Project Overview: Stock Data Fetcher
In this project stream, you'll build a command-line tool that fetches stock prices from a real API. It's a practical project that teaches skills you'll use constantly: making API calls, parsing JSON data, handling errors, and building user-friendly CLI interfaces.
What You'll Build
The finished tool lets you check stock prices from your terminal:
$ python stocks.py AAPL
AAPL: $178.52
$ python stocks.py AAPL GOOGL MSFT
AAPL: $178.52
GOOGL: $141.23
MSFT: $378.91
$ python stocks.py INVALID
Error: Symbol 'INVALID' not found
Simple to use, but there's real complexity underneath. Your code will connect to external services, handle network failures, validate user input, and present data clearly.
Skills This Project Teaches
API Integration: You'll learn to read API documentation, authenticate requests, and handle responses. These skills transfer to any API you'll ever use.
Data Parsing: Stock APIs return JSON data with nested structures. You'll extract exactly what you need from complex responses.
CLI Design: Good command-line tools are intuitive. You'll learn to handle arguments, provide helpful error messages, and format output readably.
Error Handling: Networks fail. APIs return unexpected data. Users type invalid input. You'll build code that handles these gracefully instead of crashing.
The Technology Stack
We'll use Python for this project — its readability makes the code easy to understand, and its requests library simplifies HTTP calls. You'll also use a free stock data API that provides real market information.
Starting Small
Your first version won't have all these features. We'll start with the simplest possible implementation: fetch one stock price and print it. Then we'll add features incrementally — multiple stocks, better error handling, formatted output.
This approach lets you see working code quickly and build confidence before tackling complexity.
What's Coming
Over the next lessons, you'll choose an API, understand its documentation, prompt AI for core functionality, add error handling, and polish the user experience. By the end, you'll have a useful tool and transferable skills for any API-based project.