Finance with Python (course) [The Python Quants]

Finance with Python
An In-Depth Online Training Course

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

Другой курс автора исключительно по алготрейдингу на пайтоне "Python for Algorithmic Trading (The Python Quants)".​

Предыстория создания данного курса от автора:

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.

Спойлер: ОБ 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

Спойлер: СОДЕРЖАНИЕ КУРСА

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

В качестве бесплатного бонуса к курсу 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.)