I've been curious about Claude Code lately and wanted to give it a try, so I decided to build a simple investment analysis tool. As it turned out, I didn't have to write a single line of code myself. For the underlying algorithm, I drew inspiration from Warren Buffett's investment strategies. The result was impressive: a stock analyzer that evaluates companies based on key financial metrics and qualitative factors—built end-to-end by Claude Code from a handful of high-level prompts.
To start, I asked the Claude App chat interface (using the Opus 4.7 model) about "Warren Buffett's investment strategies." In response, it provided the following five categories, along with approximate weightings for each. Opus 4.7 also furnished detailed explanations and calculation methods for each category; however, since I wanted to keep the requirements at a high level for this overview, I have omitted those specifics here.
Users interact with the program either by entering a company's ticker symbol directly into the UI or by providing a file containing a list of companies to be analyzed.
The analysis results for each company are displayed using horizontal bars and assigned a score. The program issues an evaluation based on the overall score: a score of 75% or higher is rated as a "Buy," 55% to 74% is rated as "Watch," and 54% or lower is rated as "Pass" (skip). Opus determined these rating thresholds as well. In the demo app, these thresholds are hard-coded, but they could easily be made configurable via a settings panel or a configuration file for more flexibility.
At this point in our chat, it generated an HTML demo of the application for me. The sample companies chosen for the demo were selected somewhat arbitrarily—though, coincidentally, they happened to be SaaS companies that are currently making headlines in the media due to the widely discussed "death of SaaS" narrative.
Here is the prototype the Claude App produced in that session:
Still within the same Claude App chat, I moved on to the architecture. I deliberately avoided overly granular implementation details and conveyed only the following points:
Based on these high-level requirements, it recommended a modern Python + React web application—a combination that strikes a reasonable balance between robust data-analysis capabilities and ease of web deployment. The resulting design is straightforward: a React frontend talks to a FastAPI backend, which orchestrates the Python data-fetching layer and persists the cleaned results into a database. The scoring engine runs server-side, keeping the frontend a thin presentation layer.
That wraps up the planning phase—requirements and architecture, all driven through the Claude App. In Part 2/3, I'll hand the plan over to Claude Code and walk through the MVP build: the prompts I used and where it needed nudging. Part 3/3 will then cover the road from MVP to the finished product.