context/sis_research/article/10830/viewcontent/3637528.3671801.pdf
• Institution: Singapore Management University

Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, and visual-to accurately analyze the financial market. Its unique dual-level reflection module not only enables rapid adaptation to market dynamics but also incorporates a diversified memory retrieval system, enhancing the agent's ability to learn from historical data and improve decision-making processes. The agent's emphasis on reasoning for actions fosters trust in its financial decisions. Moreover, FinAgent integrates established trading strategies and expert insights, ensuring that its trading approaches are both data-driven and rooted in sound financial principles. With comprehensive experiments on 6 financial datasets, including stocks and Crypto, FinAgent significantly outperforms 12 state-of-the-art baselines in terms of 6 financial metrics with over 36% average improvement on profit. Specifically, a 92.27% return (a 84.39% relative improvement) is achieved on one dataset. Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.