LLM can be used to develop autonomous trading systems. The key is to have agents mimic the workflow within a multi-agent trading framework that divides a trading system’s tasks. When you look at a deep decomposition of the tasks associated with a trading system, you will get better risk-adjusted returns. In the paper, “Toward Expert Investment Teams: A Multi-agent LLM System with Fine-Grained Tradign Tasks”, the researchers develop what they call a fine-grained system that looks at detailed tasks and instructions and compares it with a coarse-grained LLM system that does not provide detailed instructions to the agents.
Across different stock portfolios, the fine-grained approach performs much better. It is notable that we can examine the effects of dropping specific agent tasks and assess their impact on the Sharpe ratio.
The results are clear - be detailed with the LLM tasks given to agents. The more specific you get, the better the results.
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