I wanted to automate chores in MarketWatch’s virtual stock game (paper trading, not a real broker). marketwatch on PyPI is the small client I shipped — watchlists, game state, portfolio, orders, leaderboard. Version française.
Why build this
Course and side projects already used BatchBacktesting on historical data — see experimentation with indicators. MarketWatch’s game is a different beast: session cookies, game rules, and leaderboards that change with the semester. A thin Python wrapper beat curl scripts in a notebook.
I also wanted a documented surface for teammates: “here is how you list games” beats sharing a gist that rots when the HTML form changes. The package is intentionally small — no ORM, no strategy engine — just HTTP shaped like the site.
Design choices
| Choice | Reason |
|---|---|
| Class-based client | One login, many calls in a script |
| Explicit methods per endpoint | Easier to grep when MarketWatch changes a path |
| Docs on GitHub Pages | PyPI description alone is too short for auth edge cases |
| No bundled strategies | Keeps the library neutral; strategies belong in your repo |
Links
- Package: pypi.org/project/marketwatch
- Documentation: antoinebou12.github.io/marketwatch
- Source & issues: github.com/antoinebou12/marketwatch
What it can do
| Area | Methods (see docs for signatures) |
|---|---|
| Watchlists | Create, list, update symbols |
| Games | List games, read settings |
| Portfolio | Positions, pending orders |
| Trading | Buy / sell inside game rules |
| Social | Leaderboard for a game |
Useful for small bots and screening inside the game — not HFT, not brokerage.
Quick start
pip install marketwatch
from marketwatch import MarketWatch
mw = MarketWatch("your_username", "your_password")
mw.get_games()
mw.get_price("AAPL")
Login edge cases (2FA absent, session expiry) are documented on the site — read before cron-scheduling anything.
Limits and responsibility
- Unofficial API — HTML/JSON endpoints can change; pin versions and expect breakage.
- Terms of use — automation may conflict with MarketWatch rules; use for learning, not abuse.
- Not financial advice — paper gains do not validate a strategy; pair with honest backtests on separate data.
For research-style batch runs on many tickers, see multiple indicators backtesting and Monte Carlo risk bands.
Related posts
- Experimentation with technical indicators
- Economics of LEGO with data science — another “Python for curiosity” project
Questions or bugs: GitHub issues.