Finance with Python (The Python Quants)

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Finance with Python
An In-Depth Online Training Course

Данный курс является изучением финансов на пайтоне для создания фундаментальной основы финансового инжиниринга и алгоритмического трейдинга.

In the past, many people contacted us and said that their knowledge of finance theory and related mathematical concepts has become a bit rusty. Others pointed out that they need a more gentle introduction to Python programming compared to what we offer with our professional training classes («Python for Algorithmic Trading»). The brand new online course Finance with Python is the right choice when you are looking for an introduction to financial theory from fundamental principles and simultaneously want to learn the very basics of using Python for computational finance. It is the perfect starting point for almost everybody.

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.

.SpoilerTarget»>Спойлер: РћР± авторе: Yves Hilpisch has 10 years of experience with Python, particularly in the finance space.
He founded The Python Quants GmbH — an independent, privately-owned analytics software provider and financial engineering boutique. The company provides Python-based financial and derivatives analytics software as well as consulting, development and training services related to Python, Open Source and Finance.
We are proud to be named Top 10 Banking Analytics Solution Provider of 2017 by Banking CIO Outlook.
He lectures on Mathematical Finance at Saarland University in Germany and is a regular speaker at Python and Finance conferences.

Автор данного курса также является автором 3 книг по данной теме (Python Books about Quantitative and Computational Finance):

• Python for Finance. Analyze Big Financial Data
• Derivatives Analytics with Python. Data Analysis, Models, Simulation, Calibration and Hedging
• Listed Volatility and Variance Derivatives. A Python-based Guide

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.
• book version as PDF: in addition to the online version of the course, there is also a book version as PDF (100+ pages as of mid-March 2017).
• 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

.SpoilerTarget»>Спойлер: Содержание РєСѓСЂСЃР°: Table of Contents
Copyright
Preface
Why this Course?
Target Audience
Overview of the Course
Bibliography

1. Finance and Python
1.1. Introduction
1.2. A Brief History of Finance
1.3. A Four Languages World
1.4. The Approach of this Course
1.5. Getting Started with Python
1.6. Conclusions
1.7. Further Resources

2. Two State Economy
2.1. Introduction
2.2. Economy
2.3. Real Assets
2.4. Agents
2.5. Time
2.6. Money
2.7. Cash Flow
2.8. Return
2.9. Interest
2.10. Present Value
2.11. Net Present Value
2.12. Uncertainty
2.13. Financial Assets
2.14. Probability
2.15. Expectation
2.16. Expected Return
2.17. Volatility
2.18. Contingent Claims
2.19. Replication
2.20. Arbitrage Pricing
2.21. Market Completeness
2.22. Arrow-Debreu Securities
2.23. Martingale Measure
2.24. First Fundamental Theorem of Asset Pricing
2.25. Martingale Pricing
2.26. Second Fundamental Theorem of Asset Pricing
2.27. Mean-Variance Portfolios
2.28. Conclusions
2.29. Further Resources

3. Three State Economy
3.1. Introduction
3.2. Uncertainty
3.3. Financial Assets
3.4. Attainable Contingent Claims
3.5. Martingale Measures
3.6. Arbitrage and Martingale Pricing
3.7. Super-Replication
3.8. Approximative Replication
3.9. Capital Market Line
3.10. Capital Asset Pricing Model
3.11. Conclusions
3.12. Further Resources

4. Optimality and Equilibrium
4.1. Introduction
4.2. Utility Maximization
4.3. Graphical Solution
4.4. Appropriate Utility Functions
4.5. Logarithmic Function
4.6. Time-Additive Utility
4.7. Expected Utility
4.8. Optimal Investment Portfolio
4.9. Time-Additive Expected Utility
4.10. Pricing in Complete Markets
4.11. Arbitrage Pricing
4.12. Martingale Pricing
4.13. Risk-Less Interest Rate
4.14. A Numerical Example I
4.15. Pricing in Incomplete Markets
4.16. Martingale Measures in Incomplete Markets
4.17. Equilibrium Pricing of Contingent Claims
4.18. A Numerical Example II
4.19. Conclusions
4.20. Further Resources

Author Biography

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

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+ Бонус Derivatives Analytics with Python (webcast) ( http://www.oreilly.com/pub/e/3086 )

Р’ качестве бесплатного Р±РѕРЅСѓСЃР° Рє РєСѓСЂСЃСѓ предлагается запись вебкаста автора «Derivatives Analytics with Python» (In this webcast you will learn how Python can be used for Derivatives Analytics and Financial Engineering. Dr. Yves Hilpisch will begin by covering the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. The approach is a practical one in that all-important aspects are illustrated by a set of self-contained Python scripts.)

Derivatives Analytics with Python

Date: This event took place live on June 24 2014

Presented by: Yves Hilpisch

Duration: Approximately 60 minutes.

Watch the webcast recording
In this webcast you will learn how Python can be used for Derivatives Analytics and Financial Engineering. Dr. Yves J. Hilpisch will begin by covering the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. The approach is a practical one in that all-important aspects are illustrated by a set of self-contained Python scripts.

This webcast talk will cover:

• Financial Algorithm Implementation
  • Monte Carlo Valuation
  • Binomial Option Pricing
• Performance Libraries
  • Dynamic Compiling
  • Parallel Code Execution
• DX Analytics
  • Overview and Philosophy
  • Multi-Risk Derivatives Pricing
  • Global Valuation
• Web Technologies for Derivative Analytics

About Yves Hilpisch
Yves Hilpisch has 10 years of experience with Python, particularly in the finance space. He founded The Python Quants GmbH — an independent, privately-owned analytics software provider and financial engineering boutique. He lectures on Mathematical Finance at Saarland University in Germany and is a regular speaker at Python and Finance conferences.

Продающий сайт:
http://finpy.tpq.io
http://www.oreilly.com/pub/e/3086 (Р±РѕРЅСѓСЃ)
http://finpy.tpq.io/finpy_excerpt.pdf
Ознакомительный фрагмент авторского учебника (book version as PDF)

Наша
Автор: Yves Hilpisch
Год выпуска курса: 2017

Сколько весит курс: 355 Мб

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