Master Trader

Master Trader is a full-stack financial simulation platform built during HackAZ 2025 as a deliberately unserious project that we nonetheless took very seriously.

The premise was straightforward: if public officials are allowed to trade stocks while in office, what if we simply copied their behavior and automated it? We pulled real congressional trading data from CapitolTrades.com and fed it into an LLM to generate stock recommendations modeled after the trading patterns of various political figures.

The result is an AI-powered "advisor" that confidently recommends stocks based on the same information that is publicly available, legally disclosed, and apparently not a problem. The platform presents these recommendations through a chatbot interface that stays in character, offering financial advice with the appropriate level of certainty.

Technically, the project is fully deployed on AWS, with a React frontend hosted on Amplify, Lambda functions handling serverless backend logic, and DynamoDB for data storage. We integrated real-time market data and built the system end-to-end over the course of the hackathon.

During judging, we were asked about potential legal ramifications of the project, which felt like an appropriate reaction given the data source. While the project is essentially a shitpost, it nevertheless was a serious full stack development project that I was able to create together with my friends.