Building an AI-Powered Trading Platform in 30 Days
The article details how the author built an AI-powered trading intelligence platform called Chai Street, using a tech stack including FastAPI, React, Rust, and OpenRouter. It covers key decisions, challenges, and solutions around the backend, data caching, scanner architecture, and project management.
Why it matters
This project demonstrates how a solo developer can leverage modern AI and software engineering tools to build a sophisticated trading platform in a short timeframe.
Key Points
- 1Leveraged FastAPI for a data-heavy API, SQLite for production database, and a Rust binary for fast options pricing
- 2Used OpenRouter to access multiple large language models (LLMs) with a single API key
- 3Implemented aggressive caching to reduce external API calls and improve scan performance
- 4Developed a commit-driven project management page that integrates with the codebase
- 5Utilized the project page to maintain context and reduce drift when working with LLMs
Details
The author built Chai Street, an AI-powered trading platform, solo in 30 days. The tech stack includes FastAPI for the backend, React for the frontend, Rust for a high-performance options pricing binary, and OpenRouter to access multiple LLMs with a single API key. Key decisions include using SQLite as the production database due to its simplicity and performance, and building a Rust binary to price options contracts much faster than Python. The author also implemented an aggressive caching strategy to reduce external API calls and improve scan performance. Additionally, they developed a commit-driven project management page that automatically updates with each code change, providing a single source of truth for both the developer and the LLMs used in development.
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