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The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.
Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:
- Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
- Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
- Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies
- Sales Rank: #35382 in Books
- Published on: 2014-12-27
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 7.00" w x 1.50" l, .0 pounds
- Binding: Paperback
- 606 pages
About the Author
Yves Hilpisch is the founder and managing partner of The Python Quants, an analytics software provider and financial engineering group. The Python Quants offer, among others, the Python Quant Platform (http://quant-platform.com) and DX Analytics (http://dx-analytics.com). Yves also lectures on mathematical finance and organizes meetups and conferences about Python for Quantitative Finance in New York and London.
Most helpful customer reviews
20 of 21 people found the following review helpful.
An excellent summary of the state-of-the art of Python for Finance
By Thomas Wiecki
This book could not be more timely. While Python has been the lingua franca for many data science centric disciplines, quantitative finance has only recently embraced the many benefits of this language and it's rich ecosystem of high quality scientific packages. The book manages to cater to all audiences and is suited for people with a background in finance who want to pick up Python, to Pythonistas who want to get started in Finance, as well as beginners in both disciplines.
What I particularly like about this book is its strong focus on applicability. The many code examples of analyzing real world data sets equips readers with the hands-on knowledge required to start analyzing financial data themselves.
Special note should also be made of how state-of-the art the concepts are the book illustrates. Not an easy feat for any book about a topic that moves at the speed of Python.
In sum, I highly recommend this book to anyone who wants to get started with, or sharpen her skills in Python for Finance.
8 of 10 people found the following review helpful.
This is an excellent book that works both as teaching medium as well ...
By ANTON.IO
This is an excellent book that works both as teaching medium as well as a reference. It targets finance people with some kind of programming experience (Matlab, R, VBA, etc) in a style that is easily accessible. The topics are well rounded and include chapters: on Data Structures; Performance Computing; on Excel integration, on Web, etc.
The book also shows that Python is well suited for typically demanding analytics tasks (simulation, HPC, hardware-bound IO). This is illustrated with real world use cases such as the DX Analytics case study which illustrates how efficient and powerful (the standard) Python (stack) can be.
In addition, this book shows how the latest "trends and fads" are being "followed" using Python (PCA, Bayesian MC, Web-based apps).
This book really helps the reader take the best of Python in (Quant) Finance -- no matter what kind of institution she/he/ai works for or what kind of academic research she/he/ai is involved in.
7 of 9 people found the following review helpful.
A mish-mash of text and code
By RSG
The problem with this book is that it's not really a book in a normal sense. It's text inserted between sequential lines of Python code: three or four lines of code followed by one or two lines of text, ad infinitum. You read it but you don't feel like you've learned anything at all other than... I am not sure what.
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