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Python for Finance

You're reading from Python for Finance Apply powerful finance models and quantitative analysis with Python

Product type Paperback

Published in Jun 2017

Publisher

ISBN-13 9781787125698

Length 586 pages

Edition 2nd Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Analysis

Author (1):

Yuxing Yan

View More author details

Table of Contents (17) Chapters

Preface

1. Python Basics FREE CHAPTER

2. Introduction to Python Modules

3. Time Value of Money

4. Sources of Data

Diving into deeper concepts

5. Bond and Stock Valuation

6. Capital Asset Pricing Model

7. Multifactor Models and Performance Measures

8. Time-Series Analysis

9. Portfolio Theory

10. Options and Futures

11. Value at Risk

12. Monte Carlo Simulation

13. Credit Risk Analysis

14. Exotic Options

15. Volatility, Implied Volatility, ARCH, and GARCH

Index

Two general formulae for many functions

This section is optional since it is quite complex in terms of mathematical expression. Skipping this section would not have any impact on the understanding of the other chapters. Thus, this section is for advanced learners. Up to now in this chapter, we have learnt the usage of several functions, such as pv(), fv(), nper(), pmt(), and rate() included in the SciPy module or numpy.lib.financial submodule. The first general formula is related to the present value:

Two general formulae for many functions

On the right-hand side of the preceding equation, the first one is the present value of one future cash flow, while the second part is the present value of annuity. The variable type takes a value of zero (default value); it is the present value of a normal annuity, while it is an annuity due if type takes a value of 1. The negative sign is for the sign convention. If using the same notation as that used for the functions contained in SciPy and numpy.lib.financial, we have the following formula...

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