Mastering Python for Finance - Second Edition: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition

Mastering Python for Finance

Second Edition

Implement advanced state-of-the-art financial statistical applications using Python

James Ma Weiming

BIRMINGHAM - MUMBAI

Mastering Python for Finance Second Edition

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First published: April 2015

Second edition: April 2019

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To the team at Skyline Trading on the CBOT trading floor - Mr. Milt Robinson, Brian Hickman, and Frank.

To my family, friends, and colleagues.

And, of course, to Paik Yen.

Contributors

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.

About the reviewers

Anil Omanwar has over 11 years' experience in researching cognitive computing, while natural language processing (NLP), machine learning, information visualization, and text analytics are a few key areas of his research interests. He is proficient in sentiment analysis, questionnaire-based feedback, text clustering, and phrase extraction in diverse domains such as banking, oil and gas, life sciences, manufacturing, retail, social media, and others. He is currently associated with IBM Australia for NLP and IBM Watson in the oil and gas domain. The objective of his research is to automate critical manual decisions and assist domain experts to optimize human-machine capabilities. He holds multiple patents on emerging technologies, including NLP automation and device intelligence.

Rahul Shendge has a bachelor's degree in computer engineering from University of Pune and is certified in multiple technologies. He is an open source enthusiast and works as a senior software engineer. He has worked in multiple domains, including finance, healthcare, and education. He has hands-on experience of cloud, designing, and trading algorithms with machine learning. He is constantly exploring technical novelties and he is open-minded and eager to learn about new technologies. He is passionate about helping clients to make valuable business decisions using analytics in respective areas. His interests include working with and exploring data analytics solutions.

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Table of Contents

Title Page

Copyright and Credits

Mastering Python for Finance Second Edition

Dedication

Contributors

About the author

About the reviewers

Packt is searching for authors like you

About Packt

Why subscribe?

Packt.com

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Section 1: Getting Started with Python

Overview of Financial Analysis with Python

Getting Python

Preparing a virtual environment

Running Jupyter Notebook

The Python Enhancement Proposal

What is a PEP?

The Zen of Python

Introduction to Quandl

Setting up Quandl for your environment

Plotting a time series chart

Retrieving datasets from Quandl

Plotting a price and volume chart

Plotting a candlestick chart

Performing financial analytics on time series data

Plotting returns

Plotting cumulative returns

Plotting a histogram

Plotting volatility

A quantile-quantile plot

Downloading multiple time series data

Displaying the correlation matrix

Plotting correlations

Simple moving averages

Exponential moving averages

Summary

Section 2: Financial Concepts

The Importance of Linearity in Finance

The Capital Asset Pricing Model and the security market line

The Arbitrage Pricing Theory model

Multivariate linear regression of factor models

Linear optimization

Getting Pulp

A maximization example with linear programming

Outcomes of linear programs

Integer programming

A minimization example with integer programming

Integer programming with binary conditions

Solving linear equations using matrices

The LU decomposition

The Cholesky decomposition

The QR decomposition

Solving with other matrix algebra methods

The Jacobi method

The Gauss-Seidel method

Summary

Nonlinearity in Finance

Nonlinearity modeling

Examples of nonlinear models

The implied volatility model

The Markov regime-switching model

The threshold autoregressive model

Smooth transition models

Root-finding algorithms

Incremental search

The bisection method

Newton's method

The secant method

Combing root-finding methods

SciPy implementations in root-finding

Root-finding scalar functions

General nonlinear solvers

Summary

Numerical Methods for Pricing Options

Introduction to options

Binomial trees in option pricing

Pricing European options

Writing the StockOption base class

A class for European options using a binomial tree

A class for American options using a binomial tree

The Cox–Ross–Rubinstein model

A class for the CRR binomial tree option pricing model

Using a Leisen-Reimer tree

A class for the LR binomial tree option pricing model

The Greeks for free

A class for Greeks with the LR binomial tree

Trinomial trees in option pricing

A class for the trinomial tree option pricing model

Lattices in option pricing

Using a binomial lattice

A class for the CRR binomial lattice option pricing model

Using the trinomial lattice

A class for the trinomial lattice option pricing model

Finite differences in option pricing

The explicit method

Writing the finite difference base class

A class for pricing European options using the explicit method of finite differences

The implicit method

A class for pricing European options using the implicit method of finite differences

The Crank-Nicolson method

A class for pricing European options using the Crank-Nicolson method of finite differences

Pricing exotic barrier options

A down-and-out option

A class for pricing down-and-out-options using the Crank-Nicolson method of finite differences

Pricing American options with finite differences

A class for pricing American options using the Crank-Nicolson method of finite differences

