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Book Information
Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applications
Key Features
Explore advanced financial models used by the industry and ways of solving them using Python
Build state-of-the-art infrastructure for modeling, visualization, trading, and more
Empower your financial applications by applying machine learning and deep learning
Book Description
The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples.
You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance.
By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis.
What you will learn
Solve linear and nonlinear models representing various financial problems
Perform principal component analysis on the DOW index and its components
Analyze, predict, and forecast stationary and non-stationary time series processes
Create an event-driven backtesting tool and measure your strategies
Build a high-frequency algorithmic trading platform with Python
Replicate the CBOT VIX index with SPX options for studying VIX-based strategies
Perform regression-based and classification-based machine learning tasks for prediction
Use TensorFlow and Keras in deep learning neural network architecture
Who this book is for
If you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.
Table of Contents
Overview of Financial Analysis with Python
The Importance of Linearity in Finance
Nonlinearity in Finance
Numerical Methods for Pricing Options
Modeling Interest Rates and Derivates
Statistical Analysis of Time Series Data
Interactive Financial Analytics with VIX
Building an Algorithmic Trading Platform
Implementing a Backtesting System
Machine Learning for Finance
Deep Learning for Finance
Book ID Asin: B07MVGW93W
Book Title: Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition
Book Author: James Ma Weiming
Book Format and Price:
Book Price: unknown
Book Category: Kindle Store, Kindle eBooks, Computers & Technology and unknown
Book Rating: 45 ratings
Book Product Detail
ASIN : B07MVGW93W
Publisher : Packt Publishing; 2nd edition (April 30, 2019)
Publication date : April 30, 2019
Language : English
File size : 18307 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 428 pages
Lending : Not Enabled
Best Sellers Rank: #132,380 in Kindle Store (See Top 100 in Kindle Store)
#3 in Bioinformatics (Kindle Store)
#12 in Bioinformatics (Books)
#49 in Computer Hardware
Customer Reviews:
4.3 out of 5 stars 45 ratings
About the Author
James Ma Weiming is a software engineer based in Singapore. His studies and research are focused on financial technology, machine learning, data sciences, and computational finance. James started his career in financial services working with treasury fixed income and foreign exchange products, and fund distribution. His interests in derivatives led him to Chicago, where he worked with veteran traders of the Chicago Board of Trade to devise high-frequency, low-latency strategies to game the market. He holds an MS degree in finance from Illinois Tech's Stuart School of Business in the United States and a bachelor's degree in computer engineering from Nanyang Technological University.
Book Review
Name: Robin T. Wernick
Rating: 4.0 out of 5 stars
Title: GoodIntroductiontotheVeryLargeWorldofFinanceComputingSecondEdition
Date: Reviewed in the United States on December 9, 2020
Review: Trying to introduce the entire world of Finance Computing is a real stretch or any reference book and this one is short a few critical sections, but I applaud this effort to cover it all. Financial concepts and supporting mathematics are covered decently, but are largely isolated from the full coding examples. This may be because it will save space.Warning: As in the first edition, All code files are of type "ipynb". This will require the use of Jupyter Notebook to display them on screen! So get ready to learn a new IDE to apply this book. See Packt Books for code downloads.API access to Alpha Advantage and Quandl financial data sources are only superficially covered in the book. Critical program coding that explains the API is missing in both cases. I hope that the recommended Jupyter Notebook won't prove to be non intuitive to use to see all the supporting code.Finally, I missed seeing any coding support for access to databases such as MySQL or 'kdb'. I think the author is primarily a concept thinker and leaves the detail work to his assistants. But for us, we need more handholding to make the entire concept framework pay off. So I recommend that you add other references such as "Python GUI Programming Cookbook" and/or "Hands-On Web Scraping with Python" to fill in the missing pieces.
Name: MM
Rating: 1.0 out of 5 stars
Title: Toobasicandfullofconceptualerrors
Date: Reviewed in the United States on August 2, 2021
Review: The author maybe has some knowledge of ML, but seems to not really understand financial time series.From using the entire dataset for min/max scaling to predicting price there isn’t much for some to actually learn anything useful.
Name: A. Thomas
Rating: 1.0 out of 5 stars
Title: Outdateddatasourcespoorsupport
Date: Reviewed in the United States on September 20, 2021
Review: The book relies on data providers that no longer provide data used in the examples. The author deems in appropriate to direct the reader to paid data sources. I regret purchasing this book and would advice potential readers to borrow it from your library first before wasting any money on it - you will soon see how inadequate of a job the author 'teaches' python for finance.
Name: Joseph R.
Rating: 5.0 out of 5 stars
Title: AbsolutelyEssential
Date: Reviewed in the United States on July 19, 2019
Review: This book is a must-read for anyone aspiring or practicing in the field of quantitative finance. James' work is extremely well-written, easy to follow, and detailed.As a financial engineer, I use numerical methods in my daily life; most of these I thought were only possible in lower-level languages. I was incredibly shocked to learn that all of these advanced mathematical methodologies can be written in Python and used for practical applications in algorithmic trading. In addition to walking the reader through these methods step-by-step, James teaches the reader how to build a real, working algorithmic trading strategy (several of them actually) and gives the reader the tools to backtest and implement the strategy for their own use!This is the first book, to my knowledge, that covers these incredibly important steps and does so in a way that programmers of all experience-levels can understand. Absolutely brilliant work!Note: The screenshot is taken from the output of my pairs trading (statistical arbitrage) strategy that I built and traded on InteractiveBrokers - all thanks to James' work!