Backtesting Essentials: Research Principles & Python Implementation

By Martin Mayer-Krebs

A rigorous guide to building your own trading strategy research tools-line by line.

Backtesting Essentials bridges the gap between theory and implementation, offering a practical, hands-on approach to designing custom backtesting engines in Python. With a focus on precision, statistical rigor, and transparency, this book walks you through the core principles of strategy evaluation-from the dangers of survivorship and look-ahead bias, to the nuances of transaction costs, performance metrics, and parameter optimization.

You will learn:

How to identify and avoid common backtesting mistakes

How to build a backtesting engine from scratch using object-oriented programming

How to incorporate realistic execution assumptions, including limit orders and market impact

How to evaluate strategies using proper statistical inference, including Monte Carlo simulations and multiple hypothesis testing

How to interpret Sharpe ratios, drawdowns, and exposure with clarity

How to assess strategy robustness through walk-forward analysis and attribution techniques

Written for traders, quants, and researchers, this book is not about using off-the-shelf tools-it is about understanding and constructing the tools themselves. Whether you are backtesting equity portfolios, futures, or crypto strategies, Backtesting Essentials will teach you what most platforms hide: the underlying assumptions.

Includes complete Python implementations and a public GitHub repository.

• Format: Paperback
• Language: English
• ISBN: B0FB2XFNTB
• ISBN13: 9798999017710
• Release Date: June 2025
• Publisher: Quantitative Modelling & Research Press
• Length: 216 Pages
• Weight: 0.66 lbs.
• Dimensions: 9.0" x 0.5" x 6.0"