Derivatives Analytics with Python
Data Analysis, Models, Simulation, Calibration and Hedging
What others say.
“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.
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
Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (https://tpq.io) and The AI Machine (https://aimachine.io). The group focuses on Open Source technologies for Financial Data Science, Artificial Intelligence, Algorithmic Trading, Computational Finance, and Asset Management. It also provides data, financial and derivatives analytics software (see Quant Platform and DX Analytics) as well as consulting services and Python for Finance online and corporate training programs.
Yves is author of the books Financial Theory with Python (O’Reilly, 2021), Artificial Intelligence in Finance (O’Reilly, 2020), Python for Algorithmic Trading (O’Reilly, 2020), Python for Finance (2nd ed., O’Reilly, 2018), Derivatives Analytics with Python (Wiley, 2015) and Listed Volatility and Variance Derivatives (Wiley, 2017). 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. Yves is also the director of the University Certificate Programs in Python for Algorithmic Trading, Computational Finance, and Asset Management.
Furthermore, Yves organizes Python for Finance, AI, and Algorithmic Trading Meetup group events in Berlin, Frankfurt, Paris, London (see Python for Quant Finance) and New York (see AI & Algo Trading).
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.
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.
We are offering comprehensive Python for Finance online training programs — leading to University Certificates — about Financial Data Science, Algorithmic Trading, Computational Finance, and Asset Management. In addition, we also offer customized corporate training classes. See https://home.tpq.io/certificates or just get in touch below.
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.