What this book covers
Chapter 1, Introduction and Installation of Python, offers a short introduction, and explains how to install Python and covers other related issues such as how to launch and quit Python.
Chapter 2, Using Python as an Ordinary Calculator, presents some basic concepts and several frequently used Python built-in functions, such as basic assignment, precision, addition, subtraction, division, power function, and square root function.
Chapter 3, Using Python as a Financial Calculator, teaches us how to write simple functions, such as functions to estimate the present value of one future cash flow, the future value of one present value, the present value of annuity, the future value of annuity, the present value of perpetuity, the price of a bond, and internal rate of return (IRR).
Chapter 4, 13 Lines of Python to Price a Call Option, shows how to write a call option without detailed knowledge about options and Python.
Chapter 5, Introduction to Modules, discusses modules, such as finding all available or installed modules, and how to install a new module.
Chapter 6, Introduction to NumPy and SciPy, introduces the two most important modules, called NumPy and SciPy, which are used intensively for scientific and financial computation.
Chapter 7, Visual Finance via Matplotlib, shows you how to use the matplotlib module to vividly explain many financial concepts by using graphs, pictures, color, and size.
Chapter 8, Statistical Analysis of Time Series, discusses many concepts and issues associated with statistics in detail. Topics include how to download historical prices from Yahoo! Finance; estimate returns, total risk, market risk, correlation among stocks, correlation among different countries' markets; form various types of portfolios; and construct an efficient portfolio.
Chapter 9, The Black-Scholes-Merton Option Model, discusses the Black-Scholes-Merton option model in detail. In particular, it will cover the payoff and profit/loss functions and their graphic presentations of call and put options, various trading strategies and their visual presentations, normal distribution, Greeks, and put-call parity.
Chapter 10, Python Loops and Implied Volatility, introduces different types of loops. Then it demonstrates how to estimate the implied volatility based on both European and American options.
Chapter 11, Monte Carlo Simulation and Options, discusses how to use Monte Carlo simulation to price European, American, average, lookback, and barrier options.
Chapter 12, Volatility Measures and GARCH, focuses on two issues: volatility measures and ARCH/GARCH.
