Ebook Download Python for Finance: Mastering Data-Driven Finance
And also to suggest you a better publication with great quality, you can pick Python For Finance: Mastering Data-Driven Finance Why we refer this book for you? We understand that you are now looking for the certified book related to this subject. For this reason, you can start it by getting this publication as one of the chosen reading publication. It is not about the book that is written by a really professional author or released by preferred author. This has to do with guide that is much-loved one and also effect for your demands.
Python for Finance: Mastering Data-Driven Finance
Ebook Download Python for Finance: Mastering Data-Driven Finance
After few time, finally guide that we as well as you await is coming. So soothed to get this wonderful book available to offer in this website. This is the book, the DDD. If you still feel so tough to obtain the published publication in guide store, you can join with us once more. If you have ever obtained the book in soft documents from this book, you can quickly get it as the referral now.
If you ally need such a referred Python For Finance: Mastering Data-Driven Finance publication that will provide you value, get the most effective seller from us currently from several preferred publishers. If you wish to amusing books, many novels, story, jokes, and more fictions collections are also released, from best seller to one of the most current released. You may not be puzzled to appreciate all book collections Python For Finance: Mastering Data-Driven Finance that we will offer. It is not about the costs. It has to do with what you require now. This Python For Finance: Mastering Data-Driven Finance, as one of the most effective sellers below will certainly be among the best options to read.
Maturing from primary to the grown-up, reading publications will certainly allow various needs to think. Sometime, we require guide as a result of the work due date. However in other time, you could check out once again this Python For Finance: Mastering Data-Driven Finance, for not just the task deadline need yet likewise for excited. So, is reading this book your fantastic anxious to read. When you have sufficient to seek for one more publication that cannot make you feel pleased, you will constantly try to find various other resources, will not you? This is why we concern you to assist in locating the right publication.
So, it will not compel your time to always spend the moment for this kind of guide. Just few times in a day, as well as you can get what the other visitors expect. In this instance, Python For Finance: Mastering Data-Driven Finance is given in soft documents system. You can download as well as obtain guide from the link attaching that is provided. It will certainly not be complicated. You will go quickly to discover the book and begin to check out.
About the Author
Dr. Yves J. Hilpisch is founder and managing partner of The Python Quants (http://tpq.io), a group that focuses on the use of open source technologies for financial data science, algorithmic trading and computational finance. He is the author of the books Python for Finance (O'Reilly, 2014), Derivatives Analytics with Python (Wiley, 2015) and Listed Volatility and Variance Derivatives (Wiley, 2017). Yves lectures on computational finance at the CQF Program (http://cqf.com), on data science at htw saar University of Applied Sciences (http://htwsaar.de), and is the director for the online training program leading to the first Python for Finance University Certificate (awarded by htw saar).
Read more
Product details
Paperback: 720 pages
Publisher: O'Reilly Media; 2 edition (December 31, 2018)
Language: English
ISBN-10: 1492024333
ISBN-13: 978-1492024330
Product Dimensions:
7 x 1.5 x 9.2 inches
Shipping Weight: 2.5 pounds (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
4 customer reviews
Amazon Best Sellers Rank:
#43,360 in Books (See Top 100 in Books)
"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.
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.
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.
Python for Finance: Mastering Data-Driven Finance PDF
Python for Finance: Mastering Data-Driven Finance EPub
Python for Finance: Mastering Data-Driven Finance Doc
Python for Finance: Mastering Data-Driven Finance iBooks
Python for Finance: Mastering Data-Driven Finance rtf
Python for Finance: Mastering Data-Driven Finance Mobipocket
Python for Finance: Mastering Data-Driven Finance Kindle
Tidak ada komentar:
Posting Komentar