Imagine a world where financial analysis and trading strategies are powered by cutting-edge AI, streamlining your workflows like never before. That's the reality we’re building with large language models.
In my article, I show the potential of LLMs to streamline workflows by generating structured data. While they can't predict stock prices, they can significantly aid in financial analysis and strategy development.
For instance, I've open-sourced a tool that uses LLMs to extract and analyze a company's fundamentals. This tool, available on GitHub, requires just a few setups, and with a simple chat interface, it can provide comprehensive financial summaries.
The real magic happens when LLMs generate JSON objects to interact with financial APIs, allowing for extensive queries and even backtesting strategies in natural language. This is the idea behind the features in NexusTrade. NexusTrade's chat agent, Aurora, automates investing, conducts financial research, and compares companies, among other capabilities.
For example, with just a simple query, the tool can generate a full financial summary of a company, complete with key metrics like P/E ratio, revenue growth, and debt levels.
✅ Large language models are game-changers for algorithmic trading, not by predicting prices, but by enhancing research and strategy workflows.
🔗 Check out the full article here on Medium