Python for Finance, 3rd Edition

Chapter 14. Statistics

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 14th chapter of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

Statistics underpins almost every quantitative finance task in this book: modeling returns, diagnosing distributional assumptions, building diversified portfolios, and interpreting model outputs and forecasts. This chapter revisits core statistical concepts with practical Python examples that you can extend in later, more specialized chapters.

You start with simple diagnostics for normality, using geometric Brownian motion as a benchmark model and then confronting it with real market data. You then explore portfolio-level statistics and efficient frontiers, before closing with a compact Bayesian example that illustrates how to update beliefs in light of new evidence. The machine-learning view of statistics is deferred to Chapter 15, where you build larger predictive models on top of the tools introduced here.

The chapter develops a compact, finance-focused statistics toolkit: normality diagnostics for simulated and real-world return series, portfolio mean-variance statistics, ...