Analysis
This article presents a brilliant architectural solution to the common problems of hallucination and token costs in financial analysis by using a hybrid Python-plugin system. By restricting the Large Language Model (LLM) to the role of an interpreter for deterministic quantitative data, the author achieves high scalability and reproducibility. It is an excellent example of pragmatic Prompt Engineering and system design that leverages the strengths of both traditional code and Generative AI.
Key Takeaways
- •The system uses a plugin architecture where one investment method equals one Python file, allowing for easy maintenance and modification.
- •Currently, 9 academic-paper-based plugins are active, covering factors like Piotroski F-Score, Factor Momentum, and Altman Z-Score.
- •The workflow runs daily at 4 PM, screening stocks via plugins and using Claude to synthesize results for Discord notifications.
Reference / Citation
View Original"I changed the approach: quantitative analysis is run deterministically with Python, and Claude is tasked only with 'integrating multiple analysis results to convey them to humans.'"