用大模型生成交易策略，靠语言反馈自动优化，比特币交易效果更好。
An Adaptive Multi Agent Bitcoin Trading System
- 分角色大模型代理：分别做技术分析、情绪判断、决策和反思。
- 牛市收益超买持有30%以上，震荡市转亏为盈超100%。
- 不用调参数，靠文字反馈就能让模型越用越好，适合量化交易者。
本文提出一种基于大语言模型（LLMs）的多智能体比特币交易系统，用于生成投资策略与管理投资组合。与股票不同，加密货币波动剧烈，受市场情绪和监管消息快速变化影响显著，传统静态回归或仅依赖历史数据训练的神经网络难以建模。该框架将LLM拆分为专业代理：技术分析、情绪评估、决策制定与绩效反思。代理通过一种新型自然语言反馈机制持续改进：反思代理每日/每周提供交易决策的文本评价，并注入后续提示中，使代理调整配置逻辑，无需权重更新或微调。在2024年7月至2025年4月的比特币数据上回测显示，该系统在各类市场环境下均表现优异：量化代理在牛市阶段回报率比买持有高出30%以上，整体收益达15%，而情绪驱动代理将盘整市从小幅亏损转为超100%盈利。加入周度反馈后，总收益提升31%，熊市损失减少10%。结果表明，语言反馈是一种可扩展、低成本的金融目标优化新范式。
原文摘要 · Abstract (English)
This paper presents a Multi Agent Bitcoin Trading system that utilizes Large Language Models (LLMs) for alpha generation and portfolio management in the cryptocurrencies market. Unlike equities, cryptocurrencies exhibit extreme volatility and are heavily influenced by rapidly shifting market sentiments and regulatory announcements, making them difficult to model using static regression models or neural networks trained solely on historical data. The proposed framework overcomes this by structuring LLMs into specialised agents for technical analysis, sentiment evaluation, decision-making, and performance reflection. The agents improve over time via a novel verbal feedback mechanism where a Reflect agent provides daily and weekly natural-language critiques of trading decisions. These textual evaluations are then injected into future prompts of the agents, allowing them to adjust allocation logic without weight updates or finetuning. Back-testing on Bitcoin price data from July 2024 to April 2025 shows consistent outperformance across market regimes: the Quantitative agent delivered over 30\% higher returns in bullish phases and 15\% overall gains versus buy-and-hold, while the sentiment-driven agent turned sideways markets from a small loss into a gain of over 100\%. Adding weekly feedback further improved total performance by 31\% and reduced bearish losses by 10\%. The results demonstrate that verbal feedback represents a new, scalable, and low-cost approach of tuning LLMs for financial goals.
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