Paperback: 408 pages
Publisher: Packt Publishing - ebooks Account (May 11, 2014)
Product Dimensions: 7.5 x 0.9 x 9.2 inches
Shipping Weight: 1.9 pounds (View shipping rates and policies)
Average Customer Review: 3.6 out of 5 stars See all reviews (19 customer reviews)
Best Sellers Rank: #945,099 in Books (See Top 100 in Books) #139 in Books > Computers & Technology > Software > Personal Finance #165 in Books > Computers & Technology > Software > Accounting #621 in Books > Computers & Technology > Software > Mathematical & Statistical
Really not very good. I was intrigued by the table of contents and the topics covered, but the depth of any one of those topics is so shallow that I imagine the author put this book together in a weekend using google and wikipedia.I skimmed through it and it appears each topic gets about 1 page of coverage with a little bit of code and not much of an explanation on how it works. Additionally, there are no real algorithms here, and instead you get shown how the author would use some open source library (without any notes about the nuances of those libraries.)If you've ever done anything in finance with python, this book is a waste of paper for you. If you are new to python perhaps there is some utility in seeing how python could be utilized. But the superficial coverage of the topics means you really aren't going to be able to just take the code and do anything meaningful yourself.Save your money and just use stackexchange/google. I got the kindle edition and returned the book within 5 minutes.
Completely lacking in structure. This is not a book so much as a set of post-it notes assembled in completely random order with sections repeated often and practically verbatim. How many times throughout the book can you repeat the process for downloading stock data from Yahoo Finance (for example)? I was really anticipating receiving this book since there is nothing really out there that's specifically targeted at Python for finance people (that I know of). The closest is "Python for Data Analysis" which is a very good book. I'm so disappointed. Author, please look at that book as an idea as to how to structure and organize a subject and then redo this.The reason I did not give this one star is that there are some good pieces of code in here to be found if you laboriously leaf through the book. I'm "Fighting Tribeca Irish"'s significant other (shared account).
This book might not serve well anyone seriously trying to get into the finance world by programming applications in Python. On a more optimistic note, the book might be better directed at an undergraduate who has read about topics like Black Scholes pricing of options. And who wants to quickly code some simple test programs. The back of the book says it is useful for practitioners. Nonsense. Practitioners in finance already have scads of far more complex programs for modelling.Each chapter of the book basically is a simple rendition of code for the chapter topic. Somewhat superficial. As another reviewer perhaps accurately observed, it is as though the author just went through Wikipedia and dragged out several finance topics and the starting equations. And then coded those into chapters.One good aspect is that the text describes how to use two standard Python modules, NumPy and SciPy. For numerical scientific and financial contexts. If you do end up using this book, you should absolutely learn how to use the routines in those modules. First, the modules have been extensively debugged and are being freely maintained. Second, you add more value for yourself by starting there rather than wasting your time recoding their routines. Though granted, an exception could be where you explicitly want to recode some of their routines as a learning experience for yourself.
First of all this is not an in-depth python book or an in-depth finance book. So if you are looking for something with a strong technical bent on python or finance this is not it.It is, however, a great read if you are familiar with doing a lot of finance in another language like R and want to transition to python. With bloomberg providing a python API and C++ still being a real pain the rear this is a good way for more "analyst" types to become much more fluent and competent using a vastly more flexible language. It is not mega detailed - basically a "crash through" approach to doing a bit of python and doing it quickly. This is by no means a standalone solution to anything.The best use of this book is in conjunction with something more rigorous for finance and python. Aside from that it could be put to good use in an undergrad finance class so that instead of messing around in excel people actually learn a bit of code that they can build on later.
I jumped on the opportunity to receive a review copy of Python for Finance. I've been programming in Python for the last 2 years, and I'm particularly interested in technical computing.Python for Finance is targeted for graduate students in finance, although I believe it is also appropriate for senior-level computer science majors who are interested using Python for financial computing.Let me start with a few minor quibbles. First, I've never cared for how Pakt formats their books; it's clunky and seems dated. Second, there are a number of places in the book where the English is awkward and could have benefited from some decent editing.Because Python for Finance is fairly short (about 350 pages of content), don't expect it to be the only book you'll need if you want to really learn Python. It will, though, get you started and up to speed with the language. About a third of the way through, the author introduces the NumPy and SciPy modules, which are essential for technical computing. Because of the scope of the book, these are given limited coverage, but enough to provide readers with an understanding of how powerful these tools are.Python for Finance then moves into plotting, analysis of time series, and then jumps into option modeling, which is where the book really starts to tie things together, including various tools to pull financial information from the web. A chapter on the ins and outs of Python loops precedes what was my favorite chapter - Monte Carlo Simulations and Options. This chapter really highlights the power of Python for technical computing.Overall, I enjoyed Python for Finance. I learned new tools and techniques in Python as well as a significant amount about financial computing.
Python: PYTHON CRASH COURSE - Beginner's Course To Learn The Basics Of Python Programming In 24 Hours!: (Python, Python Programming, Python for Dummies, Python for Beginners, python crash course) Python: Learn Python In A DAY! - The Ultimate Crash Course to Learning the Basics of Python In No Time (Python, Python Course, Python Development, Python Books, Python for Beginners) PYTHON: Python in 8 Hours, For Beginners, Learn Python Fast! A Smart Way to Learn Python, Plain & Simple, Learn Python Programming Language in Easy Steps, A Beginner's Guide, Start Coding Today! Python: Learn Web Scraping with Python In A DAY! - The Ultimate Crash Course to Learning the Basics of Web Scraping with Python In No Time (Web Scraping ... Python Books, Python for Beginners) Python: Learn Python FAST - The Ultimate Crash Course to Learning the Basics of the Python Programming Language In No Time (Python, Python Programming, ... (Learn Coding Fast with Hands-On Project 7) Programming #45: Python Programming Professional Made Easy & Android Programming In a Day! (Python Programming, Python Language, Python for beginners, ... Programming Languages, Android Programming) Python: Learn Python in One Day and Learn It Well. Python for Beginners with Hands-on Project. (Learn Coding Fast with Hands-On Project Book 1) Programming Raspberry Pi 3: Getting Started With Python (Programming Raspberry Pi 3, Raspberry Pi 3 User Guide, Python Programming, Raspberry Pi 3 with Python Programming) Modeling Structured Finance Cash Flows with Microsoft Excel: A Step-by-Step Guide (Wiley Finance) Mastering Python for Finance Python for Finance Big Data, MapReduce, Hadoop, and Spark with Python: Master Big Data Analytics and Data Wrangling with MapReduce Fundamentals using Hadoop, Spark, and Python Python : The Ultimate Python Quickstart Guide - From Beginner To Expert (Hands On Projects, Machine Learning, Learn Coding Fast, Learning code, Database) Hacking: Hacking Made Easy 1: Beginners: Python: Python Programming For Beginners, Computer Science, Computer Programming Python: Complete Crash Course for Becoming an Expert in Python Programming (2nd Edition) Mobile Apps: Python and HTML: Programming Guide: Learn In A Day (Python, Swift, HTML, Apps) Programming Google App Engine with Python: Build and Run Scalable Python Apps on Google's Infrastructure Python Data Analytics: Data Analysis and Science using pandas, matplotlib and the Python Programming Language Black Hat Python: Python Programming for Hackers and Pentesters Python: Python Made Easy 1: Hacking: Beginners