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Python for Finance: Analyze Big Financial Data
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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
- ISBN-101491945281
- ISBN-13978-1491945285
- Edition1st
- PublisherO'Reilly Media
- Publication dateJanuary 20, 2015
- LanguageEnglish
- Dimensions7 x 1.5 x 9.25 inches
- Print length603 pages
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Product details
- Publisher : O'Reilly Media
- Publication date : January 20, 2015
- Edition : 1st
- Language : English
- Print length : 603 pages
- ISBN-10 : 1491945281
- ISBN-13 : 978-1491945285
- Item Weight : 2.14 pounds
- Dimensions : 7 x 1.5 x 9.25 inches
- Best Sellers Rank: #505,856 in Books (See Top 100 in Books)
- #178 in Data Modeling & Design (Books)
- #313 in Business Finance
- #443 in Python Programming
- Customer Reviews:
About the author

Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading, and computational finance. He is also the founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform.
Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance.
Yves is the author of five books (https://home.tpq.io/books):
* Artificial Intelligence in Finance (O’Reilly, forthcoming)
* Python for Algorithmic Trading (O’Reilly, forthcoming)
* Python for Finance (2018, 2nd ed., O’Reilly)
* Listed Volatility and Variance Derivatives (2017, Wiley Finance)
* Derivatives Analytics with Python (2015, Wiley Finance)
Yves is the director of the first online training program leading to University Certificates in Python for Algorithmic Trading (https://home.tpq.io/certificates/pyalgo) and Computational Finance (https://home.tpq.io/certificates/compfin). He also lectures on computational finance, machine learning, and algorithmic trading at the CQF Program (http://cqf.com).
Yves is the originator of the financial analytics library DX Analytics (http://dx-analytics.com) and organizes Meetup group events, conferences, and bootcamps about Python, artificial intelligence and algorithmic trading in London (http://pqf.tpq.io), New York (http://aifat.tpq.io), Frankfurt, Berlin, and Paris. He has given keynote speeches at technology conferences in the United States, Europe, and Asia.













