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Mastering Python for Finance
Mastering Python for Finance

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

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Mastering Python for Finance

Overview of Financial Analysis with Python

Since the publication of my previous book Mastering Python for Finance, there have been significant upgrades to Python itself and many third-party libraries. Many tools and features have been deprecated in favor of new ones. This chapter walks you through how to get the latest tools available and how to prepare the environment that will be used throughout the rest of the book.

We will be using Quandl for the majority of datasets covered in this book. Quandl is a platform that serves financial, economic, and alternative data. These sources of data are contributed by various data publishers, including the United Nations, World Bank, central banks, trading exchanges, investment research firms, and even members of the Quandl community. With the Python Quandl module, you can easily download datasets and perform financial analytics to derive...

Getting Python

At the time of writing, the latest Python version is 3.7.0. You may download the latest version for Windows, macOS X, Linux/UNIX, and other operating systems from the official Python website at https://www.python.org/downloads/. Follow the installation instructions to install the base Python interpreter on your operating system.

The installation process should add Python to your environment path. To check the version of your installed Python, type the following command into the terminal if you are using macOS X/Linux, or the command prompt on Windows:

$ python --version
Python 3.7.0
For easy installation of Python libraries, consider using an all-in-one Python distribution such as Anaconda (https://www.anaconda.com/download/), Miniconda (https://conda.io/miniconda.html), or Enthought Canopy (https://www.enthought.com/product/enthought-python-distribution/). Advanced...

Introduction to Quandl

Quandl is a platform that serves financial, economic, and alternative data. These sources of data are contributed by various data publishers, including the United Nations, World Bank, central banks, trading exchanges, and investment research firms.

With the Python Quandl module, you can easily get financial datasets into Python. Quandl offers free datasets, some of which are samples. Paid access is required for access to premium data products.

Setting up Quandl for your environment

The Quandl package requires the latest versions of NumPy and pandas. Additionally, we will require matplotlib for the rest of this chapter.

To install these packages, type the following code in your terminal window:

...

Plotting a time series chart

A simple and effective technique for analyzing time series data is by visualizing it on a graph, from which we can infer certain assumptions. This section will guide you through the process of downloading a dataset of stock prices from Quandl and plotting it on a price and volume graph. We will also cover plotting candlestick charts, which will give us more information than line charts.

Retrieving datasets from Quandl

Fetching data from Quandl into Python is fairly straightforward. Suppose we are interested in ABN Amro Group from the Euronext Stock Exchange. The ticker symbol in Quandl is EURONEXT/ABN. In a Jupyter notebook cell, run the following command:

In [ ]:
import quandl

# Replace...

Performing financial analytics on time series data

In this section, we will visualize some statistical properties of time series data used in financial analytics.

Plotting returns

One of the classic measures of security performance is its returns over a prior period. A simple method for calculating returns in pandas is pct_change, where the percentage change from the previous row is computed for every row in the DataFrame.

In the following example, we use ABN stock data to plot a simple graph of daily percentage returns:

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...

Summary

In this chapter, we set up our working environment with Python 3.7 and used the virtual environment package to manage separate package installations. The pip command is a handy Python package manager that easily downloads and installs Python modules, including Jupyter, Quandl, and pandas. Jupyter is a browser-based interactive computational environment for executing Python code and visualizing data. With a Quandl account, we can easily obtain high-quality time series datasets. These sources of data are contributed by various data publishers. Datasets directly download into a pandas DataFrame object that allows us to perform financial analytics, such as plotting daily percentage returns, histograms, Q-Q plots, correlations, simple moving averages, and exponential moving averages.

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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.

Who is this book 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.

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

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

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Aparatologia ANAD SA de CV Nov 13, 2023
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
As you need a subscription to a 2000+ USD do download data and technical indicators..... it is useless.
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Alicia M. Sep 05, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Llegó en buen estado y era lo que esperaba.
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Amazon Customer Mar 27, 2022
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Book was in perfect shape. Quick delivery.
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A. Thomas Sep 20, 2021
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
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.
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MM Aug 02, 2021
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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.
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