Paper: arXiv 2407.09546

Abstract

The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions. Yet, the unique opportunities presented by the cryptocurrency market, noted for its on-chain data’s transparency and the critical influence of off-chain signals like news, remain largely untapped by LLMs. This work aims to bridge the gap by developing an LLM-based trading agent, CryptoTrade, which uniquely combines the analysis of on-chain and off-chain data. This approach leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market. CryptoTrade incorporates a reflective mechanism specifically engineered to refine its daily trading decisions by analyzing the outcomes of prior trading decisions. This research makes two significant contributions. Firstly, it broadens the applicability of LLMs to the domain of cryptocurrency trading. Secondly, it establishes a benchmark for cryptocurrency trading strategies. Through extensive experiments, CryptoTrade has demonstrated superior performance in maximizing returns compared to traditional trading strategies and time-series baselines across various cryptocurrencies and market conditions. Our code and data are available at \url{“https://anonymous.4open.science/r/CryptoTrade-Public-92FC/"}.

Complexity vs Empirical Score

  • Math Complexity: 1.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on LLM prompting and traditional financial indicators (e.g., MACD, MA) rather than dense mathematical theory, but features heavy empirical backtesting with specific crypto assets, market conditions, and published code/data.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Bridge LLM gap in<br>cryptocurrency trading"] --> B["Data Sources<br>On-chain & Off-chain signals"]
  B --> C["LLM-based Agent<br>CryptoTrade Framework"]
  C --> D["Core Process<br>Reflective Decision Loop"]
  D --> E["Execution<br>Zero-shot Trading"]
  E --> F["Key Outcomes<br>Superior returns vs baselines<br>Benchmark established"]

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3 of 55 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.