Lesson 1 · 12 min
From zero to systematic trader
What quantitative trading is, how Quant Buffet teaches it, and the mindset you need before touching code.
MindsetWorkflowLab overview
A quantitative trader uses rules, data, and code — not gut feel — to decide when to buy and sell. You do not need a finance degree to start. You need curiosity, basic Python literacy, and patience to treat every backtest as research, not a profit promise.
Research idea
Read a paper or hypothesis: e.g. "assets with positive 12-month return tend to keep outperforming."
What makes trading "quant"?
| Discretionary trader | Quant / systematic trader |
|---|---|
| Reads news and charts subjectively | Encodes rules in code (if X, then buy Y) |
| Hard to reproduce decisions | Same inputs → same signals every time |
| Back-of-napkin risk guess | Sharpe, drawdown, and scenario stats |
| One market story | Tests 800+ academic ideas in a library |
How Quant Buffet helps beginners
- Strategy Library — peer-reviewed ideas with economic rationale and Python.
- Backtest lab (draft preview) — edit code, run in-browser, see equity curves.
- API docs — reference for
ASSETS,make_on_day, engine, and metrics. - This course — concepts before code, mapped to what the platform actually runs.
Before Lesson 2 — you should understand
- Quants express ideas as rules + data + simulation.
- Quant Buffet lab strategies use daily ETF prices and long-only weights.
- Your goal in Lesson 2–7 is to read data, infrastructure, orders, metrics, and classic patterns.