What you need for this book

Here, we use several concrete examples to show what a reader could achieve after going through this book carefully.

First, after reading the first two chapters, a reader/student should be able to use Python to calculate the present value, future value, present value of annuity, IRR (internal rate of return), and many other financial formulae. In other words, we could use Python as a free ordinary calculator to solve many finance problems. Second, after the first three chapters, a reader/student or a finance instructor could build a free financial calculator, that is, combine a few dozen small Python programs into a big Python program. This big program behaves just like any other module written by others. Third, readers learn how to write Python programs to download and process financial data from various public data sources, such as Yahoo! Finance, Google Finance, Federal Reserve Data Library, and Prof. French's Data Library.

Fourth, readers will understand basic concepts associated with modules, which are packages written by experts, other users, or us, for specific purposes. Fifth, after understanding the Matplotlib module, readers can produce various graphs. For instance, readers could use graphs to demonstrate payoff/profit outcomes based on various trading strategies by combining the underlying stocks and options. Sixth, readers will be able to download IBM's daily price, the S&P 500 index price, and data from Yahoo! Finance and estimate its market risk (beta) by applying CAPM. They will also be able to form a portfolio with different securities, such as risk-free assets, bonds, and stocks. Then, they can optimize their portfolios by applying Markowitz's mean-variance model. In addition, readers will know how to estimate the VaR of their portfolios.

Seventh, a reader should be able to price European and American options by applying both the Black-Scholes-Merton option model for European options only, and the Monte Carlo simulation for both European and American options. Last but not least, readers will learn several ways to measure volatility. In particular, they will learn how to use AutoRegressive Conditional Heteroskedasticity (ARCH) and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models.