Finance with Python

in-depth online training course

ABOUT FINANCE WITH PYTHON

This is an in-depth online training course about Finance with Python that gives you the necessary background knowledge to proceed to more advanced topics in the field, like computational finance or algorithmic trading with Python.

Book the course today based on our special deal of 199 EUR or read on to learn more. Now includes 9+ hours of video instruction. The course was updated in QI of 2021.

No refunds possible since you get full access to the complete electronic course material (HTML, Jupyter Notebooks, Python codes, etc.). Also note that the course material is copyrighted and not allowed to be shared or distributed. It comes with no warranties or representations, to the extent permitted by applicable law.

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A Perfect Symbiosis

Both Quantitative and Computational Finance are fields in applied mathematics. For example, linear algebra, probability theory and analysis are fruitfully applied to phenomena and problems in financial markets. However, financial text books often use advanced mathematics and complex models from the beginning — diverting attention from the fundamental concepts and insights to the intricacies of the mathematical techniques. This course starts with the most simple models and progresses slowly to fully focus on the financial notions, results and applications first — thereby creating a solid understanding of important topics in Finance.

The Python programming language and its eco-system of powerful packages has become the technology platform of choice for Quantitative and Computational Finance. The syntax of the language is rather close to mathematical and financial notation such that translations from abstract mathematical models to executable Python codes are rather straightforward in general. In addition, packages like NumPy provide powerful vectorization approaches that make, for example, the coding of linear algebra operations highly efficient. It is therefore the ideal language for an introduction to computational aspects of Finance.

Topics of the course

This is an in-depth, intensive online course about Finance with Python (version 3.6). Such a course at the intersection of two vast and exciting fields can hardly cover all topics of relevance. However, it can cover a range of selected topics in-depth:

static two state economy: this is the most simple setting in which a discussion of Finance under uncertainty makes sense; it allows to introduce concepts such as replication, arbitrage, complete markets, option pricing or mean-variance portfolios
static three state economy: adding on state to the model economy represents a natural progression and allows for the analysis of Finance in incomplete markets
optimality and equilibrium: expected utility maximizing (representative) agents underlie much of the economic theory in Finance; topics such as optimal investment portfolios, optimal consumption-saving plans or the equilibrium pricing of financial assets and derivatives are central in Finance
general static economy: equipped with a solid background from the most simple model framework, generalizations to more complex models are seamless; the formalism mainly carries over to economies with potentially infinite discrete future states
dynamic economy: some of the most important results in Quantitative and Computational Finance are derived in dynamic model economies that cover a potentially infinite number of discrete points in time; a major example is the binomial option pricing model to price both European and American put and call options

An incomplete list of the technical and financial topics comprises: static model economy, money & currency, agents, real assets, financial assets, cash flow, present value, net present value, uncertainty, return, interest, probability, expectation, expected return, volatility, contingent claim, replication, arbitrage, market completeness, market incompleteness, Arrow-Debreu securities, state prices, martingale measure, fundamental theorem of asset pricing, risk-neutral pricing, mean-variance portfolios, attainable contingent claims, span of financial assets, super-replication, approximative replication, capital market line, capital asset pricing model, optimality, equilibrium, utility function, preferences, time-additive utility, expected utility, arbitrage pricing, martingale pricing, pricing in incomplete markets, equilibrium pricing.

Finance with Python Excerpt

Have a look at the (current) table of contents of the PDF version of the online course material.

Uniqueness and Benefits

The course offers a unique learning experience with the following features and benefits.

coverage of relevant topics: it is the only course introducing to Finance from fundamental principles using Python
self-contained code base: the course is accompanied by a comprehensive set of Jupyter Notebooks containing all the codes from the course material; for interactive exploration and a learning experience with direct feedback
book version as PDF: in addition to the online version of the course, there is also a book version as PDF (160+ pages as of January 2021)
video instruction (9+ hours): this course now comes with 9+ hours of video instruction explaining the most important topics and models based on live coding sessions
real-world application as the goal: the focus is on the practical, computational aspects of Finance such that the student can directly make use of the material in academic contexts as well as at work in a financial institution
do-it-yourself & self-paced approach: since the material, videos and the codes are self-contained and only relying on standard Python packages, the student has full knowledge of and full control over what is going on, how to use the code examples, how to change them, etc; there is no need to rely on third-party platforms or applications
user forum: although you are supposed to study pro-actively by yourself, we are there to help; you can post questions and comments in our user forum; we aim to follow up within 24 hours

About the course author

Dr. Yves J. Hilpisch is founder and CEO of The Python Quants, a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading and computational finance.

Yves is the author of five books: Artificial Intelligence in Finance (2020, O’Reilly), Python for Algorithmic Trading (2020, O’Reilly), Python for Finance (2018, 2nd ed., O’Reilly), Listed Volatility and Variance Derivatives (2017, Wiley Finance) and Derivatives Analytics with Python (2015, Wiley Finance).

Git Repository

All Python codes and Jupyter Notebooks are provided as a Git repository on the Quant Platform for easy updating and also local usage. Make sure to have a comprehensive scientific Python 3.6 installation ready.

Order the course

Sign up today for only 199 Euro and secure access to all future updates. It has never been easier to learn Finance with Python.

Simply place your order through PayPal for which you can also use your credit card.

No refunds possible since you get full access to the complete electronic course material (HTML, Jupyter Notebooks, Python codes, etc.). Also note that the course material is copyrighted and not allowed to be shared or distributed. It comes with no warranties or representations, to the extent permitted by applicable law.

Get & Keep in Touch

DR YVES J HILPISCH

Write me under training@tpq.io. Stay informed about the latest in Open Source for Quant Finance by signing up below.

THE PYTHON QUANTS

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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