Python金融大数据分析

by Yves Hilpisch, 伊夫·希尔皮斯科

Python凭借其简单、易读、可扩展性以及拥有巨大而活跃的科学计算社区，在需要分析、处理大量数据的金融行业得到了广泛而迅速的应用，并且成为该行业开发核心应用的编程语言。《Python金融大数据分析》提供了使用Python进行数据分析，以及开发相关应用程序的技巧和工具。
《Python金融大数据分析》总计分为3部分，共19章，第1部分介绍了Python在金融学中的应用，其内容涵盖了Python用于金融行业的原因、Python的基础架构和工具，以及Python在计量金融学中的一些具体入门实例；第2部分介绍了金融分析和应用程序开发中重要的Python库、技术和方法，其内容涵盖了Python的数据类型和结构、用matplotlib进行数据可视化、金融时间序列数据处理、高性能输入/输出操作、高性能的Python技术和库、金融学中需要的多种数学工具、随机数生成和随机过程模拟、Python统计学应用、Python和Excel的集成、Python面向对象编程和GUI的开发、Python与Web技术的集成，以及基于Web应用和Web服务的开发；第3部分关注的是蒙特卡洛模拟期权与衍生品定价实际应用的开发工作，其内容涵盖了估值框架的介绍、金融模型的模拟、衍生品的估值、投资组合的估值、波动率期权等知识。
《Python金融大数据分析》适合对使用Python进行大数据分析、处理感兴趣的金融行业开发人员阅读。

Genres: Finance, Programming, Computer Science, Technology, Coding, Nonfiction, Reference

511 pages, Kindle Edition

First published October 1, 2012

Reviews

[Łukasz Słonina · Rating 3 out of 5]

Very good book on applying Python to finance. I was hoping to learn both (or at least understand more finance aspect), but the assumption is that you should know finance, and this book is focusing on applying Python, not on learning finance.

[Dennis Cahillane · Rating 5 out of 5]

The perfect book for someone who knows math and software engineering, but not the Python Scientific Stack and basic vectorization techniques to optimize backtesting trading algorithms. I recommend typing in the sample code any time you're not 100% following along, even though that is much slower than simply plowing ahead. Also, buy the Kindle version of the book to view the plots in color, I can't distinguish between the line colors in the printed version of the book.

[Jacob Lindberg · Rating 4 out of 5]

A good read for any finance Quant. Code examples are great. I like his code style. He builds up the DX package gradually which is pedagogical.

[Alex · Rating 4 out of 5]

This makes for a very good "grand tour" of a lot of the practical financial uses of a handful of important Python libraries, and illustrates well how to combine the functionality they offer into a fairly coherent financial application library for derivatives valuation. If one doesn't already have at least a bit of background in both Python and some aspects of financial valuation, there may be a little less value for the reader here -- most chapters touch on topics that are worthy of books in their own right, so some preexisting knowledge (or willingness to go find and read those other books to answer questions) is assumed. Generally, though, a useful resource, and while some of the underlying libraries have evolved since this was published, the presentation is general enough that a motivated reader could figure out how to make the appropriate updates for similar applications of their own.

[Benji · Rating 3 out of 5]

Shows how straightforward the application of ML algorithms to financial data is, at least in a technical sense.
Practically, a number of important topics need to be considered, see e.g. Advances in Financial Machine Learning (2018) by Marcos López de Prado.

[Genert · Rating 5 out of 5]

Gives overview on how to achieve daily financial tasks with Python.

[Amine · Rating 3 out of 5]

This is a broad introduction to Python, really useful if you are new to Python and even programming in general. There are good chapters on Black and Scholes derivative pricing and on portfolio optimization, but I wish there were fewer fillers (such as using Excel in Python) and the author had more self-confidence to write mostly on financial applications. It is true that access to Microsoft Excel files is paramount when dealing with financial data. Yet, this is also something that can be learnt quite quickly online. Readers should know that his other book, on derivative pricing, is a significantly more insightful book, with extremely detailed examples -- a treat, really. So depending on your initial programming and maths level, you may want to start with this one, or with Hilpisch's other more advanced books.