Putting it all together – implied volatility modeling

Implied volatilities of the AAPL American put option

Summary

Modeling Interest Rates and Derivatives

Fixed-income securities

Yield curves

Valuing a zero-coupon bond

Spot and zero rates

Bootstrapping a yield curve

An example of bootstrapping the yield curve

Writing the yield curve bootstrapping class

Forward rates

Calculating the yield to maturity

Calculating the price of a bond

Bond duration

Bond convexity

Short–rate modeling

The Vasicek model

The Cox-Ingersoll-Ross model

The Rendleman and Bartter model

The Brennan and Schwartz model

Bond options

Callable bonds

Puttable bonds

Convertible bonds

Preferred stocks

Pricing a callable bond option

Pricing a zero-coupon bond by the Vasicek model

The value of early exercise

Policy iteration by finite differences

Other considerations in callable bond pricing

Summary

Statistical Analysis of Time Series Data

The Dow Jones industrial average and its 30 components

Downloading Dow component datasets from Quandl

About Alpha Vantage

Obtaining an Alpha Vantage API key

Installing the Alpha Vantage Python wrapper

Downloading the DJIA dataset from Alpha Vantage

Applying a kernel PCA

Finding eigenvectors and eigenvalues

Reconstructing the Dow index with PCA

Stationary and non-stationary time series

Stationarity and non-stationarity

Checking for stationarity

Types of non-stationary processes

Types of stationary processes

The Augmented Dickey-Fuller Test

Analyzing a time series with trends

Making a time series stationary

Detrending

Removing trend by differencing

Seasonal decomposing

Drawbacks of ADF testing

Forecasting and predicting a time series

About the Autoregressive Integrated Moving Average 

Finding model parameters by grid search

Fitting the SARIMAX model

Predicting and forecasting the SARIMAX model

Summary

Section 3: A Hands-On Approach

Interactive Financial Analytics with the VIX

Volatility derivatives

STOXX and the Eurex

The EURO STOXX 50 Index

The VSTOXX

The S&P 500 Index

SPX options

The VIX

Financial analytics of the S&P 500 and the VIX

Gathering the data

Performing analytics

The correlation between the SPX and the VIX

Calculating the VIX Index

Importing SPX options data

Parsing SPX options data

Finding near-term and next-term options

Calculating the required minutes

Calculating the forward SPX Index level

Finding the required forward strike prices

Determining strike price boundaries

Tabulating contributions by strike prices

Calculating the volatilities

Calculating the next-term options

Calculating the VIX Index

Calculating multiple VIX indexes

Comparing the results

Summary

Building an Algorithmic Trading Platform

Introducing algorithmic trading

Trading platforms with a public API

Choosing a programming language

System functionalities

Building an algorithmic trading platform

Designing a broker interface

Python library requirements

Installing v20

Writing an event-driven broker class

Storing the price event handler

Storing the order event handler

Storing the position event handler

Declaring an abstract method for getting prices

Declaring an abstract method for streaming prices

Declaring an abstract method for sending orders

Implementing the broker class

Initializing the broker class

Implementing the method for getting prices

Implementing the method for streaming prices

Implementing the method for sending market orders

Implementing the method for fetching positions

Getting the prices

Sending a simple market order

Getting position updates

Building a mean-reverting algorithmic trading system

Designing the mean-reversion algorithm

Implementing the mean-reversion trader class

Adding event listeners

Writing the mean-reversion signal generators

Running our trading system

Building a trend-following trading platform

Designing the trend-following algorithm

Writing the trend-following trader class

Writing the trend-following signal generators

Running the trend-following trading system

VaR for risk management

Summary

Implementing a Backtesting System

Introducing backtesting

Concerns in backtesting

Concept of an event-driven backtesting system

Designing and implementing a backtesting system

Writing a class to store tick data

Writing a class to store market data

Writing a class to generate sources of market data

Writing the order class

Writing a class to keep track of positions

Writing an abstract strategy class

Writing a mean-reverting strategy class

Binding our modules with a backtesting engine

Running our backtesting engine

Multiple runs of the backtest engine

Improving your backtesting system

Ten considerations for a backtesting model

Resources restricting your model

Criteria of evaluation of the model

Estimating the quality of backtest parameters

Be prepared to face model risk

Performance of a backtest with in–sample data

Addressing common pitfalls in backtesting

Have a common-sense idea of your model

Understanding the context for the model

Make sure you have the right data

Data mine your results

Discussion of algorithms in backtesting

K-means clustering

K-nearest neighbors machine learning algorithm

Classification and regression tree analysis

The 2k factorial design

The genetic algorithm

Summary

Machine Learning for Finance

Introduction to machine learning

Uses of machine learning in finance

Algorithmic trading

Portfolio management

Supervisory and regulatory functions

Insurance and loan underwriting

News sentiment analysis

Machine learning beyond finance

Supervised and unsupervised learning

Supervised learning

Unsupervised learning

Classification and regression in supervised machine learning

Overfitting and underfitting models

Feature engineering

Scikit-learn for machine learning

Predicting prices with a single-asset regression model

