  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/}.