[Matthieu Brucher · Rating 1 out of 5]

My full review is on my blog: http://blog.audio-tk.com/2018/01/30/b...

[Jacob Lindberg · Rating 4 out of 5]

Good code. He introduces new functionality gradually in the dx package Read on kindle so math formulas didn't work but I know them from before.

[Ferhat Culfaz · Rating 4 out of 5]

Good practical book, especially for mix of algo trading, portfolio allocation ,option strategies.

[Zhijing Jin · Rating 4 out of 5]

Helpful when I was doing an undergraduate research project on financial data.

[JDK1962 · Rating 3 out of 5]

My June "work book" in my work-book-a-month effort, that obviously took me far longer to get through than I thought it would.

Useful intro to Python to someone in the financial field, especially with regard to identifying the 3rd party libraries of interest. Some of the specific financial topics were less interesting to me (e.g., option pricing) than others, and some of the examples--early on especially--would have benefited from immediate use of concrete examples, rather than using random number sets, since the motivation was sometimes unclear. But I was grateful for the amount of code...I always learn better from examples.

Not quite sure where the reference to "big [financial] data" was coming from in the subtitle. The term "big data" typically refers to using a data set that can't fit in memory. I don't think any of the examples actually addressed big data (or the topics that come along with it, such as distributed servers or databases, multiple cores, or explicit parallelization of code).

[Julius · Rating 2 out of 5]

Bought this out of curiosity, to maybe try and do any automated financial analysis for my investments. The book is trying to do too much. It's 300+ (more than half!) pages introduction to Python, introduction to numpy & pandas, and introduction into high Python performance. Only then we go into mathematical methods, 2 pages per method, nothing substantial, but a formula, a graph and a piece of code with a paragraph of description of what it is. Then the book becomes intro to statistics.

Around page 500 it goes into algorithmic trading, but the problem is that the examples use some proprietary API that you need to pay to get access to. (And I did my research - there are more accessible options out there!)

Only from pages 550 to 650 it has some interesting stuff, but there's another problem. There's just code with a few explanations of what specific lines do. Wouldn't be a big deal, but as a software engineer I can tell you the code is awful - difficult to read and understand.

I'll probably use this book to lift a monitor. Two stars.

[Joe Michalak · Rating 3 out of 5]

The book excels at conceptually translating theoretical finance calculations (specifically in the subdomain of trading) and applying them to Python. The author correctly makes the claim that the book will not teach you finance (but the material is nothing more difficult than a MBA-level investments class - or a particularly quantitative upperclassman undergrad class).

I did knock a star away for some of the python choices. I suppose I understand that using Python for data science leaves a bit of room to be unpythonic that wouldn't be present in standard development, but some choices were particularly suspect, especially having the "market_environment" attributes be a dictionary instead of a proper attribute of the class. Could have been accomplished without any loss of generality and with the gain of better data checking at implementation, so it's a bit puzzling why it wasn't.

Overall, still a pretty good read. But note that as of 2024, the fxcmpy package was no longer on PyPI.

[Lizi Zhu · Rating 1 out of 5]

The book is awfully written. I have not read a worse written book on a language. Imagine you write something in English and let google translate it into Hawaiian, and then translate that back into English, and then...back and forth for 100 times. This book has to be written in a way aforementioned. It doesn't feel like it's written by a human. I trust a program can write a better book. Ironically, it's a book about language written a computer scientist. Awful. My suggestion is to ignore the explanations and simply type the code provided in the book. So work through the book by literally typing the code. What you see is what you believe. You will see what he fails to convey in his own words.

[Robert · Rating 4 out of 5]

I love the quant software that comes with this. Its great that they share all of the data and scripts for you to play along while reading through this well written and informative introduction to pythons never ending usefulness in finance.

[Victor · Rating 4 out of 5]

Interesting introduction to Pandas library for finance related programs