Chapter 3. Introductory Examples

Quantitative analysis, as we define it, is the application of mathematical and/or statistical methods to market data.

— John Forman

This chapter dives into some concrete examples from quantitative finance to illustrate how

convenient and powerful it is to use Python and its libraries for financial analytics. The

focus lies on the flow of the exposition, and a number of details that might be important in

real-world applications are not touched upon. Also, details of Python usage are mainly

skipped because later chapters explain them further.

Specifically, this chapter presents the following examples:

Implied volatilities

Option quotes for certain maturity dates are taken to back out the implied volatilities

of these options and to plot them — a task option traders and risk managers, among

others, are faced with on a daily basis.

Monte Carlo simulation

The evolution of a stock index over time is simulated via Monte Carlo techniques,

selected results are visualized, and European option values are calculated. Monte

Carlo simulation is a cornerstone for numerical option pricing as well as for risk

management efforts involving value-at-risk calculations or credit value adjustments.

Technical analysis

An analysis of historical time series data is implemented to backtest an investment

strategy based on trend signals; both professional investors and ambitious amateurs

regularly engage in this kind of investment analysis.

All examples have to deal in some ways with date-time information. Appendix C

introduces handling such information with Python, NumPy, and pandas.