"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.
"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.
Yves Hilpisch is founder and managing partner of The Python Quants Group (cf. http://tpq.io). The group focuses on Open Source Software for Quant Finance and provides data, financial and derivatives analytics software (cf. datapark & Quant Platform & DX Analytics) as well as consulting services and trainings related to Python and Open Source for Quant Finance.
Yves is also author of the book Python for Finance — Analyze Big Financial Data (O'Reilly, 2014). As a graduate in Business Administration with a Dr.rer.pol. in Mathematical Finance, he lectures on Computational Finance at the CQF Program.
Furthermore, Yves organizes Open Source for Quant Finance meetups and conferences in Frankfurt (cf. Open Source in Quant Finance), London (cf. Python for Quant Finance) and New York (cf. For Python Quants).
All Python codes (scripts, modules, etc. — a total of over 5,000 lines of codes) as well as complementary IPython/Jupyter Notebooks for immediate execution are available on the Quant Platform. No installation necessary, just an easy and quick registration under
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.
All IPython 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.
This 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).
I am offering trainings about Derivatives Analytics with Python and Python for Finance — for example based on this and my O'Reilly book. I also offer customized trainings about Python, Data Analytics, Computational Finance, Derivatives & Risk Analytics. Just get in touch below.
Write me under firstname.lastname@example.org. Stay informed about the latest in Open Source for Quant Finance by signing up below.