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A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading

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arxiv 2407.09546 v2 pith:FLEPYDMD submitted 2024-06-27 q-fin.TR cs.SI

Yuan Li , Bingqiao Luo , Qian Wang , Nuo Chen , Xu Liu , Bingsheng He This is my paper · ORCID
classification q-fin.TRcs.SI
keywords tradingcryptocurrencydatamarketcryptotradedecisionsllmsoff-chain
verification ladder T0 review T1 audit T2 compute T3 formal

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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 https://anonymous.4open.science/r/CryptoTrade-Public-92FC/.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DMind Benchmark: Toward a Holistic Assessment of LLM Capabilities across the Web3 Domain

    cs.CR 2025-04 reject novelty 6.0 of 10

    DMind is a 3,543-item, nine-domain benchmark for LLMs in Web3; evaluation of 31 models shows strong fundamentals but weak security, token economics, and meme-related reasoning.

  2. LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management

    q-fin.TR 2025-01 conditional novelty 6.0 of 10

    A fine-tuned multi-agent LLM system that averages token-level confidence across specialized crypto agents reports 83.5% cumulative return and a 1.54 Sharpe ratio in a Nov 2023-Sep 2024 backtest, beating single-agent a...

  3. SoK: Security and Privacy of AI Agents for Blockchain

    cs.CR 2025-09 conditional novelty 5.0 of 10

    A systematization of knowledge that proposes a taxonomy and reference architecture for blockchain AI agents, and catalogs security and privacy threats.

  4. PulseReddit: A Novel Reddit Dataset for Benchmarking MAS in High-Frequency Cryptocurrency Trading

    cs.CL 2025-06 reject novelty 5.0 of 10

    MAS traders using Reddit sentiment from PulseReddit beat traditional baselines in the reported bull-market backtests, but the gains are small, most runs lose money, and the evaluation has critical flaws.

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