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Automated Stock Trend Summarization API for Market Watchers

Added Jun 2025 3 design docs

People who follow markets casually, such as retail investors, finance students, and professionals tracking their own portfolios, want one thing each day: a quick, trustworthy read on how the stocks they care about are moving and why. Financial data providers offer raw numbers and news firehoses, but the synthesis step, turning movement plus context into a two-paragraph plain-English trend note, is still manual.

The intern builds a backend service that automates that synthesis. Using Python and FastAPI, the intern creates endpoints where a client submits one or more stock tickers and receives a generated trend summary. The service gathers recent price movement data for the ticker, structures it into a context block, and calls OpenAI models with carefully designed prompts to produce a summary describing the direction, magnitude, and notable pattern of recent movement in accessible language. The intern implements request validation, response schemas, caching so repeated queries do not burn unnecessary API calls, and clear disclaimers distinguishing description from investment advice.

The project introduces an early-stage developer to financial data handling, prompt design for factual summarization, and disciplined API construction with FastAPI, producing a portfolio piece that shows they can put an LLM to work inside a well-behaved backend service.

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