Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python for Finance

You're reading from   Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

Arrow left icon
Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction and Installation of Python FREE CHAPTER 2. Using Python as an Ordinary Calculator 3. Using Python as a Financial Calculator 4. 13 Lines of Python to Price a Call Option 5. Introduction to Modules 6. Introduction to NumPy and SciPy 7. Visual Finance via Matplotlib 8. Statistical Analysis of Time Series 9. The Black-Scholes-Merton Option Model 10. Python Loops and Implied Volatility 11. Monte Carlo Simulation and Options 12. Volatility Measures and GARCH Index

A useful dataset

With limited research funding, many teaching schools would not have a CRSP subscription. For them, we have generated a dataset that contains more than 200 stocks, 15 different country indices, Consumer Price Index (CPI), the US national debt, the prime rate, the risk-free rate, Small minus Big (SMB), High minus Low (HML), Russell indices, and gold prices. The frequency of the dataset is monthly. Since the name of each time series is used as an index, we have only two columns: date and value. The value column contains two types of data: price (level) and return. For stocks, CPI, debt-level, gold price, and Russell indices, their values are the price (level), while for prime rate, risk-free rate, SMB, and HML, the second column under value stands for return. The prime reason to have two types of data is that we want to make such a dataset as reliable as possible since any user could verify any number himself/herself. The dataset could be downloaded from http://canisius.edu...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime