Summary of Quantagent: Seeking Holy Grail in Trading by Self-improving Large Language Model, By Saizhuo Wang et al.
QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model
by Saizhuo Wang, Hang Yuan, Lionel M. Ni, Jian Guo
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework for developing autonomous agents based on Large Language Models (LLMs) that can tackle real-world challenges, such as quantitative investment. The main challenge is building a domain-specific knowledge base for the agent’s learning process. To address this, the authors introduce a two-layer loop framework, where the inner loop refines the agent’s responses by drawing from its knowledge base and the outer loop tests these responses in real-world scenarios to enhance the knowledge base with new insights. The approach enables the agent to progressively approximate optimal behavior with provable efficiency. The paper instantiates this framework through an autonomous agent for mining trading signals, called QuantAgent, which uncovers viable financial signals and enhances the accuracy of financial forecasts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about building a special kind of computer program that can make decisions on its own, like a robot. It’s trying to figure out how to do this in a way that works well for things like investment and finance. The main problem is getting the program to learn from experience and get better over time. To solve this, the authors came up with a new approach that involves the program refining its decisions based on what it knows, and then testing those decisions in real-life situations to improve its knowledge. This makes the program get smarter and make better choices. The paper shows how this works by creating an example program called QuantAgent that can find useful investment signals and predict stock prices more accurately. |
Keywords
» Artificial intelligence » Knowledge base
