Book Image

Mastering Python for Finance - Second Edition

By : James Ma Weiming
Book Image

Mastering Python for Finance - Second Edition

By: James Ma Weiming

Overview of this book

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.
Table of Contents (16 chapters)
Free Chapter
1
Section 1: Getting Started with Python
3
Section 2: Financial Concepts
9
Section 3: A Hands-On Approach

To get the most out of this book

Prior experience in Python is required.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Mastering-Python-for-Finance-Second-Edition. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "By default, pandas' .plot() command uses the matplotlib library to display graphs."

A block of code is set as follows:

In [ ]:
%matplotlib inline
import quandl

quandl.ApiConfig.api_key = QUANDL_API_KEY
df = quandl.get('EURONEXT/ABN.4')
daily_changes = df.pct_change(periods=1)
daily_changes.plot();

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

2015-02-26 TICK WIKI/AAPL open: 128.785 close: 130.415
2015-02-26 FILLED BUY 1 WIKI/AAPL at 128.785
2015-02-26 POSITION value:-128.785 upnl:1.630 rpnl:0.000
2015-02-27 TICK WIKI/AAPL open: 130.0 close: 128.46

Any command-line input or output is written as follows:

$ cd my_project_folder
$ virtualenv my_env

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "To start your first notebook, select New, then Python 3."

Warnings or important notes appear like this.
Tips and tricks appear like this.