Recent advancements in Large Language Models (LLMs) have revolutionized question-answering tasks across various domains, showcasing their ability to integrate extensive web knowledge. This has sparked interest in developing LLM-based autonomous agents for diverse applications. However, transitioning these models into purpose-driven agents requires a rational architecture capable of processing multi-source information, establishing reasoning chains, and prioritizing critical tasks. In response to this challenge, the innovative \textsc{FinMem} framework has been introduced as a novel LLM-based agent tailored specifically for financial decision-making. Comprising three core modules - Profiling, Memory, and Decision-making - \textsc{FinMem} aims to empower the agent with the ability to customize its characteristics, assimilate hierarchical financial data through layered message processing, and convert insights gained from memories into informed investment decisions. Of particular significance is the memory module of \textsc{FinMem}, which closely mirrors the cognitive structure of human traders. This feature offers robust interpretability and real-time tuning capabilities, allowing the agent to adapt its cognitive span beyond human perceptual limits. By enabling the retention of critical information and facilitating continuous refinement of trading decisions in volatile financial environments, \textsc{FinMem} empowers the agent to self-evolve its professional knowledge and react agilely to new investment cues. To validate its efficacy, \textsc{FinMem} was compared against various algorithmic agents using a scalable real-world financial dataset. The results underscored its superior trading performance in stocks. Furthermore, through fine-tuning of perceptual span and character settings, \textsc{FinMem} achieved significantly enhanced trading outcomes. In summary, represents a cutting-edge LLM agent framework designed for automated trading that enhances cumulative investment returns.
SummaryRecent advancements in Large Language Models (LLMs) have improved how computers answer questions in different areas. People are now interested in creating smart computer programs using LLMs for many different tasks. To make these programs useful, they need to be able to process lots of information, think logically, and focus on important tasks. A new system called \textsc{FinMem} uses LLMs to help with financial decisions by customizing information, storing data like a human trader, and making smart investment choices. \textsc{FinMem} has three main parts - Profiling, Memory, and Decision-making - that help it learn and adapt like a professional.
Definitions
- Large Language Models (LLMs): Advanced computer programs that understand and generate human language.
- Autonomous agents: Smart computer programs that can make decisions without direct human control.
- Architecture: The way different parts of a system are organized and work together.
- Financial decision-making: Making choices about money-related matters like investments.
- Framework: A structure or plan used as a guide for building something.
- Hierarchical: Arranged in levels or layers of importance.
- Interpretability: The ability to understand and explain how something works.
- Real-time: Happening immediately or without delay.
- Scalable: Capable of growing or adapting easily to handle more work or data.
- Fine-tuning: Making small adjustments to improve performance or accuracy.
Recent advancements in Large Language Models (LLMs) have revolutionized question-answering tasks across various domains, showcasing their ability to integrate extensive web knowledge. This has sparked interest in developing LLM-based autonomous agents for diverse applications. However, transitioning these models into purpose-driven agents requires a rational architecture capable of processing multi-source information, establishing reasoning chains, and prioritizing critical tasks.
In response to this challenge, the innovative \textsc{FinMem} framework has been introduced as a novel LLM-based agent tailored specifically for financial decision-making. Comprising three core modules - Profiling, Memory, and Decision-making - \textsc{FinMem} aims to empower the agent with the ability to customize its characteristics, assimilate hierarchical financial data through layered message processing, and convert insights gained from memories into informed investment decisions.
The first module of \textsc{FinMem}, Profiling, enables the agent to personalize its characteristics based on user-defined preferences and risk tolerance levels. This feature allows the agent to adapt its decision-making process according to individual needs and goals. By incorporating human-like traits such as risk aversion or aggressiveness into its profile, \textsc{FinMem} can better align with investors' expectations.
The second module of \textsc{FinMem}, Memory, is particularly significant as it closely mirrors the cognitive structure of human traders. This feature offers robust interpretability and real-time tuning capabilities by enabling the retention of critical information and facilitating continuous refinement of trading decisions in volatile financial environments. The memory module comprises two sub-modules: Short-term Memory (STM) and Long-term Memory (LTM). STM stores recent market trends and updates while LTM retains past experiences for long-term learning purposes.
One key aspect that sets \textsc{FinMem} apart from other LLM-based agents is its ability to self-evolve its professional knowledge beyond human perceptual limits through continuous fine-tuning of perceptual span and character settings. This feature allows the agent to adapt its cognitive span according to market conditions, enabling it to react agilely to new investment cues.
The final module of \textsc{FinMem}, Decision-making, utilizes the insights gained from the Profiling and Memory modules to make informed investment decisions. By integrating hierarchical financial data through layered message processing, \textsc{FinMem} can establish reasoning chains and prioritize critical tasks based on user-defined preferences.
To validate its efficacy, \textsc{FinMem} was compared against various algorithmic agents using a scalable real-world financial dataset. The results underscored its superior trading performance in stocks. Furthermore, through fine-tuning of perceptual span and character settings, \textsc{FinMem} achieved significantly enhanced trading outcomes.
In summary, \textsc{FinMem} represents a cutting-edge LLM agent framework designed for automated trading that enhances cumulative investment returns by empowering the agent with personalized characteristics, robust interpretability through human-like memory structures, and continuous self-evolution capabilities. With further advancements in LLM technology and integration of more diverse datasets into its memory bank, \textsc{FinMem} has the potential to revolutionize autonomous financial decision-making processes even further.
Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains. Their prowess in integrating extensive web knowledge has fueled interest in developing LLM-based autonomous agents. While LLMs are efficient in decoding human instructions and deriving solutions by holistically processing historical inputs, transitioning to purpose-driven agents requires a supplementary rational architecture to process multi-source information, establish reasoning chains, and prioritize critical tasks. Addressing this, we introduce \textsc{FinMem}, a novel LLM-based agent framework devised for financial decision-making. It encompasses three core modules: Profiling, to customize the agent's characteristics; Memory, with layered message processing, to aid the agent in assimilating hierarchical financial data; and Decision-making, to convert insights gained from memories into investment decisions. Notably, \textsc{FinMem}'s memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. Its adjustable cognitive span allows for the retention of critical information beyond human perceptual limits, thereby enhancing trading outcomes. This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. We first compare \textsc{FinMem} with various algorithmic agents on a scalable real-world financial dataset, underscoring its leading trading performance in stocks. We then fine-tuned the agent's perceptual span and character setting to achieve a significantly enhanced trading performance. Collectively, \textsc{FinMem} presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.
