Derivatives Analytics with Python

Data Analysis, Models, Simulation, Calibration and Hedging

Derivatives Analytics with Python

What others say.

In Academia

“There is currently much excitement about the application of Python to Quant Finance in both academia and the financial markets. Yves’ monumental undertaking guides the reader through the mathematical and numerical aspects of derivative valuation with programming in Python, in an expert and pedagogical manner. I will be making his publication the standard text for all my Computational Finance courses.” Dr Riaz Ahmad — Fitch Learning and Dept. of Mathematics, University College London​.

“Another excellent offering from Dr. Hilpisch. This book has a very good coverage of derivatives analytics and their implementations in Python.” Alain Ledon — Adjunct Professor, Baruch Master in Financial Engineering.

In the Markets

“A thorough overview of the state of the art in equity derivatives pricing and how to apply it using Python, with an implementor’s eye to detail.” Dr Mark Higgins — CEO, Washington Square Technologies, former co-head of Quantitative Research for JPMorgan’s Investment Bank.

“A must read for any practitioner who is serious about implementing Python across their derivatives platform. Dr Hilpisch excels at simplifying complex state-of-the-art techniques for both the pricing and hedging of derivatives in Python that both operators and academics will appreciate.” Bryan Wisk — Founder and CIO, Asymmetric Return Capital, LLC.


About the author

Yves Hilpisch is founder and CEO of The Python Quants (cf. and The AI Machine. The group focuses on Open Source technologies for Financial Data Science, Algorithmic Trading and Computational Finance. It also provides data, financial and derivatives analytics software (cf. Quant Platform and DX Analytics) as well as consulting services and Python for Finance trainings.

Yves is also author of the books Artificial Intelligence in Finance (O’Reilly, 2020), Python for Finance — Mastering Data-Driven Finance (2nd ed., O’Reilly, 2018) and Listed Volatility and Variance Derivatives (Wiley, 2016). As a graduate in Business Administration with a Dr.rer.pol. in Mathematical Finance, he lectures on Computational Finance at the CQF Program and is Adjunct Professor of Computational Finance at Miami Business School. Yves is also the director of the first University Certificate Programs in Python for Algorithmic Trading and Computational Finance

Furthermore, Yves organizes Python & Open Source for Quant Finance meetups and events in Frankfurt (cf. Open Source in Quant Finance), London (cf. Python for Quant Finance) and New York (cf.  For Python Quants).

Quant Platform

All Python codes (scripts, modules, etc. — a total of over 5,000 lines of codes) as well as complementary Jupyter Notebooks for immediate execution are available on the Quant Platform. No installation necessary, just an easy and quick registration under

brief tutorial explaining how to register

Below you find a brief tutorial explaining how to register and how to use the IPython/Jupyter Notebooks and all Python code files on the Quant Platform.

Github Repository

All Jupyter Notebooks and all Python code files for easy cloning and local usage. Make sure to have a comprehensive scientific Python installation (2.7.x) ready.

DX Analytics

DX Analytics is a purely Python-based derivatives and risk analytics library which implements all models and approaches presented in the book (e.g. stochastic volatility & jump-diffusion models, Fourier-based option pricing, least-squares Monte Carlo simulation, numerical Greeks) on the basis of a unified API.

Our Training

We are offering Python for Finance online training classes — leading to a University Certification — about Financial Data Science, Algorithmic Trading and Computational Finance. In addition, we also offer customized corporate training classes. See or just get in touch below.

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The Experts in Data-Driven and AI-First Finance with Python. We focus on Python and Open Source Technologies for Financial Data Science, Artificial Intelligence, Algorithmic Trading and Computational Finance.

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