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.

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