用大模型和NLP技术重构量化交易文献，提升知识提取效率。
Enhancing literature review with LLM and NLP methods. Algorithmic trading case
- 将论文分析拆解为小任务并加入推理步骤，提升复杂问题处理能力。
- 从1.36亿论文中筛选出1.43万篇相关文献，发现机器学习方法近年最流行。
- 大模型可有效优化数据集，支持对不同模型效率的对比分析。
本研究利用机器学习算法分析并组织量化交易领域的知识。通过筛选包含1.36亿篇论文的数据集，识别出1956年至2020年第一季度期间发表的14,342篇相关文章。对比传统关键词算法与嵌入技术，以及最新的主题建模方法（结合降维与聚类），评估了量化交易中各类方法与主题的流行度与演变趋势。结果表明，自然语言处理（NLP）在自动知识提取中具有显著价值，尤其得益于如ChatGPT等大型语言模型（LLM）的最新进展。分析显示，量化交易相关论文的增长速度超过整体出版物增速；尽管股票与主要指数仍占资产类别的一半以上，但加密货币等新兴资产类别呈现更强增长态势。近年来，机器学习模型已成为最主流的研究方法。研究表明，通过将任务分解为子步骤并引入推理机制，大模型能有效应对复杂文献问题，如不同模型效率的比较。该方法深化了对量化交易方法的理解，凸显先进NLP技术在文献综述中的潜力。
原文摘要 · Abstract (English)
This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020. We compare traditional practices-such as keyword-based algorithms and embedding techniques-with state-of-the-art topic modeling methods that employ dimensionality reduction and clustering. This comparison allows us to assess the popularity and evolution of different approaches and themes within algorithmic trading. We demonstrate the usefulness of Natural Language Processing (NLP) in the automatic extraction of knowledge, highlighting the new possibilities created by the latest iterations of Large Language Models (LLMs) like ChatGPT. The rationale for focusing on this topic stems from our analysis, which reveals that research articles on algorithmic trading are increasing at a faster rate than the overall number of publications. While stocks and main indices comprise more than half of all assets considered, certain asset classes, such as cryptocurrencies, exhibit a much stronger growth trend. Machine learning models have become the most popular methods in recent years. The study demonstrates the efficacy of LLMs in refining datasets and addressing intricate questions about the analyzed articles, such as comparing the efficiency of different models. Our research shows that by decomposing tasks into smaller components and incorporating reasoning steps, we can effectively tackle complex questions supported by case analyses. This approach contributes to a deeper understanding of algorithmic trading methodologies and underscores the potential of advanced NLP techniques in literature reviews.
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