Python for Finance: Analyze Big Financial Data

Part I. Python and Finance

This part introduces Python for finance. It consists of three chapters:

Chapter 1 briefly discusses Python in general and argues why Python is indeed well

suited to address the technological challenges in the finance industry and in financial

(data) analytics.

Chapter 2, on Python infrastructure and tools, is meant to provide a concise overview

of the most important things you have to know to get started with interactive

analytics and application development in Python; the related Appendix A surveys

some selected best practices for Python development.

Chapter 3 immediately dives into three specific financial examples; it illustrates how

to calculate implied volatilities of options with Python, how to simulate a financial

model with Python and the array library NumPy, and how to implement a backtesting

for a trend-based investment strategy. This chapter should give the reader a feeling

for what it means to use Python for financial analytics — details are not that

important at this stage; they are all explained in Part II.