Linear regression by OLS

Preparing the independent and target variables

Writing the linear regression model

Risk metrics for measuring prediction performance

Mean absolute error as a risk metric

Mean squared error as a risk metric

Explained variance score as a risk metric

R2 as a risk metric

Ridge regression

Other regression models

Lasso regression

Elastic net

Conclusion

Predicting returns with a cross-asset momentum model

Preparing the independent variables

Preparing the target variables

A multi-asset linear regression model

An ensemble of decision trees

Bagging regressor

Gradient tree boosting regression model

Random forests regression

More ensemble models

Predicting trends with classification-based machine learning

Preparing the target variables

Preparing the dataset of multiple assets as input variables

Logistic regression

Risk metrics for measuring classification-based predictions

Confusion matrix

Accuracy score

Precision score

Recall score

F1 score

Support vector classifier

Other types of classifiers

Stochastic gradient descent

Linear discriminant analysis

Quadratic discriminant analysis

KNN classifier

Conclusion on the use of machine learning algorithms

Summary

Deep Learning for Finance

A brief introduction to deep learning

What is deep learning ?

The artificial neuron

Activation function

Loss functions

Optimizers

Network architecture

TensorFlow and other deep learning frameworks

What is a tensor ?

A deep learning price prediction model with TensorFlow

Feature engineering our model

Requirements

Intrinio as our data provider

Compatible Python environment for TensorFlow

The requests library

The TensorFlow library

Downloading the dataset

Scaling and splitting the data

Building an artificial neural network with TensorFlow

Phase 1 – assembling the graph

Phase 2 – training our model

Plotting predicted and actual values

Credit card payment default prediction with Keras

Introduction to Keras

Installing Keras

Obtaining the dataset

Splitting and scaling the data

Designing a deep neural network with five hidden layers using Keras

Measuring the performance of our model

Running risk metrics

Displaying recorded events in Keras history

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

Preface

This second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the finance industry, using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn how to manage risks using advanced examples. 

You will start by setting up a Jupyter notebook to implement the tasks throughout the book. You will learn how to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, NumPy, SciPy, scikit-learn, and so on. 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 how to apply statistical analysis on time series data and understand how to harness high-frequency data to devise trading strategies in building an algorithmic trading platform. You will learn to validate your trading strategies by implementing an event-driven backtesting system and measure its performance. Finally, you will explore machine learning and deep learning techniques that are applied in finance.

By the end of this book, you will have learned how to apply Python to different paradigms in the financial industry and perform efficient data analysis.

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, 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 using smart machine learning techniques. 

What this book covers

Chapter 1, Overview of Financial Analysis with Python, goes briefly through setting up a Python environment, including a Jupyter Notebook, so that you can proceed with the rest of the chapters in this book. Within Jupyter, we will perform some time series analysis with pandas, using plots for analysis.

Chapter 2, The Importance of Linearity in Finance, uses Python to solve systems of linear equations, perform integer programming, and apply matrix algebra to the linear optimization of portfolio allocation.

Chapter 3,  Nonlinearity in Finance, explores some methods that will help us extract information from nonlinear models. You will learn root-finding methods in nonlinear volatility modeling. The optimize module of SciPy contains the root and fsolve functions, which can also help us to perform root finding on non-linear models.

Chapter 4, Numerical Methods for Pricing Options, explores trees, lattices, and finite differencing schemes for the valuation of options.

Chapter 5, Modeling Interest Rates and Derivatives, discusses the bootstrapping process of the yield curve and covers some short-rate models for pricing interest rate derivatives with Python.

Chapter 6, Statistical Analysis of Time Series Data, introduces principal component analysis for identifying principal components. The Dicker-Fuller test is used for testing whether a time series is stationary.

Chapter 7, Interactive Financial Analytics with VIX, discusses volatility indexes. We will perform analytics on a US stock index and VIX data, and replicate the main index using the options prices of the sub-indexes.

Chapter 8, Building an Algorithmic Trading Platform, takes a step-by-step approach to developing a mean-reverting and trend-following live trading infrastructure using a broker API.

Chapter 9, Implementing a Backtesting System, discusses how to design and implement an event-driven backtesting system and helps you to visualize the performance of our simulated trading strategy.

Chapter 10, Machine Learning for Finance, introduces us to machine learning, allowing us to study its concepts and applications in finance. We will also look at some practical examples for applying machine learning to assist in trading decisions.

Chapter 11, Deep Learning for Finance, encourages us to take a hands-on approach to learning TensorFlow and Keras by building deep learning prediction models using neural networks.

