Nate's Project Guide

Project: Portfolio Tracker Category: Data Science Last updated: April 14

Note: This guide is created based on the latest state of your project repository + any notes from our discussion. It may not always reflect the most up-to-date information.

Where You Are

You have a project idea: a portfolio tracker that shows value, allocation, gain/loss, and expected return (CAPM) for a securities portfolio. You've started filling out your spec and you have some files in your data/ folder (portfolio_analysis.py and portfolio.txt), which is a good start.

However, there are several things that need attention before Checkpoint 1 tomorrow:

  • Spec formatting -- Your spec has the right ideas but the markdown formatting needs cleanup. The section headers got mixed up with your answers. Take a pass through project.spec.md and make sure each section is clearly filled in under the right heading.
  • Tech stack -- You listed "Open AI" but this project sounds more like a Pandas + Matplotlib project (reading portfolio data, calculating values, creating charts). Update your tech stack to reflect what you'll actually use.
  • No pyproject.toml -- Your project hasn't been initialized with uv yet, so there are no dependencies installed.
  • Journal -- Your journal entry is still empty.
  • Code location -- Your Python file is inside data/ which is meant for data files. Your main code should live in the project root.

Next Steps (Before Tomorrow 3pm)

  1. Clean up your spec. Use this prompt with your agent:

    Read my project.spec.md. I'm building a portfolio tracker that
shows portfolio value, allocation warnings, and gain/loss for
securities. Help me clean up the formatting so each section is
properly filled in. Update the tech stack to include Pandas and
Matplotlib. Keep the scope realistic for 3 weeks.

  2. Scaffold your project. Use this prompt:

    Read my project.spec.md and the data science setup guide at
https://csc-121.path.app/unit-3/resources/data-science-setup.guide.llm.md
Set up my project: initialize uv, install pandas and matplotlib,
move my portfolio_analysis.py out of data/ to the project root,
and create a basic starting point I can run.

  3. Get one tiny slice working. Read your portfolio.txt, parse it into a data structure, and print a summary (total value, or a list of holdings). That's enough to prove the core loop works.

  4. Write your journal entry. Fill in the Checkpoint 1 section of project.journal.md.

  5. Commit and push.

Checkpoint 1 Readiness

By Thursday April 16th at 3pm, you need:

Helpful Resources