Python for Finance 2e: Mastering Data-Driven Finance Paperback – Illustrated, 31 December 2018
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Why Python for Finance
Python is a high-level, multipurpose programming language that is used in a wide range of domains and technical fields. Python is used by the beginner programmer as well as by the highly skilled expert developer, at schools, in universities, at web companies, in large corporations and financial institutions, as well as in any scientific field.
Python as a language—and even more so as an ecosystem—is an ideal technological framework for the financial industry as whole and the individual working in finance alike. It is characterized by a number of benefits, like an elegant syntax, efficient development approaches, and usability for prototyping as well as production. With its huge amount of available packages, libraries, and tools, Python seems to have answers to most questions raised by recent developments in the financial industry in terms of analytics, data volumes and frequency, compliance and regulation, as well as technology itself.
It has the potential to provide a single, powerful, consistent framework with which to streamline end-to-end development and production efforts even across larger financial institutions.
In addition, Python has become the programming language of choice for artificial intelligence in general and machine and deep learning in particular. Python is therefore the right language for data-driven finance as well as for AI-first finance, two recent trends that are about to reshape finance and the financial industry in fundamental ways.
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Most helpful customer reviews on Amazon.com
Dr. Hilpisch's book is an end to end explanation and demonstration of the complete process of setting up and using Python for financial data science.
He begins with selection of software and installation on either a local computer or on cloud facilities. He has chosen a set of software packages that are fully compatible with each other, easily installed, open source and free, well documented, and well supported.
The next few chapters review the structure and use of Python. The examples are well chosen and clearly explained. Real financial data is used when possible. He addresses the criticism that Python is slow by showing that alternative methods -- sometimes as simple as rewriting a single line of code -- can result in significantly improved execution speed.
Analysts spend large portions of their time and effort on data preparation. Beginning with real financial data, well chosen examples show how to inspect, clean, transform, and display data series.
Analysis of risk and opportunity requires understanding of the distributions involved. Dr. Hilpisch devotes several chapters to Monte Carlo analysis. Illustrative examples include pricing of derivatives.
The sections of the book that discuss algorithmic trading use the FXCM platform. FXCM focuses on currency pairs, along with a few global indexes and a few commodities. The raw historical data is ticks -- each a bid/ask pair. The FXCM API provides tools to form OHLC bars of whatever length is desired. The text provides examples using the raw ticks as well as the consolidated bars in trading systems. The API also allows order placement and management. A free demo account allows access to downloading data (1 minute bars and longer) and testing trading.
Several trading systems are illustrated. These range from very simple moving average crossover to machine learning.
Profitable trading systems have, at their core, trade secrets. As it does not contain secrets, this book will be of little value to readers hoping to read one book and be rich by Wednesday. You will need to supply your own secret techniques for selection of auxiliary variables, data transformations, and target definition. With those in hand, this book will clarify your path and speed your development. It is exceptionally well done and highly recommended.