Python for Quant

This is the learning path for the "Python Quantitative Programming" series: from "Why Learn Python" to "Independently Writing Strategy Engineering," listing all materials on the site in order. Follow the sequence; do not skip.

One-Sentence Conclusion

Stage	What to Do	Site Entry
Start (1-2 weeks)	Set up environment, learn core Numpy/Pandas syntax	01 Why Learn Python → Numpy/Pandas
Advanced (1-2 months)	Efficient coding, data processing, visualization	chap06 Efficient Coding → Visualization Series
Practical (Ongoing)	Engineering standards: unit tests, version control, documentation	chap08/10 → Real projects

I. Start: Environment + Numpy/Pandas

01 Why Learn Python clarifies "why Python is chosen for quantitative finance" (ecosystem, not the language itself). Then:

1. 02 Programming Development Environment — Configure virtual environments and editors properly in one go; stop jumping back and forth between environments.
2. Numpy/Pandas 20-Episode Series — Core syntax + application cases; this is the most complete free tutorial on this site, bar none.
3. Efficient Numpy Programming — "Speed up 10x" tricks like Mask Arrays and find_runs; usable immediately after learning.

The goal of this stage: When seeing any data processing requirement, your first reaction should be "how to vectorize it," not "how to write a for loop."

II. Advanced: Write Fast, But More Importantly, Write Correctly

• 06 Efficient Coding — 10x speed habits: profile first, vectorize first, cache intermediate results.
• Visualization Series — matplotlib layout, Dash for web pages, how to perform statistical inference using Q-Q plots (Why Q-Q plots are useful).
• Scaling and Normalization of Quantitative Strategies — Read alongside pitfalls in data standardization; if preprocessing is wrong, everything downstream is wrong.

The goal of this stage: Code reproducibility — the same script, on a different machine or three months later, yields the same results.

III. Practical: Engineering Standards (Distinguishing "Can Write" from "Can Use")

• 08 Code Version Control — Quantitative code without git is as good as unwritten.
• Unit Tests and Continuous Integration — How to write unit tests for strategy code (chap02 also covers this).
• 10 Writing Technical Documentation — Writing for your future self (three months from now).

IV. Further Reading

• Numpy/Pandas 20-Episode Full Series — Core of this series.
• Dash: Making Web Pages with Python — Build a dashboard for your strategies.
• Adjustment Issues — An unavoidable lesson in data engineering.

The biggest pitfall in learning Python is "always being a beginner." Finish the sequence above, and you graduate — the rest is learned through practice.