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Mastering Python for Finance. - Second Edition
Mastering Python for Finance. - Second Edition

Mastering Python for Finance.: Implement advanced state-of-the-art financial statistical applications using Python, Second Edition

By James Ma Weiming
€25.99 €17.99
Book Apr 2019 426 pages 2nd Edition
eBook
€25.99 €17.99
Print
€32.99
Subscription
€14.99 Monthly
eBook
€25.99 €17.99
Print
€32.99
Subscription
€14.99 Monthly

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Mastering Python for Finance. - Second Edition

Section 1: Getting Started with Python

This section will help us to set up Python on our machine in preparation for running code examples in this book.

This section will contain only one chapter:

  • Chapter 1, Overview of Financial Analysis with Python
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Key benefits

  • 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

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

Product Details

Country selected

Publication date : Apr 30, 2019
Length 426 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781789346466
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details


Publication date : Apr 30, 2019
Length 426 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781789346466
Category :

Table of Contents

16 Chapters
Preface Chevron down icon Chevron up icon
1. Section 1: Getting Started with Python Chevron down icon Chevron up icon
2. Overview of Financial Analysis with Python Chevron down icon Chevron up icon
3. Section 2: Financial Concepts Chevron down icon Chevron up icon
4. The Importance of Linearity in Finance Chevron down icon Chevron up icon
5. Nonlinearity in Finance Chevron down icon Chevron up icon
6. Numerical Methods for Pricing Options Chevron down icon Chevron up icon
7. Modeling Interest Rates and Derivatives Chevron down icon Chevron up icon
8. Statistical Analysis of Time Series Data Chevron down icon Chevron up icon
9. Section 3: A Hands-On Approach Chevron down icon Chevron up icon
10. Interactive Financial Analytics with the VIX Chevron down icon Chevron up icon
11. Building an Algorithmic Trading Platform Chevron down icon Chevron up icon
12. Implementing a Backtesting System Chevron down icon Chevron up icon
13. Machine Learning for Finance Chevron down icon Chevron up icon
14. Deep Learning for Finance Chevron down icon Chevron up icon
15. Other Books You May Enjoy Chevron down icon Chevron up icon

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