کتاب "امور مالی کمی با پایتون: راهنمای عملی برای مدیریت سرمایه‌گذاری، معاملات و مهندسی مالی" پلی است میان تئوری مالی ریاضی و کاربردهای عملی آن در قیمت‌گذاری مشتقات و مدیریت پرتفو. این کتاب، مقدمه‌ای دقیق و کاملاً عملی بر موضوعات بنیادی مالی کمی، مانند قیمت‌گذاری اختیار معامله، بهینه‌سازی پرتفو و یادگیری ماشین، برای دانشجویان فراهم می‌کند. در عین حال، خواننده از تمرکز ویژه بر کاربردهای واقعی این مفاهیم در سرمایه‌گذاری نهادی نیز بهره‌مند می‌شود.

ویژگی‌ها:

• قابل استفاده هم به‌عنوان منبع آموزشی و هم به‌عنوان ابزار عملی برای سرمایه‌گذاران حرفه‌ای
• کتاب درسی ایده‌آل برای دانشجویان سال اول کارشناسی ارشد در رشته‌های مالی کمی مانند مالی ریاضی، مالی کمی یا مهندسی مالی
• ارائه دیدگاهی از آینده‌ی تکنیک‌های مالی کمی، همراه با معرفی اولیه مفاهیم یادگیری ماشین
• دسترسی رایگان به کدهای پایتون از طریق مخزن آنلاین در:

Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors.

Features

Useful as both a teaching resource and as a practical tool for professional investors.

Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering.

Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning.

Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on

Table of Contents

SECTION I: Foundations of Quant Modeling

CHAPTER 1: Setting the Stage: Quant Landscape

CHAPTER 2: Theoretical Underpinnings of Quant Modeling: Modeling the Risk Neutral Measure

CHAPTER 3: Theoretical Underpinnings of Quant Modeling: Modeling the Physical Measure

CHAPTER 4: Python Programming Environment

CHAPTER 5: Programming Concepts in Python

CHAPTER 6: Working with Financial Datasets

CHAPTER 7: Model Validation

SECTION II: Options Modeling

CHAPTER 8: Stochastic Models

CHAPTER 9: Options Pricing Techniques for European Options

CHAPTER 10: Options Pricing Techniques for Exotic Options

CHAPTER 11: Greeks and Options Trading

CHAPTER 12: Extraction of Risk Neutral Densities

SECTION Ill: Quant Modeling in Different Markets

CHAPTER 13: Interest Rate Markets

CHAPTER 14: Credit Markets

CHAPTER 15: Foreign Exchange Markets

CHAPTER 16: Equity & Commodity Markets

SECTION IV: Portfolio Construction & Risk Management

CHAPTER 17: Portfolio Construction & Optimization Techniques

CHAPTER 18: Modeling Expected Returns and Covariance Matrices

CHAPTER 19: Risk Management

CHAPTER 20: Quantitative Trading Models

CHAPTER 21: Incorporating Machine Learning Techniques

Review

"This ambitious book is a practical guide for aspirant quants, on both the buyside and the sellside. It includes a 175-page supplement of code and exercises to provide a solid coding baseline.

The subject matter is neatly partitioned into 21 chapters, starting with an overview of the quant landscape and ending with basic machine learning in finance. The flow between chapters makes the book a pleasure to read, and readers can easily access topics of particular interest.

The author is both a lecturer and a practitioner in the field. This is evident from the accessible writing style, comprehensive examples, and the way topics are logically built up. The content strikes a good balance between theory and practice, covering a broad range of finance topics—from swaptions and currency triangles to CDO mechanics and feature explainability in machine learning. Few books in this space are as comprehensive.

Readers will find a strong selection of case studies throughout the book. The author's real-world experience enables him to write with conviction, and the commentary is accessible and free of jargon. These case studies, such as the 2018 natural gas options squeeze and the 2021 Reddit meme GameStop squeeze, are valuable cautionary tales for newcomers.

Finance students in their final years and those beginning careers as quants will find this book a useful resource. It may be considered an equally comprehensive but more practical complement to Hull’s classic Options, Futures, and Other Derivatives."

— Mark Greenwood, Quantitative Finance

About the Author

Chris Kelliher is a Senior Quantitative Researcher in the Global Asset Allocation group at Fidelity Investments. In addition, Mr. Kelliher is a Lecturer in the Masters in Mathematical Finance and Financial Technology program at Boston University's Questrom School of Business. In this role he teaches multiple graduate level courses including Computational Methods in Finance, Fixed Income & Programming for Quant Finance. Prior to joining Fidelity in 2019, Mr. Kelliher served as a portfolio manager for RDC Capital Partners. Before joining RDC, Mr. Kelliher served as a principal and quantitative portfolio manager at a leading quantitative investment management firm, FDO Partners. Prior to FDO, Mr. Kelliher was a senior quantitative portfolio analyst and trader at Convexity Capital Management and a senior quantitative researcher at Bracebridge Capital. He has been in the financial industry since 2004. Mr. Kelliher earned a BA in Economics from Gordon College, where he graduated Cum Laude with Departmental Honours, and an MS in Mathematical Finance from New York University's Courant Institute.