A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
CoRR(2024)
摘要
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 9 state-of-the-art baselines in terms of 6 financial metrics with
over 36
relative improvement) is achieved on one dataset. Notably, FinAgent is the
first advanced multimodal foundation agent designed for financial trading
tasks.
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