Python for Finance: Analyze Big Financial Data

Conclusions

Without going into too much detail, this chapter illustrates the use of Python by the means

of concrete and typical financial examples:

Calculation of implied volatilities

Using real-world data, in the form of a cross section of option data for a given day,

we calculate numerically the implied volatilities of European call options on the

VSTOXX volatility index. This example introduces some custom Python functions

(e.g., for analytical option valuation) and uses functionality from NumPy, SciPy, and

pandas.

Monte Carlo simulation

Using different implementation approaches, we simulate the evolution of an index

level over time and use our simulated end-of-period values to derive Monte Carlo

estimators for European call options. Using NumPy, the major benefits of vectorization

of Python code are illustrated: namely, compactness of code and speed of execution.

Backtesting of trend signal strategy

Using real historical time series data for the S&P 500, we backtest the performance

of a trading strategy based on signals generated by 42-day and 252-day trends

(moving averages). This example illustrates the capabilities and convenience of

pandas when it comes to time series analytics.

In terms of working with Python, this chapter introduces interactive financial analytics

(using the IPython interactive shell), working with more complex functions stored in

modules, as well as the performance-oriented implementation of algorithms using

vectorization. One important topic is not covered: namely, object orientation and classes in

Python. For the curious reader, Appendix B contains a class definition for a European call

option with methods based on the functions found in the code of Example 3-1 in this

chapter.