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Book Cover
E-book
Author Weiming, James Ma, author

Title Mastering Python for finance : implement advanced state-of-the-art financial statistical applications using Python / James Ma Weiming
Edition Second edition
Published Birmingham, UK : Packt Publishing, 2019

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Description 1 online resource (1 volume) : illustrations
Summary 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 scikit-learn. 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 ..
Notes Previous edition published: 2015
Bibliography Includes bibliographical references
Notes Online resource; title from title page (Safari, viewed July 30, 2019)
Subject Python (Computer program language)
Application software -- Development.
Computers -- Finance
Finance -- Mathematical models.
Application software -- Development
Computers -- Finance
Finance -- Mathematical models
Python (Computer program language)
Form Electronic book
ISBN 1789345278
9781789345278