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:

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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

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789346466_ColorImages.pdf.

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.

Get in touch

Feedback from our readers is always welcome.

General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email us at [email protected].

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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

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 useful insights.

We will explore time series data manipulation using the pandas module. The two primary data structures in pandas are the Series object and the DataFrame object. Together, they can be used to plot charts and visualize complex information. Common methods of financial time series computation and analysis will be covered in this chapter.

The intention of this chapter is to serve as a foundation for setting up your working environment with libraries that will be used throughout this book. Over the years, like any software packages, the pandas module has evolved drastically with many breaking changes. Codes written years ago interfacing with older version of pandas will no longer work as many methods have been deprecated. The version of pandas used in this book is 0.23. Code written in this book conforms to this version of pandas.

In this chapter, we will cover the following:

Setting up Python, Jupyter, Quandl, and other libraries for your environment

Downloading datasets from Quandl and plotting your first chart

Plotting last prices, volumes, and candlestick charts

Calculating and plotting daily percentage and cumulative returns

Plotting volatility, histograms, and Q-Q plots

Visualizing correlations and generating the correlation matrix

Visualizing simple moving averages and exponential moving averages

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 users, however, may prefer to control which libraries get installed with their base Python interpreter.

Preparing a virtual environment

At this point, it is advisable to set up a Python virtual environment. Virtual environments allow you to manage separate package installations that you need for a particular project, isolating the packages installed in other environments.

To install the virtual environment package in your terminal window, type the following:

$ pip install virtualenv

On some systems, Python 3 may use a different pip executable and may need to be installed via an alternate pip command; for example: $ pip3 install virtualenv.

To create a virtual environment, go to your project's directory and run virtualenv. For example, if the name of your project folder is my_project_folder, type the following:

$ cd my_project_folder

$ virtualenv my_venv

virtualenv my_venv will create a folder in the current working directory that includes Python executable files of your base Python interpreter installed earlier, and a copy of the pip library, which you can use to install other packages.

Before using the new virtual environment, it needs to be activated. In a macOS X or Linux terminal, type the following command:

$ source my_venv/bin/activate

On Windows, the activation command is as follows:

$ my_project_folder\my_venv\Scripts\activate

The name of the current virtual environment will now appear on the left of the prompt (for example, (my_venv) current_folder$) to let you know that the selected Python environment is activated. Package installations from the same terminal window will be placed in the my_venv folder, isolated from the global Python interpreter.

Virtual environments can help prevent conflicts should you have multiple applications using the same module but from different versions. This step (creating a virtual environment) is entirely optional as you can still use your default base interpreter to install packages.

Running Jupyter Notebook

Jupyter Notebook is a browser-based interactive computational environment for creating, executing, and visualizing interactive data across various programming languages. It was formerly known as IPython Notebook. IPython continues to exist as a Python shell and a kernel for Jupyter. Jupyter is an open-source software, free for all to use and learn about a variety of topics, from basic programming to advanced statistics or quantum mechanics.

To install Jupyter, type the following command in your terminal window:

$ pip install jupyter

Once installed, start Jupyter with the following command:

$ jupyter notebook

...

Copy/paste this URL into your browser when you connect for the first time, to login with a token:       

http://127.0.0.1:8888/?token=27a16ee4d6042a53f6e31161449efcf7e71418f23e17549d

Watch your terminal window. When Jupyter has started, the console will provide information about this running status. You should also see a URL. Copy that URL into a web browser to bring you to the Jupyter computing interface.

Since Jupyter starts in the directory where you have issued the preceding command, Jupyter will list all saved notebooks in the working directory. If this is the first time you are working in the directory, the list will be empty.

To start your first notebook, select New, then Python 3. A new Jupyter Notebook will open in a new window. Henceforth, most computations in this book will be performed in Jupyter.

The Python Enhancement Proposal

Any design considerations in the Python programming language are documented as a Python Enhancement Proposal (PEP). Hundreds of PEPs have been written down, but probably the one that you should be familiar with is PEP 8, a style guide for Python developers to write better, readable code. The official repository for PEPs is https://github.com/python/peps.

What is a PEP?

PEPs are a numbered collection of design documents describing a feature, process, or environment related to Python. Each PEP is carefully maintained in a text file, containing technical specifications of a particular feature and its rationale for its existence. For example, PEP 0 serves as the index of all PEPs, while PEP 1 provides the purpose and guidelines of PEPs. As software developers, we often read code more than we write code. To create clear, concise, and readable code, we should always use a style guide as a coding convention. PEP 8 is a set of style guidelines for writing presentable Python code. You can read more about PEP 8 at https://www.python.org/dev/peps/pep-0008/.

The Zen of Python

PEP 20 embodies the Zen