November 2020
Intermediate to advanced
378 pages
8h 47m
English
Content preview from Python for Algorithmic Trading
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Chapter 1. Python and Algorithmic Trading
At Goldman [Sachs] the number of people engaged in trading shares has fallen from a peak of 600 in 2000 to just two today.1
The Economist
This chapter provides background information for, and an overview of, the topics covered in this book. Although Python for algorithmic trading is a niche at the intersection of Python programming and finance, it is a fast-growing one that touches on such diverse topics as Python deployment, interactive financial analytics, machine and deep learning, object-oriented programming, socket communication, visualization of streaming data, and trading platforms.
For a quick refresher on important Python topics, read the Appendix A first.
Python for Finance
The Python programming language originated in 1991 with the first release by Guido van Rossum of a version labeled 0.9.0. In 1994, version 1.0 followed. However, it took almost two decades for Python to establish itself as a major programming language and technology platform in the financial industry. Of course, there were early adopters, mainly hedge funds, but widespread adoption probably started only around 2011.
One major obstacle to the adoption of Python in the financial industry has been the fact that the default Python version, called CPython, is an interpreted, high-level language. Numerical algorithms in general and financial algorithms in particular are quite often implemented based on (nested) loop structures. While compiled, low-level languages ...
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