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Product details
File Size: 46630 KB
Print Length: 720 pages
Simultaneous Device Usage: Unlimited
Publisher: O'Reilly Media; 2 edition (December 5, 2018)
Publication Date: December 5, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07L8NMW2P
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Amazon Best Sellers Rank:
#66,393 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
"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.
Great introduction to finance with python.
Subject matter coverage greatly expanded and in depth.
I am in the physical sciences, where Python is heavily used for data analysis, and as a side-effect it gives some of our students a second career path as quants. I think this book is a good introduction to setting up Python for use with lots of different types of data sets, including social science data sets. The coverage of Monte Carlo techniques is especially good, and one that is close to me both on the physics side of work as well as on doing the financial risk management for large science projects (based on expected probabilities and inheritances, what is the likely amount of contingency needed at the 75 and 90% levels for example). More than once I've pondered whether techniques shown here in the book could be at least partially applied to science data. And the 1st edition copy of the book I had has wondered off, likely with someone looking at careers outside of academic science...
This is a good reference for Python in financial applications that goes beyond simple tips and tricks code examples. The author provides some decent explanation of the language and structure, as well as the benefits of using Python. There is some good effort in helping you really understand Python at different scales, which I appreciate.While Python is used in a variety of fields, including financial, information systems, and data management, this book is clearly geared for financial applications. With that said, it can still serve as a general Python reference if you also needed a reference for additional reference purposes.
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.
Python is a programming language suited to data analytics, and this book is an excellent introduction to its application to finance. The author uses clear writing to illustrate concepts and many good examples, including lengthy segments of code.The book covers installation, data preparation and analysis, optimization, derivatives, trading strategies, and even topics that aren't covered in many books, like object-oriented programming and automated trading.
As a graduate student in business analytics, my education has really been a sampling of everything, with no real specialization in anything. Essentially - we are learning the foundations of the tools we need to use, and when to use them. My working background is in healthcare, and my undergrad is in accounting/finance. What am I going to do when I finish school? I have no idea - doctorate or corporate sector gig. Either way, this is an excellent reference and educational book. We've done a fair bit with R and Python in my classes, and I've found that actually applying things is truly the best way to learn something. And this book (along with all the O'Reilly books I've picked up) excels at that. If you don't know Python at all, or you're an expert, this isn't the book for you - but if you are more of a beginner, you'll be happy.
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