Gate Research Institute: Multi-Agent LLM Trading Framework Significantly Outperforms Buy & Hold Strategy in BTC Backtesting

Odaily Odaily News: A recent report released by Gate Research Institute, titled "Research and Backtesting Analysis of BTC Trading Framework Based on Multi-Agent LLM," points out that compared to a single LLM directly generating trading signals, the Multi-Agent LLM architecture more closely mirrors the research and investment process of real financial institutions. By leveraging collaboration and debate among analysts, researchers, traders, and risk control teams, it enhances the transparency and risk control capabilities of trading decisions. The research, based on the TradingAgents framework, constructs an AI trading system applicable to the crypto scenario for the BTC market, introducing multiple agent roles such as technical analysis, news analysis, sentiment analysis, and macro/on-chain analysis.

Using BTC/USDT 1-hour data, the study conducted historical backtesting of the TradingAgents-BTC strategy. The results show that the strategy achieved a total return of +20.25% during the testing period, significantly outperforming the Buy & Hold strategy's -7.89% over the same period. Furthermore, its maximum drawdown was controlled at -17.41%, lower than the Buy & Hold's -27.06%. The research suggests that during periods of consolidation and decline, the multi-agent framework can reduce some risk exposure through Sell/Underweight and Flat states, and re-enter long positions during market rebounds, thereby improving overall risk-adjusted returns.

The report indicates that the Multi-Agent LLM framework shows certain application potential in crypto trading scenarios. However, the current backtesting period covers only about three months, and 1-hour level trading may still be affected by transaction fees, slippage, and signal latency. Future work requires further validation of the strategy's stability and generalization capabilities over longer historical periods, different market conditions, and across a wider range of asset classes.