Python for Finance: Mastering Data-Driven Finance 2nd Edition

"Python is the programming language and technology platform of choice, not only for this book but for almost every leading financial institution. However, Python deployment can be tricky at best and sometimes even tedious and nerve-wracking. Fortunately, several technologies that can help with the deployment issue have become available in recent years." Python for Finance, page 56.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.

With the fast pace of AI development, a few things in the book are out of date, but relatively easy to work around. The example code is well organized, and the author explains the structure from start to finish making it easy to implement topics covered and expand into other applications.

The book was well written. if you are a beginner for python, this book is right for you. Those heavy finance theories arent that difficult, you just have to watch a little youtube about efficient frontier and you'll get it. This book was easy to read through the chapters, unlike some other O'Reily books where I was stuck on Chapter 2 or 3 for a month month.

This book is a slightly odd hybrid in terms of audience, since it's not apparently targeted at either finance specialists or experienced Python programmers, but rather at a semi-introductory level for both, but for that job it seems well-designed. Working through it should provide a useful introduction to… well, enough Python to do basic data-analysis tasks fluently if not really enough to be a general-purpose Python programmer (a lot of functionality and a lot of basic computer science is omitted entirely), along with basic market concepts and how to apply them in code form. It's not quite a "cookbook" with "recipes" but the code snippets in each chapter/example are generally easily reusable and repurposable. I would probably recommend skipping it if you're already fluent in Python, but as a single volume to become a basically competent Python market-data person, this fits the bill nicely.

Really good book. Great for beginners in programming. I’d say you probably need a background in finance to understand it more fully but nevertheless it’s a great product. Very applicable in today’s world.

Great book book for learning how to manipulate data with python, specially with the pandas module. The book is well explained with lots of examples.