Course Orientation
Meet the course goals, research boundaries, and starting points for different backgrounds.
No quantitative background required. Start by understanding the market, then learn to ask questions, find data, and test your ideas with evidence.
Begin with no prior experience
No background in stocks, quant, or Python required
Learn how research works
Ask questions, find data, and examine the evidence
Let AI help with the code
You decide whether the results are trustworthy
Complete learning path
The course is arranged in learning order. Start or continue your current unit, or open the outline to see what each lesson helps you solve.
Part 0
Understand what the course offers, what it cannot promise, and where you should begin.
Meet the course goals, research boundaries, and starting points for different backgrounds.
Part 1
Start with how markets work, then build a connected view of market forces, strategies, and quantitative research.
Understand the basic relationships among shares, companies, expectations, trading, and prices.
Place companies, industries, the economy, policy, participants, news, and market traces on one factor map.
Understand investment and trading approaches through advantage source, time scale, and research method.
Turn fuzzy intuition into a research question that can be defined, computed, compared, and checked against evidence.
Part 2
Move into a local research environment, learn validation and research-grade backtesting through two cases, then investigate your own market idea.
Organize data, code, and results on your computer, then work with an AI research assistant safely and reproducibly.
Content in preparation
Opens after production and review.
Use a tightly defined lucky-number hypothesis to complete a first end-to-end quantitative experiment.
Content in preparation
Opens after production and review.
Study a technical signal that unfolds over time, including event definitions, observation windows, and comparisons.
Content in preparation
Opens after production and review.
Advance an early pattern into a research-grade backtest with rules, costs, robustness checks, and conclusion boundaries.
Content in preparation
Opens after production and review.
Use the same research process to define, implement, inspect, and honestly answer your own market question.
Content in preparation
Opens after production and review.