LLM-Based Multi-Agent System for Individual Investment in Energy and Natural Resources
Abstract
Recent advancements in large language models and multiagent large language model based systems show that these technologies can be applied to a large number of problems. They
can automate complex tasks and perform advanced analyses that would take an expert a significant amount of time. This article describes a multiagent large language model (LLM) based platform for investment advisory in the energy natural resources sector. The system integrates multiple types of investment analyses e.g. technical analysis, fundamental analysis, sentiment analysis and stock price prediction. The approach of integrating multiple types of analyses in one system allows the investor to save significant amount of time on analyzing potential investments.
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