Tidy Finance with Python

Tidy Finance with Python brings rigorous empirical finance within reach of Python users. The book demystifies how to move from theoretical finance and econometrics to clean, reproducible code, making it easier to conduct financial research from the ground up.

From Tidy Data to Real-World Datasets

The journey starts with tidy data principles and the core Python stack — pandas, numpy, and plotnine. You’ll learn how to retrieve and organize widely used financial data sources such as CRSP, Compustat, Mergent FISD, and TRACE into a structured database, then reuse that data across every subsequent chapter.

Essential Applications in Empirical Finance 📊

Each chapter is self-contained, focusing on practical techniques: beta estimation, portfolio sorts, performance evaluation, and replication of Fama-French factors. The book then moves into modeling territory with fixed effects, clustered standard errors, difference-in-differences, and machine learning methods including ridge regression, lasso, elastic net, random forests, and neural networks.

Portfolio Optimization and Backtesting

The final section tackles portfolio choice from a code-first perspective — parametric portfolio policies, constrained optimization, and out-of-sample backtesting. Transaction costs and estimation uncertainty are addressed, giving you tools to evaluate trading strategies honestly.

Built for Reproducibility and Learning

A defining feature of the Tidy Finance approach is transparency: every figure, table, and number in the book can be replicated by copying the provided code. End-of-chapter exercises support self-study and classroom use, making this a versatile resource for students, instructors, and practitioners alike.

Whether you’re validating an investment idea, teaching empirical methods, or building a foundation for research, Tidy Finance with Python offers a clear, reproducible path from data to insight.