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...
Python for Finance
By :
Python for Finance
By:
Overview of this book
Table of Contents (20 chapters)
Python for Finance
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introduction and Installation of Python
Using Python as an Ordinary Calculator
Using Python as a Financial Calculator
13 Lines of Python to Price a Call Option
Introduction to Modules
Introduction to NumPy and SciPy
Visual Finance via Matplotlib
Statistical Analysis of Time Series
The Black-Scholes-Merton Option Model
Python Loops and Implied Volatility
Monte Carlo Simulation and Options
Volatility Measures and GARCH
Index
Customer Reviews